Network doi: 10.3390/network6030066

Authors: Muhammad Farooq Gioacchino Manfredi Luca De Cicco Saverio Mascolo

The growing demand for virtual reality and immersive applications has increased interest in 360° video streaming. When viewing omnidirectional content through a head-mounted display, users observe only a limited portion of the content, i.e., the viewport, at any given time. Consequently, transmitting the complete panoramic frame at uniformly high quality is bandwidth-inefficient. This review presents a system-level analysis of viewport-adaptive three-degree-of-freedom (3DoF) 360° video streaming, focusing on the coupled roles of viewport prediction, tile-based multi-rate encoding and bitrate allocation, transport mechanisms, and edge-assisted processing. The reviewed literature is examined to identify the design dependencies and trade-offs among these components. Viewport-adaptive approaches seek to reduce the bandwidth allocated to regions outside the instantaneous viewport while preserving the quality of the visible region. The analysis shows that their effectiveness cannot be attributed to prediction accuracy alone: the resulting Quality of Experience (QoE) depends jointly on tile granularity, bitrate allocation, buffer occupancy, transport delay, and whether prioritized tiles arrive before their playback deadlines. Finer tiling can improve spatial selectivity but increases coding, signaling, and request overhead. Moreover, HTTP/2, HTTP/3/QUIC, RTP/RTSP, and WebRTC present different reliability, latency, congestion-control, and scalability trade-offs across buffered video-on-demand, low-latency live streaming, and interactive immersive applications. Based on this synthesis, the review formulates a unified closed-loop cross-layer framework that coordinates prediction, tiling, bitrate allocation, request timing, transport configuration, buffering, and edge processing under bandwidth, latency, and resource constraints.

Network doi: 10.3390/network6030065

Authors: Md Imtiaz Ahmed Yaser Al Mtawa

Fast Reroute (FRR) after link failures is essential for carrier-grade Software-Defined Networking (SDN), yet hybrid deployments remain dominated by slow legacy routing convergence. This paper presents two port-state-driven FRR mechanisms for homogeneous and hybrid SDN networks. First, Port-State-Aware Fast Reroute (PSA-FRR) uses OpenFlow port-status events to trigger proactive, rule-based protection in the data plane. Second, Port-State-Aware Neural Fast Reroute (PSAR-FRR) formulates hybrid FRR as a controller-local multi-class classification problem and predicts the backup egress port from a port-centric state representation, enabling microsecond-scale decision latency. We evaluate the methods on the Abilene wide-area network (WAN) topology using Mininet with Open vSwitch (OVS) and a Ryu controller (homogeneous case) and Graphical Network Simulator-3 (GNS3) with Cisco IOS routers (hybrid baseline). In homogeneous SDN emulation, PSA-FRR restores connectivity within 30–100 ms under the evaluated configurations. In the hybrid baseline, conventional routing protocols converge in 13.8–256.1 s (Enhanced Interior Gateway Routing Protocol (EIGRP), Intermediate System to Intermediate System (IS-IS), Open Shortest Path First (OSPF), Border Gateway Protocol (BGP), and Routing Information Protocol (RIP)), confirming that control-plane recovery cannot meet a 50 ms target. Using the collected dataset, PSAR-FRR reduces controller decision time from 6.753 μs (PSA-FRR rule evaluation) to 0.214 μs (deep neural network (DNN) inference), a 31.5× speedup. These results show that port-state awareness combined with learned, controller-local policies can substantially reduce the decision-to-action latency of FRR, providing a practical path toward low-latency failure recovery in SDN migration scenarios.

Network doi: 10.3390/network6030064

Authors: Charbel El Gemayel Joseph El Gemayel Joseph Constantin

Civilian communication systems often fail during armed conflicts, political unrest, and large-scale Internet disruptions—precisely when reliable communication is most needed. This paper presents HERMES, a resilient hybrid communication architecture that integrates HTTP/IP networking, Bluetooth Low Energy (BLE) mesh communication, and Delay-Tolerant Networking (DTN) within a unified adaptive routing framework. Unlike conventional approaches that treat alternative transports as backup solutions, HERMES dynamically selects the most efficient transport path based on current network conditions using a transport-aware forwarding policy whose cost function combines round-trip time, transport preference, and observed link risk. The architecture is built on distributed microservices that support topology discovery, shortest-path routing, and fault-tolerant message delivery. Reliability is enhanced through acknowledgments, bounded retransmissions, duplicate suppression, and graceful degradation mechanisms, while end-to-end authenticated encryption (Noise XX with a Double Ratchet) ensures secure communication across transport changes. A prototype implementation developed in C# on .NET 9 was evaluated on a five-node testbed, and a custom Network Simulator 3 (NS-3) module was used to extend the evaluation to networks of up to 500 nodes, under multiple failure scenarios, including node crashes, network partitioning, and complete Internet outages. Experimental results show that HERMES maintains perfect or near-perfect delivery in static topologies, including during a complete Internet blackout that disables IP-only messaging. Compared with the published Delay-Tolerant Networking protocols Epidemic and PRoPHET at one hundred nodes, HERMES exceeds their delivery ratio in static and failure scenarios and remains within 0.06 of them under pedestrian mobility during blackout, while transmitting roughly 35× fewer bytes– and about 21× fewer even relative to the more bandwidth-efficient MaxProp baseline. Under coordinated drop attacks by adversarial relays, HERMES degrades gracefully where flooding-based baselines collapse. This approach demonstrates that resilient civilian communication can be effectively achieved through metric-driven adaptive multi-transport routing, making it suitable for disaster recovery, contested environments, and connectivity-limited regions.

Network doi: 10.3390/network6030063

Authors: Aymen Chentouf Zouhair Chiba Mounia Miyara

Multi-controller deployments in Software-Defined Networking require choosing both the number of controllers and their placement on the topology. Clustering-based methods, particularly k-means, are widely used for this problem, but the topology representation that the clustering operates on is rarely chosen explicitly. We treat the representation step as a design axis: the clustering algorithm is held fixed at k-means with k-means++ initialisation, and the input is varied across three classical, training-free embeddings of the propagation-delay distance matrix: metric multidimensional scaling (MDS), Isomap, and Laplacian Eigenmaps (Spectral). The evaluation covers thirteen Internet Topology Zoo backbones grouped into three scale tiers under an effective-N definition, with the controller count K varied from 2 to 10, and reports node-to-controller latency (N2C), controller-to-controller latency (C2C), and load imbalance jointly rather than singly, with paired significance tests over ten distinct seeds. The representation choice is consequential: 60% of pairwise embedding comparisons are statistically separated (Holm-corrected Wilcoxon, α=0.05), and the median best-versus-worst gap per configuration is 23–25% on the two latency metrics and 71% on load imbalance. Metric MDS achieves the lowest N2C in most regimes; Spectral achieves the lowest C2C on the medium and large tiers at mid-to-high K; Isomap trades single-metric wins for worst-case robustness and is non-dominated in 79% of the 63 large-tier configurations. Across all 117 configurations, each embedding is empirically non-dominated (within the embeddings compared and on this benchmark) in 67–85% of configurations, and 16% admit a single statistically separated best choice. Two baselines contextualise these results: k-means on the raw latency matrix leads in under 8% of configurations, and a Node2Vec baseline is competitive on C2C and load balance but trails the classical methods on N2C at one to two orders of magnitude higher embedding cost. Best-embedding identities transfer from k-means to a k-medoids variant in 70% of configurations. Capacity-, energy-, and reliability-aware extensions remain open.

Network doi: 10.3390/network6030062

Authors: Mohamad Khattar Awad Ghazal Alsholi Haniah Altabaa Dania Hani Abu Daqar Shahad Alshaher Hamed M. K. Alazemi

Software-defined Networking (SDN) is a promising networking architecture that separates the control and data planes to allow flexible network management. However, the SDN architecture makes networks vulnerable to various security threats, such as Distributed Denial-of-Service (DDoS) attacks. A DDoS attack is one of the most common SDN threats, aiming to exhaust a network’s computational and bandwidth resources. Self-similarity is a statistical property of time series in which data patterns repeat at different time scales. Several studies have shown that network traffic exhibits increased self-similarity during DDoS attacks, making it a promising tool for DDoS detection. Despite the effectiveness of statistical methods for detecting DDoS, some methods, such as self-similarity, are discarded due to their high computational cost, leading to detection delays. This paper proposes a lightweight Rescaled Range (R/S)-based scheme for effective real-time DDoS attack detection in SDN. The scheme employs the Welford online algorithm to compute statistical parameters of the R/S scheme. Experimental results demonstrate that the proposed scheme efficiently captures changes in self-similarity and detects TCP/UDP DDoS attacks in real time. Moreover, it achieves high detection performance compared to other R/S methods, with a False Positive Rate (FPR) below 0.5% and an average computation time of 0.047 ms.

Network doi: 10.3390/network6030061

Authors: Bin Bai Gang Xie Yuanan Liu

This paper develops a reliability-oriented resource-allocation framework for a single-cell uplink sparse code multiple access (SCMA) system with fixed low-projection codebook (LPCB) constellation components. Link-level SCMA–message-passing-algorithm (MPA) samples calibrate a compact bit-error-rate (BER) surrogate, enabling repeated candidate evaluation without embedding MPA decoding in the search loop. The proposed two-stage method combines a subcarrier allocation whale optimization algorithm (SAWOA), equipped with stochastic binary mapping and exact degree-feasibility repair, with an exact Karush–Kuhn–Tucker (KKT) active-set power allocator. For the J=6, K=4 setting, exact enumeration over all 210 main conditions shows that the SAWOA attains the enumerated equal-power P1 oracle objective within relative tolerance 10−10 in every case. Across 30 paired channel/optimizer blocks, each aggregating the seven power points, the SAWOA reduces the initial-gap-normalized convergence area under the curve by 47.5% relative to the Standard Binary WOA (Holm-adjusted p=1.19×10−5); its oracle-hit rate by iteration 20 is 93.3% versus 75.7%, while no significant AUC difference is detected relative to particle swarm optimization. After 100 iterations, the three population methods approach the same oracle plateau, whereas random is significantly worse at the midpoint (p=7.45×10−9). Exact KKT refinement improves every recorded SAWOA solution and reduces the equal-power surrogate by 3.64–56.27% on average across the sweep, with a maximum relative KKT residual of 1.01×10−16. A single-point direct Rayleigh SCMA–MPA check confirms the executable transfer of the selected allocation and returns the same decoded BER for the SAWOA and Standard Binary WOA; it remains a bounded transfer sanity check. The results demonstrate finite-budget stage-1 search efficiency, reliable feasible support recovery, and effective exact power refinement for the investigated quasi-static configuration.

Network doi: 10.3390/network6030060

Authors: Jose Neftali Limon-Ortiz Pedro David Arjona-Villicaña Alejandra Guadalupe Silva-Trujillo Francisco Javier Torres-Reyes Francisco Javier Ramirez-Aguilera

Data Centers that implement Software-Defined Networks (SDN) are not required to employ the Address Resolution Protocol (ARP), but network hosts do. Therefore, there is a need to support this protocol without modifying the intrinsic functionality of the SDN controller. In this work, four strategies for handling ARP are evaluated using an SDN and OpenFlow rules. The strategies include disabling ARP at the host level, using static MAC addresses, introducing a fake gateway, and generating ARP replies using OpenFlow flows. To our knowledge, nobody has tested and compared the main characteristics and advantages offered by these four strategies. Experimental evaluation was conducted on a real SDN network and complemented with similar experiments using Mininet. Performance was assessed using metrics such as ping response time, address resolution response time, jitter, and packet loss ratio. The results show that OpenFlow-based ARP replies provide a good balance in terms of scalability, performance, and configuration effort. This strategy achieved the lowest average ping response time (0.641 ms) and ARP response time (0.6188 ms), while avoiding the manual configuration requirements of static approaches.

Network doi: 10.3390/network6030059

Authors: Xaba Mondli Bakhe Nleya

The convergence of optical burst switching (OBS) and elastic optical networks (EON) offers a promising pathway for 6G IoT backhaul. However, existing solutions treat OBS and EON separately and rely on heuristic resource allocation that fails to meet stringent QoS demands. This paper proposes a fully AI-driven converged OBS/EON architecture integrating a hybrid switching fabric, a multi-agent deep reinforcement learning (DRL) orchestrator, and a federated learning (FL) plane for autonomous, QoS-aware resource provisioning. The control plane implements multi-agent Proximal Policy Optimization (PPO) for joint burst scheduling, routing, modulation selection, and spectrum allocation. The orchestration plane employs q-fair FL for privacy-preserving cross-domain traffic prediction. Mathematical formulations of the optimization problem with spectrum, GSNR, and delay constraints are provided, along with pseudo-algorithms. Simulations over a 14-node NSFNET topology demonstrate a 78% reduction in blocking probability, a 42% improvement in spectral efficiency, sub-millisecond URLLC delays, and a Jain’s fairness index of 0.92, while preserving data privacy. The framework builds upon SDN principles for seamless integration with optical transport infrastructures.

Network doi: 10.3390/network6030058

Authors: Ronild Hako Evjola Spaho Andres Annuk

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures.

Network doi: 10.3390/network6030057

Authors: Salvatore Dello Iacono Chiara Franzoni Paolo Bellagente Alessandra Flammini Emiliano Sisinni

Smart and sustainable mobility increasingly relies on distributed sensing, low-power communication technologies, and cloud-based ICT platforms. This article presents a comprehensive scientific analysis of the technological foundations of sensitized mobility, reviewing the state of the art in embedded sensing, distributed systems, and communication paradigms for future mobility challenges. The research project “MOST” and in particular its subgroup “Spoke 5” falls within this framework of sustainable and sensorized mobility, with numerous activities in data collection, analysis, and field experimentation. In order to allow data collection, retention and analysis, one of the challenges that we must address is the definition of an adequate ICT architecture. The core contribution of this work is the presentation of the MOST ICT architecture, designed as a containerized, scalable, and resilient infrastructure capable of integrating heterogeneous data coming from field-deployed systems. In addition, it discusses the primary research challenges encountered in the definition and development of the presented architecture by examining two representative case studies within the MOST-Spoke 5 research project: renewable energy charging stations for light electric vehicles and cyclists monitoring systems.

Network doi: 10.3390/network6030056

Authors: Ivica Lukić Nikola Ramčić Ivan Ivković Miljenko Švarcmajer

Blockchain technology has introduced a rapidly expanding range of consensus mechanisms, each designed to satisfy different operational requirements related to security, scalability, decentralization, transaction throughput, and energy efficiency. Selecting an appropriate consensus mechanism has consequently become a complex multi-criteria decision problem, particularly for developers and organizations without extensive expertise in distributed systems and blockchain architectures. Existing tools primarily address protocol benchmarking and static documentation, leaving the decision-support dimension largely unaddressed. This paper presents a web-based expert system designed to support the selection of blockchain consensus mechanisms according to specific user-defined operational requirements. The proposed solution was implemented using the MERN technology stack, consisting of MongoDB, Express.js, React, and Node.js, enabling a modular and scalable architecture suitable for future expansion and maintenance. The recommendation process is based on a two-phase filtering and scoring algorithm. In the first phase, mechanisms incompatible with mandatory user-defined constraints, including network type and key resource type, are systematically eliminated. In the second phase, the remaining mechanisms are ranked using attribute matching across criteria encompassing energy efficiency, scalability, security, decentralization, and transaction speed. The system returns the three most suitable consensus mechanisms for the given operational scenario together with their key characteristics. In addition to recommendation functionality, the application supports user authentication, recommendation history management, and administrative maintenance of the consensus mechanism database, which currently contains 38 distinct blockchain consensus protocols. Experimental evaluation through twelve representative usage scenarios demonstrated that the system consistently produces contextually relevant recommendations aligned with user-specified requirements. A comparative analysis with existing tools confirms that the proposed system occupies a distinct decision-support role currently absent from the available tooling landscape. A comparative analysis against four representative existing tools indicates that the proposed system combines a set of decision-support capabilities not jointly offered by any one of them. The presented approach contributes a transparent and extensible decision-support framework intended to simplify architectural planning and management of blockchain-based distributed systems.

Network doi: 10.3390/network6030055

Authors: Maksim Iavich Vladimer Svanadze

IPv4 fragmentation attacks, including tiny fragment injection, teardrop offset manipulation, and fragment flooding exploit the RFC 791 reassembly process to evade firewalls and network intrusion detection systems (NIDSs). Detection is challenging because widely used flow-level datasets (UNSW-NB15, CIC-IDS2017), lack the packet-level fragment header information required for direct RFC 791 validation. This study investigates whether fragmentation-related traffic can be identified using only flow-level statistical features. The proposed framework introduces four contributions: (1) a direction-corrected proxy labeling scheme, where flows are labeled as fragmented when CVL < 0.70, validated on a controlled 3000-flow Scapy dataset with F1 = 0.873 and ROC-AUC = 0.895 against verified packet-level ground truth; (2) a dual-model Random Forest architecture with feature separation to prevent circular self-prediction; (3) RFC 791-inspired statistical heuristics applied as a post-inference filter; and (4) a six-configuration ablation study with a reproducible protocol. The study distinguishes fragmentation-like statistical signatures from confirmed packet-level fragmentation. The benchmark model Mfrag achieves F1 ≈ 0.998 on synthetic data, while external PCAP validation yields F1 = 0.873. Unlike most flow-level NIDS research, the framework is validated against both controlled Scapy-generated traffic and the MAWI real-world backbone trace, establishing a practical performance bound (F1 = 0.82 for the full hybrid framework). The attack classification model Mattack achieves F1 = 0.974 on dataset-provided labels. Results show that flow-level analysis can provide useful indicators of fragmentation-related activity when packet-level evidence is unavailable, while highlighting the limitations of statistical detection.

Network doi: 10.3390/network6030054

Authors: Lucia Pintor Marco Garau Virginia Pilloni Luigi Atzori

As climate and societal challenges intensify, achieving sustainable development requires a holistic approach that balances economic growth with environmental preservation and social equity. The United Nations’ 2030 Agenda outlines 17 Sustainable Development Goals with corresponding global indicators. However, adapting these high-level goals to the specific context of telecommunications networks, which are more and more governed by AI-based components, presents major challenges. This paper proposes an extensive framework for assessing the adequacy of Key Value Indicators, used to evaluate the Key Values brought by network services and applications. The framework encompasses the phases of indicator elicitation, analysis, technical realization, and assessment. Applying this framework ensures that Key Value Indicators remain relevant, actionable, and pertinent to the objectives throughout the system’s evolution. Case studies from leading projects in the telecommunications sector illustrate how the proposed methodology effectively identifies deficiencies and helps refine the framework.

Network doi: 10.3390/network6030053

Authors: Avinash Karhana Ikjot Saini Arunita Jaekel

The recent advances in Vehicular Ad Hoc Networks (VANETs) can have a tremendous positive impact on vehicle safety and traffic flow. In VANETs, vehicles communicate wirelessly with each other and with roadside infrastructure nodes to improve awareness of neighboring vehicles and traffic conditions. However, such communication also increases the potential for various safety and security challenges, such as the threat of false reporting attacks. In these attacks, malicious or compromised nodes inject alert notifications that report fictitious traffic incidents that may trigger unnecessary evasive actions and increase the risk of collisions. This research addresses false alert attacks in VANETs by developing a machine learning-based detection framework that leverages innovative feature engineering and model assessment strategies. The proposed framework designs new features that capture vehicle kinematics to improve the detection of malicious alerts. The performance of multiple ML models is then analyzed in terms of both detection effectiveness and computational requirements to determine their suitability for different deployment scenarios. Our simulation results demonstrate that the proposed framework can achieve significant improvements compared to existing techniques for false alert detection. In addition, the study highlights the critical role of feature engineering in improving detection performance.

Network doi: 10.3390/network6030052

Authors: Chakadkit Thaenchaikun Komsan Kanjanasit

Modern communication networks require routing protocols that can adapt to dynamic traffic conditions while accounting for topology-based structural risk. Conventional open shortest path first (OSPF) relies on static or linear link cost metrics, which are often inadequate for capturing the nonlinear behavior of network dynamics and structural risk. This paper proposes sigmoid-enhanced OSPF (SE-OSPF), which integrates topology-based structural risk into the OSPF routing metric through a nonlinear sigmoid function. The proposed framework employs two configurable sigmoid parameters, the midpoint (x0) and the steepness (k), to provide smooth cost transitions and adaptive routing decisions under varying network conditions. Simulation results on a Barabási–Albert scale-free topology demonstrate that SE-OSPF reduces the average end-to-end delay by 19.7% and packet jitter by 8.6% compared with Standard OSPF. In addition, SE-OSPF increases the average number of successfully delivered packets by up to 16.6% compared with Linear-OSPF while reducing maximum link utilization (MLU), indicating more balanced traffic distribution, improved load balancing, and reduced congestion. These results demonstrate that the proposed sigmoid-based routing metric effectively balances routing efficiency, packet delivery reliability, and network load distribution, establishing SE-OSPF as an effective framework for topology-based structural risk-aware adaptive routing in modern communication networks.

Network doi: 10.3390/network6030051

Authors: Xinyi Zhuang Shiyang He Yuxin Zhang

The post-quantum signature scheme SQIsign achieves extremely compact public keys and signatures, making it attractive for bandwidth-constrained environments. However, its signing efficiency is limited by the high random failure rate of the ideal-to-isogeny mapping procedure and the substantial cost of each retry. Existing optimizations mainly reduce the number or cost of isogeny computations, while overlooking how to schedule commitment retries when multiple candidate ideals are available. We formulate commitment-stage scheduling as a lightweight learning-to-rank problem and provide an instrumented scheduling framework for SQIsign signing only. The pipeline uses two features, trains a weighted logistic regression scorer offline by maximum likelihood with class weighting, and deploys the same scorer online in Rank-ML mode. Live instrumentation on Apple M2 (n = 20,000 candidate attempts at NIST-I) quantifies the commitment bottleneck (86.4% failure; 7.36 mean attempts per session) and shows constant features at a fixed commitment degree (live AUC =0.50). Synthetic training supports the scorer when feature variance is present (test AUC ≈0.634). A remeasured four-way ablation with Batch-only control (n=100, seed 42) separates batch overhead from learned ordering: Rank-ML is indistinguishable from Batch-only at deployment, while Baseline remains fastest for its wall-clock signing time at batch size 10. These results clarify when lightweight ML scheduling applies in SQIsign and provide a reproducible evaluation template separating live, synthetic, remeasured, and proxy evidence.

Network doi: 10.3390/network6030050

Authors: Ahmed Lateef Salih Al-Karawi Rafet Akdeniz

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving edge intelligence because it enables geographically distributed devices to collaboratively train a shared model without transferring raw data to a central cloud. This capability is particularly valuable for 5G and emerging 6G networks, where edge-native services are required to satisfy stringent latency, bandwidth, and privacy constraints while operating on highly heterogeneous devices and time-varying wireless channels. In practice, however, synchronous FL is often constrained by straggling clients with limited computation capability or unfavorable communication conditions, which increases round latency and reduces overall resource efficiency. To address this challenge, this study develops a rigorously structured framework for dynamic client selection and radio resource allocation in heterogeneous wireless edge environments. Each FL round is formulated as a latency-aware scheduling problem that jointly captures local computation time, uplink transmission time, minimum participation constraints, and resource block assignment. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that integrates computation-aware, channel-aware, and fairness-aware scoring with greedy resource block allocation guided by marginal completion time reduction. The study further provides a clear methodological structure, workflow visualization, literature-grounded justification, dataset documentation, and uncertainty-aware result reporting. Under the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces the average round completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. Standard deviation reductions of 70.59% and 80.77% further indicate improved round-to-round stability and more reliable training behavior. These results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks.

Network doi: 10.3390/network6030049

Authors: Olumide Alamu Thomas O. Olwal Munguakonkwa Emmanuel Migabo

The internet of things (IoT) has rapidly evolved into a ubiquitous communication paradigm for enabling the deployment of autonomous wireless networks across diverse application domains. However, the limited energy storage capacity and computational resources of IoT devices (IoTDs) pose a serious concern to their long-term sustainability and the expected quality of service delivery. Moreover, in the foreseeable era of the internet of everything, centralised network resource management is likely to constrain network scalability. To tackle these challenges in the current and next-generation communication networks, the adoption of adaptive and lightweight computational frameworks coupled with energy-efficient transmission strategies is essential. To demonstrate this, we exploit the concept of cooperative communication and radio frequency-based energy-harvesting to improve the network throughput while maintaining power supply to the IoTDs. Furthermore, to intelligently and autonomously perform resource allocation, we employ the reinforcement learning frameworks, particularly state–action–reward–state–action (SARSA) and Q-learning. Based on key performance evaluation metrics, we compare our findings with the baseline methods, including the equal, random, and greedy power level selection schemes, with SARSA exhibiting the most favourable performance trade-offs.

Network doi: 10.3390/network6030048

Authors: Abdulaziz G. Alanazi Haifa A. Alanazi Nasser S. Albalawi

IoT networks now handle traffic from billions of devices, and edge nodes are under constant pressure to classify that traffic and dispatch tasks within tight latency deadlines. Most existing systems treat classification and scheduling as two separate steps that run one after the other. This sequence adds unnecessary delay and breaks the feedback between the two tasks: the scheduler never sees the traffic type, and the classifier never sees the queue state. We propose ParallelEdge-AI, a system built around a shared flow encoder that feeds two task-specific heads in parallel, one for multi-class traffic classification and one for task-urgency scoring. Both heads are trained end-to-end using a joint loss that combines cross-entropy and pairwise ranking. A load-balance controller then reads the urgency scores alongside live queue lengths to decide, every 200 ms, whether a task stays local or moves to a less-loaded edge node. No global synchronisation is needed. We test the system on three real IoT datasets: RT-IoT2022, N-BaIoT, and CICIoT2023. ParallelEdge-AI reaches 97.63% accuracy and an F1-score of 97.34%, which is 3.16 percentage points above the best baseline. Inference latency is 19.62 ms per batch, the deadline-miss rate is 2.34%, and the load-imbalance index is 0.083, all three are the best results in our comparison. These numbers show that running classification and scheduling together on a shared representation is both faster and more accurate than treating them as separate problems.

Network doi: 10.3390/network6030047

Authors: Övgüm Can Sezen Claus Pahl Florian Hofer

Long-Range Wide-Area Networks (LoRaWANs) combine long-range and low-power communication, making them a key technology for Internet of Things (IoT) applications. This systematic mapping study provides a comprehensive analysis of research on LoRaWAN network technology, focusing on performance and robustness optimization published between 2015 and 2026. Through a rigorous screening of2746 papers, we identified and analyzed 209 papers that met strict inclusion criteria and addressed network-layer optimization mechanisms. The studies were retrieved from IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus using a PICO-based search strategy, and synthesized descriptively without effect-size meta-analysis. Our analysis reveals a rapidly growing research field, with 53.1% of the 209 included studies were published in the recent period (2023–2026), predominantly simulation-based evaluation approaches (72.2%), and strong geographic concentration in Europe (38.8%) and Asia (35.4%). We identified that performance optimization is the primary focus (96.2% of papers), while robustness optimization remains significantly underfocused (27.3% of papers), representing a critical research gap. This study identifies and prioritizes five research gaps, including the need for real-world field studies, multi-objective optimization frameworks, and lightweight machine learning approaches for edge devices. This mapping study provides structured guidance for future research in LoRaWAN optimization and supports evidence-based decision-making in the field.

Network doi: 10.3390/network6030046

Authors: Fadwa Bezzazi Dounia Lotfi

Firewall rule ordering aims to improve packet filtering efficiency while preserving the dependency constraints that guarantee the intended security behavior of the policy. Existing approaches often rely either on local criteria, such as rule frequency, or on iterative optimization procedures whose behavior depends on initialization, parameter settings and search budget. In this paper, we propose PPCO, a deterministic dependency-aware rule ordering method based on propagated probability combined with descendant-based centrality. The proposed score reflects both the traffic relevance of a rule and its structural influence in the dependency graph. The structural component is essential, especially when some rules are inactive or have zero activation probability, since it prevents probability-based ties from violating dependency constraints. The final policy is obtained directly by sorting rules in a decreasing score order. Experiments were conducted on synthetic rule sets ranging from 50 to 2000 rules and on ClassBench-ng benchmark instances, showing that PPCO consistently achieves a competitive ordering quality among the compared deterministic methods under the considered experimental settings. The method remains stable as the policy size and dependency rate increase, produces zero dependency violations in all valid configurations, achieves the lowest score-coherence values, and maintains competitive execution times at large scales. These results suggest that PPCO provides an effective, robust, and computationally efficient solution for dependency-aware firewall rule ordering within the scope of the evaluated configurations.

Network doi: 10.3390/network6030045

Authors: Alimuddin Arriesgado Marc Caesar Talampas

Path loss modeling is essential for the design, analysis, and applications (e.g., localization) of low-power wide-area networks (LPWANs). Conventional models typically rely on coarse regional land cover classifications (e.g., urban or suburban), which fail to capture the direction-dependent path loss variations of long-range LPWAN links that traverse heterogeneous environments. Although per-link modeling and geographical clustering have individually shown promise in addressing these limitations, their combined potential remains unexplored. This paper presents GeoSeg, a path loss modeling approach that integrates per-link modeling with geographical clustering. GeoSeg represents the propagation environment between each transmitter-receiver pair as a variable-length sequence that encodes both land cover types and their spatial arrangement and employs a hidden Markov model (HMM)-based clustering method to group these sequences into subregions. A per-subregion path loss exponent is then estimated for each identified subregion, enabling spatially adaptive path loss estimation. Evaluated using an open-access LoRaWAN dataset, the preliminary results demonstrate median MAE reductions of up to 96% across the evaluated clusters compared with the standard log-distance path loss model. These results suggest that integrating per-link environmental characterization with geographical clustering can potentially improve path loss estimation accuracy in heterogeneous LPWAN deployments.

Network doi: 10.3390/network6030044

Authors: Anne-Gaëlle Calandre David Espes Johanne Vincent

The deployment of 5G technology represents a significant advancement in telecommunications, offering unprecedented speed, connectivity, and innovation opportunities. However, this progress comes at a significant cost for Public Land Mobile Network (PLMN) operators, who face challenges in meeting high Quality of Service (QoS) standards for optimal user experience while ensuring appropriate levels of security. This paper addresses the joint optimization of latency and resource consumption under security constraints within 5G networks, focusing on the Packet Data Unit (PDU) session path to ensure compliance with security and latency requirements. We propose an innovative approach in which access control rules are distributed across User Plane Functions (UPFs) in the network. The optimization problem has been formulated as a mixed integer linear programming (MILP) problem that aims to minimize round-trip latency and operational costs for PLMN operators. We evaluate the performance of our model using a discrete event network simulator (NS3). The simulation results demonstrate the effectiveness of our approach, particularly in scenarios with stringent latency requirements. Latency is reduced, and a lower session drop rate is maintained, especially in conditions of network congestion. These findings emphasize the importance of considering both QoS and security in the design of next-generation 5G networks.

Network doi: 10.3390/network6030043

Authors: Nikolaos Petroulakis Alexandros Kornilakis Panos Chatziadam Vasileios Theodorou Nicolas Louca Stefanos Fafalios Petros Zervoudakis Dimitrios Laskaratos Maria Eleftheria Vlontzou Eleni Zarogianni

Secure and trustworthy data exchange across distributed data sources remains a major challenge in the health domain, where strict legal, regulatory, and privacy requirements must be satisfied. Data space technologies provide a promising approach to enabling interoperable and sovereign data sharing among diverse stakeholders while preserving data ownership and regulatory compliance. The NextGEM Innovation and Knowledge Hub (NIKH) was developed as a collaborative ecosystem for FAIR data access and evidence-based health risk assessment. This paper describes the NIKH Data Space (NIKH-DS), the underlying network provisioning platform within NIKH that enables secure data exchange in a health data space environment. The work outlines the key requirements, intended uses, and core implemented functionalities necessary for enabling secure network-provisioned data sharing among distributed data locations. Based on these requirements, a prototype architectural framework is proposed that integrates secure networking and interoperable services. The implementation of the individual components is described, including the data space controller, access control mechanisms, and a user-oriented dashboard that enables data visualization and interaction with distributed data sources. The NIKH-DS platform is validated through a set of case studies that demonstrate the feasibility and effectiveness of the platform in supporting secure, interoperable, and Findable, Accessible, Interoperable and Reusable (FAIR)-compliant health data sharing and risk assessment for the investigation of potential health effects of radio-frequency electromagnetic fields (EMF).

Network doi: 10.3390/network6030042

Authors: Nadera Aljawabrah Nedal Y. Al-Tamimi Ayoub Alsarhan Mahmoud Aljamal Bashar S. Khassawneh Sami Aziz Alshammari Nayef H. Alshammari Khalid Hamad Alnafisah

Cyber-physical smart infrastructures integrate sensing devices, communication networks, control components, and service platforms, which makes them vulnerable to malicious activities that may evolve gradually through several attack stages. The objective of this study is to develop and evaluate a simulation-based cybersecurity framework capable of detecting a proposed novel multi-stage cyber attack and identifying its internal progression within a realistic smart infrastructure environment. To achieve this objective, a NetSim-based cyber-physical smart infrastructure was modeled to generate both normal operational traffic and staged malicious traffic. The generated traffic was captured, processed, labeled, and transformed into a stage-aware cybersecurity dataset. An artificial neural network (ANN) model was then trained and evaluated for two detection tasks: binary classification of normal versus attack traffic and multi-class classification of compromise, coordination, and execution attack stages. Twenty experimental configurations were designed to examine the model under progressively broader infrastructure contexts, including sensing, service, gateway, control, backbone, and full-span operational scenarios. The best binary testing performance was achieved in the eighteenth experimental configuration, representing a broad full-span infrastructure scenario, with 97.96% accuracy, 97.80% precision, 97.65% recall, 97.72% F1-score, and 1.06% false positive rate. For stage-aware multi-class detection, the ANN model achieved 96.97% accuracy, 96.36% macro-averaged precision, 96.20% macro-averaged recall, 96.28% macro-averaged F1-score, and 96.55% weighted F1-score. Macro-averaged metrics report the unweighted average performance across classes, while weighted F1-score accounts for class support. These results show that the proposed simulation-based framework can generate realistic attack-aware traffic data and support reliable ANN-based detection of both attack presence and attack-stage progression.

Network doi: 10.3390/network6020041

Authors: Jimmy Agung Gunawan Moses Laksono Singgih Raden Venantius Hari Ginardi

Traditional Network Intrusion Detection Systems (NIDSs) face persistent challenges in detecting zero-day attacks due to concept drift, high false-positive rates, and limited adaptability. This research introduces a Cognitive Network Intrusion Detection System (CNIDS) whose central novelty is that effective zero-day handling does not arise from any single mechanism but from the interaction between continual representation learning, persistent vector memory, and human-aligned feedback. By reframing zero-day resilience as a continuous learning process rather than a static detection task, CNIDS emphasizes adaptive operational behavior over raw automated accuracy. The proposed framework integrates Continual Pre-Training (CPT) to align representations with evolving traffic, Supervised Fine-Tuning (SFT) to preserve precision on known attacks, and a Human-in-the-Loop Reinforcement Signal (HRS) that converts low-confidence alerts into structured learning updates. These components are unified through a vector database that functions as long-term episodic memory, enabling similarity-based reasoning and cross-dataset generalization. Ablation results show that disabling any component degrades zero-day adaptation: removing CPT increases drift sensitivity, removing vector memory prevents knowledge retention, and removing human feedback collapses learning to static inference. Using a class-exclusion zero-day protocol on NSL-KDD, UNSW-NB15, and CICIDS2017, CNIDS raises zero-day detection from 0% to 18.2% while maintaining precision above 80% and stabilizing false positives.

Network doi: 10.3390/network6020040

Authors: Samuel John Ivey Saleem Noel Bhatti

We examine the growth of Internet Protocol version 6 (IPv6) routing state from 2010 to 2025. The global IPv4 address space has been exhausted, and the transition to IPv6 is ongoing. Using publicly accessible data from the RIPE Route Collectors (RRCs), we show that growth in the number of globally visible IPv6 routing prefixes follows different models over time, reflecting different growth patterns: exponential, power-law, and stretched-exponential. In addition to building models using publicly available RIPE data, we use this data source to demonstrate that our analysis holds across different Internet Exchange Points (IXPs) around the world and has predictive value. We provide in-depth analyses of IPv6 routing state growth, and we believe these are the first such analyses. Additionally, we highlight previous similar analyses of other aspects of network characteristics (such as topology and network traffic), and show that our analyses provide new insights. Specifically, we show the following: (1) previous models that have worked well for other network characteristics do not work well for routing state; (2) growth patterns for IPv6 routing state have changed significantly over time; (3) growth patterns cannot be described by a single model, and need to be analysed in a piecewise fashion; (4) fitting of previous data might not necessarily result in good predictive quality, and we identify the factors that may affect the predictive quality of a model and the predictive models that are suitable at the current time. Our analyses include metrics for assessing model fit. Overall, we observe a decrease in the rate of growth of IPv6 routing state, while the overall use of IPv6 continues to grow. We provide a critical evaluation of our approach, and also discuss possible factors affecting the growth of global IPv6 routing state.

Network doi: 10.3390/network6020039

Authors: Xiuyuan Chen Tomoaki Tsutsumi Rei Nakagawa Yong Jin Nariyoshi Yamai

This paper presents an organization-managed IMAP Agent framework for extending email functionality in environments that rely on outsourced mail services. In this study, outsourced mail services refer to externally operated mailbox providers offering sufficiently scalable email infrastructures and standard IMAP interfaces, such as Gmail, Microsoft 365, and other commercial mailbox providers. In the proposed framework, IMAP Agents are operated within an organization, while user authentication continues to rely on existing institutional infrastructures such as Identity Providers (IdP) or Integrated Authentication Infrastructure (IAI). The IMAP Agent operates as a post-authentication processing component using credentials issued by these infrastructures, without modifying or intervening in the outsourced mail service itself. The framework enables organization-managed mailbox-side email processing without requiring administrative control over the mail server or dependence on provider-specific APIs. As a proof of concept, representative email-processing functions are implemented, including detection of suspicious messages based on header-level authentication information and automatic insertion of thread-consistent warning messages without altering the original email content. To evaluate the feasibility of the proposed framework, a prototype system was implemented using multiple containerized IMAP Agent instances. The experimental results showed that warning messages were typically appended within approximately 300 ms after message detection. Multi-container evaluations ranging from 1 to 100 concurrent IMAP Agent instances demonstrated low CPU overhead and approximately linear memory growth under idle-monitoring conditions, indicating the operational feasibility of deploying multiple IMAP Agent instances on a single host. These results suggest that the proposed framework can provide provider-independent and organization-managed extension of email functionality in outsourced mail environments through standard IMAP operations.

Network doi: 10.3390/network6020038

Authors: Ihor Rusnak Michael Segal

Network function virtualization (NFV) enables cost reduction and optimized service deployment. By means of virtualization, network functions which used to be executed on specialized hardware are being replaced with software called Virtual Network Functions (VNFs) that can run on commodity hardware. These VNFs are applied to data flows passing through network nodes with VNFs hosted on them. To fully realize the benefits of NFV, each flow must be fully processed on VNF nodes. Given the budget constraints, only a finite number of nodes can be selected to host VNFs, and these nodes also have limited capacity to process the flows passing through them. In this paper, we consider the problem of VNF node placement and capacity allocation in a network graph G=(V,E), i.e., selecting the best subset of VNF nodes and optimally distributing their bandwidth to maximize the total volume of fully processed traffic flows F. We propose a simpler algorithm for solving this problem than the previously proposed version, representing it as an integer linear programming problem with an approximation ratio of 12(1−1/e), and time complexity O(|V|2.5·|F|2.5·L), where L is the number of bits of input data.

Network doi: 10.3390/network6020037

Authors: Ze-Yu Jin Hsin-Min Lin Li-Hsing Yen Chien-Chao Tseng

The 5G Wireless and Wireline Convergence (5G-WWC) standards introduce critical network functions—notably the Access Gateway Function (AGF) and the User Plane Function (UPF)—to enable unified wired and wireless access through a single 5G core. However, deploying and integrating these functions across heterogeneous programmable hardware platforms remains a significant open architectural challenge. This paper presents a configurable integration framework that orchestrates AGF and UPF workloads on heterogeneous programmable data planes, specifically NVIDIA BlueField-2 Data Processing Units (DPUs) and P4-based switches. Unlike traditional, hardware-specific implementations, the framework provides a unified control plane that dynamically manages AGF-only, UPF-only, or Combined AGF/UPF deployments. A hardware abstraction mechanism decouples the control logic from pipeline-specific details, enabling the same control plane to drive different underlying hardware without modification. A Generic Flow Rule interface standardises communication between the control plane and each user-plane backend, while a merged DPU pipeline for Combined AGF/UPF eliminates the redundant GTP-U encapsulation and decapsulation steps inherent in a naively cascaded design. Experiments on NVIDIA BlueField-2 DPUs achieve near-100 Gbps throughput across all three TR-470 scenarios (AGF-only, UPF-only, and Collocated AGF/UPF). The Combined AGF/UPF configuration exhibits lower end-to-end latency than the separated AGF + UPF configuration, confirming both the feasibility and the efficiency of the proposed framework for next-generation high-performance programmable networks.

Network doi: 10.3390/network6020036

Authors: Etienne Alain Feukeu

Vehicular Ad Hoc Networks (VANETs) face significant challenges in high-mobility environments, where dynamic channel conditions, particularly Doppler Shift (DS), degrade communication reliability and increase latency, thereby undermining safety-critical applications. To address these limitations, this paper proposes a neural network (NN)-based link adaptation strategy trained using the Scaled Conjugate Gradient (SCG) algorithm. SCG is selected as a second-order approximation optimizer that leverages curvature information to produce well-conditioned weight updates particularly suited to the small, physics-constrained training dataset. The SCG-optimized model dynamically adjusts transmission parameters to mitigate DS effects, improving real-time adaptability by explicitly incorporating Doppler Shift as a key input feature. Simulation results demonstrate that the proposed approach outperforms both the conventional Auto Rate Fallback (ARF) method and the SampleRate baseline. Specifically, the SCG-based strategy achieves an overall throughput improvement of +34.6% relative to ARF (1.77 Mbps vs. 1.32 Mbps) across all tested conditions, with condition-specific gains of +16.1% at 5 Hz Doppler (0.9 km/h), +21.7% at 750 Hz (137.3 km/h), and +35.2% at 1500 Hz (274.6 km/h), while consistently reducing transmission duration. A formal ablation study confirms that the Doppler Shift feature alone contributes +67% to +78% throughput gain at high mobility (DS > 900 Hz) compared to an SNR-only model. The main contributions of this work are threefold: (i) the explicit integration of Doppler Shift as a first-class input feature for link adaptation; (ii) the application of SCG optimization for fast, stable training of a lightweight feedforward neural network on a compact, physics-constrained dataset; and (iii) the formal ablation study that isolates and quantifies the Doppler feature’s contribution, establishing that the performance gain is attributable to feature engineering rather than the neural network architecture alone. This approach offers a scalable, real-time solution for Doppler-resilient VANET link adaptation.

Network doi: 10.3390/network6020035

Authors: Iacovos Ioannou Michael Georgiades Prabagarane Nagaradjane Ala Khalifeh Christophoros Christophorou Marios Raspopoulos Vasos Vassiliou

A coupled multi-stage learning framework is presented for joint bit error rate (BER) prediction and beam angle optimization in massive multiple-input multiple-output (MIMO) systems under a controlled simulation protocol. Unlike purely sequential benchmarking pipelines, the proposed method jointly coordinates BER prediction and beam-angle selection through a shared latent representation, an uncertainty-guided refinement mechanism, a cross-stage consistency loss and alternating optimization. Ten diverse approaches are systematically evaluated across two task-specific stages: Stage 1 examines six classical and adapted methods for BER prediction, including polynomial regression and deep unfolding networks; Stage 2 investigates four machine-learning and generative adversarial network (GAN)-based approaches for angle optimization, including conditional GANs and the proposed Direct-Angle neural network. Stage 3 couples the best-performing methods into a unified hybrid architecture through a shared encoder, explicit consistency regularization and alternating cross-stage updates, thereby producing an integrated beamforming decision strategy rather than an independent cascade. It is shown through the evaluation that the coupled hybrid framework achieves 96.0% overall angle-selection accuracy, a mean BER of 8.0×10−5 and 100% BER tolerance compliance within ±3 dB. In this framework, a differentiable BER surrogate initialized from a second-degree polynomial-regression teacher is coupled with the proposed Direct-Angle-NN for angle optimization. Relative to the strongest reimplemented literature baseline under the same controlled simulation assumptions, a 33.3% reduction in mean BER is achieved. Ablation experiments show that the coupling mechanism provides a modest but consistent improvement over the decoupled sequential baseline, increasing angle-selection accuracy from 93.5% to 96.0% and reducing mean BER from 1.05×10−4 to 8.0×10−5; the shared encoder accounts for the largest part of this gain while the consistency loss adds 0.6 percentage points. These results indicate that the shared encoder, consistency regularization and uncertainty-guided refinement improve the final beamforming decision, although the gain should be interpreted as incremental rather than as a large architectural breakthrough. A spectral efficiency of 38.0 bps/Hz and an energy efficiency of 0.466 Gbps/W are achieved with a power consumption of only 32.6 W. The theoretical discussion is presented as an analytical characterization of BER sensitivity, complemented by a computational-complexity assessment and empirical convergence diagnostics for the alternating optimization, rather than as a formal optimality proof. The effectiveness of the framework across multiple performance metrics is supported by Monte Carlo simulations, while the limitations of the current setup, including perfect CSI, uncoded QPSK, ideal hardware assumptions and a fixed beam codebook, are explicitly discussed. The complete simulation framework, including code and trained models, can be made available by the corresponding author upon reasonable request to facilitate reproducible research in massive MIMO optimization.

Network doi: 10.3390/network6020034

Authors: Sofia Polymeni Georgios Spanos Stefanos Georgiadis Anastasios Pechlivanidis Dimitris Tsiktsiris Evangelos Athanasakis Konstantinos Votis Dimitrios Tzovaras Georgios Kormentzas

Weather forecasting, given the ever-increasing occurrence of climate change-induced events, has been widely introduced as a method to offer accurate and timely forecasts for proactive measures and risk mitigation. Artificial intelligence of things (AIoT) offers promising solutions for short-term weather forecasting, contributing to the advancement of sustainable and efficient weather monitoring technologies. This work presents everWeather_2.0, a significantly enhanced low-cost and self-powered AIoT-based weather forecasting station, which addresses key challenges in power consumption, user engagement and forecasting accuracy. The proposed end-to-end Cloud-Edge-IoT (CEI) proof-of-concept solution improves upon its predecessor by combining a more robust renewable energy subsystem for complete power autonomy with a series of lightweight, adaptive statistical models for on-device forecasting and an integrated display for on-site user engagement. Deployed in a real-world scenario, the station demonstrated seamless operation and high short-term forecasting accuracy for the thermodynamic variables during the pilot deployment period, with model errors observed as low as 2% for 30 min forecasts to 4.3% for 120 min intervals, validating its applicability in real-time and continuous physical weather monitoring. While wind speed and rainfall were monitored, they were excluded from the current accuracy metrics due to their high volatility and the insufficient number of events recorded during the pilot period to ensure reliable modeling.

Network doi: 10.3390/network6020033

Authors: Massimiliano Gaffurini Dennis Brandão Emiliano Sisinni Paolo Ferrari

Over the last two decades, the automotive industry has spearheaded a shift toward data-centric manufacturing, where Real-Time Ethernet (RTE) networks defined in IEC61784-2 serve as critical components for ensuring deterministic communication at the Operation Technology level. Although AI-based systems offer significant potential for predictive maintenance and cybersecurity, their effectiveness is currently limited by a lack of structured datasets from real-world industrial environments. Most existing research relies on small-scale simulations or laboratory setups that fail to capture the scale and complexity of actual production. To address this gap, this paper introduces a novel methodology for repurposing network data collected throughout a plant’s lifecycle, specifically during the commissioning and validation phases of RTE networks according to IEC61918. An additional important contribution is the creation of the first multi-plant dataset for real RTE (PROFINET) traffic in the automotive sector, aggregating 300 GB of data from 54,000+ devices across nearly 700 production lines in 17 industrial sites. The work defines standardized methodologies and replicable processes for systematic data acquisition, validation, and labeling to ensure long-term usability for training AI models. Finally, four case studies (focused on performance, maintenance, security, and machine learning) show how this dataset can be used to enhance the reliability of modern smart manufacturing.

Network doi: 10.3390/network6020032

Authors: Jibrilla A. Tanimu Gueltoum Bendiab Aikaterini Kanta Stavros Shiaeles

The growing sophistication of cyber threats has reduced the effectiveness of traditional cybersecurity tools in protecting modern organisations and complex networks. This challenge requires advanced solutions capable of real-time detection, rapid response, and efficient threat mitigation. In this context, AI-based approaches have emerged as a powerful enabler of intelligent, adaptive, and data-driven security operations. This study presents a comprehensive analysis of AI-driven threat detection combined with automated incident response mechanisms in modern cybersecurity architectures. The novelty of this work lies in the integration of advanced machine learning-based detection with real-time, automated response capabilities to address zero-day and previously unknown threats in heterogeneous digital environments. The paper examines system architecture design, implementation strategies, and performance evaluation across diverse deployment scenarios. Experimental results demonstrate that AI-driven detection with automated response significantly enhances cybersecurity effectiveness, achieving accuracies between 96% and 97%, dramatically reducing the mean response time from 45 min to less than 30 s, and substantially improving zero-day threat detection and containment success rates. Overall, the proposed approach achieves up to a 98.9% improvement in incident containment efficiency, highlighting the operational and defensive advantages of intelligent automation.

Network doi: 10.3390/network6020031

Authors: Setayesh Ghadir Delaram Ghadir Tesfalem Mehari Berhe Davide Adami Stefano Giordano Michele Pagano Pietro Rossi Francesca Daniela Sotgiu Francesca Mossa Fiammetta Berlinguer

Continuous and objective monitoring of livestock behavior plays a key role in precision farming, animal welfare assessment, and reproductive management. This study proposes a non-invasive framework for sheep behavior and reproductive activity monitoring that integrates wearable actigraphy, machine learning, and a cloud-based data processing architecture. Tri-axial accelerometer data were collected at 30 Hz using collar-mounted ActiGraph sensors under real farming conditions. Raw acceleration signals were processed without temporal aggregation, preserving full temporal resolution that includes axis-specific acceleration, vector magnitude, and delta magnitude features. Several supervised learning models were evaluated for behavior classification, including BLSTM, LSTM, CNN–BLSTM, Random Forest, and Support Vector Machine, targeting behaviors such as standing, walking, grazing, lying, flehmen, and mating. The results indicate that both deep learning and classical machine learning approaches achieve high classification performance, with Random Forest obtaining an overall accuracy of 0.82, while deep sequential models effectively capture temporal patterns and behavioral transitions. Furthermore, a scalable cloud architecture is introduced to automate data ingestion, preprocessing, inference, storage in InfluxDB, and visualization through an interactive web application. The proposed framework supports continuous monitoring and offers practical tools for precision livestock management.

Network doi: 10.3390/network6020030

Authors: Yusor Rafid Bahar Al-Mayouf Omar Adil Mahdi Sameer Sami Hassan Namar A. Taha

Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring and exploration. However, harsh underwater conditions and frequent topology changes caused by node and sink mobility pose significant challenges for reliable routing. Conventional routing protocols that depend on global route reconstruction and static paths generate excessive control overhead and degrade performance in large-scale underwater environments. In this paper, we propose an energy-efficient virtual cell-based mobile-sink adaptive routing (VC-MAR) protocol for UWSNs. The sensing field is logically partitioned into a three-dimensional grid of virtual cells, where a cell-gateway is elected in each cell to construct a low-overhead routing backbone. To support sink mobility, VC-MAR introduces a localized route-adjustment mechanism that updates only the affected backbone segments rather than reconstructing the entire routing structure. By confining routing updates to neighboring cells influenced by sink movement, the proposed protocol significantly reduces control packet exchanges while ensuring stable and reliable data delivery. Simulation results show that the proposed VC-MAR improves the packet delivery ratio by up to 20% and reduces routing control overhead by about 34% compared with traditional grid-based routing methods. These results confirm the suitability of VC-MAR for dynamic and realistic underwater sensing scenarios.

Network doi: 10.3390/network6020029

Authors: Shafika S. Moni Dakshnamoorthy Manivannan

The dynamic nature of Vehicular Ad-hoc Networks (VANETs) necessitates robust authentication mechanisms to prevent adversaries from compromising vehicle privacy. To address privacy concerns, many existing approaches employ pseudonyms in place of real vehicle identities. However, the use of a single pseudonym is insufficient, as vehicle trajectories can still enable tracking. Consequently, vehicles must frequently change pseudonyms, typically selecting them from a pre-assigned pool, to ensure unlinkability and preserve privacy. In most existing schemes, a central authority issues certificates corresponding to each pseudonym, which vehicles present for authentication. While effective, this approach incurs significant computation, storage, and communication overhead, particularly in managing certificate revocation lists (CRLs), since each vehicle may possess a large number of pseudonyms. To address these challenges, we propose a Low-overhead and Efficient Privacy-preserving Authentication (LEPA) scheme for VANETs, leveraging Merkle Hash Trees (MHTs) and Cuckoo Filters (CFs) to efficiently manage pseudonym sets and revocation. We analyze the security of the proposed scheme against various attacks and demonstrate, through performance evaluation, that LEPA significantly reduces authentication and revocation overhead while maintaining strong privacy and security guarantees.

Network doi: 10.3390/network6020028

Authors: Nurzhigit Smailov Yerlan Tashtay Pawel Komada Yerzhan Nussupov Kanat Zhunussov Askhat Batyrgaliyev Daulet Naubetov Aziskhan Amir Beibarys Sekenov Darkhan Yerezhep

This paper provides a comprehensive overview of the progress in fiber-optic gyroscope technology, covering 260 key studies of the last ten years. A critical comparative analysis of fiber-optic gyroscope with alternative inertial sensors (Micro-Electro-Mechanical Systems, Hemispherical Resonator Gyroscope, Ring Laser Gyroscope) has been carried out. Confirming the unique advantages of fiber-optic gyroscope for autonomous navigation. Fundamental limitations of accuracy are considered in detail: temperature drifts, polarization noise, and Rayleigh backscattering. Modern hardware methods for suppressing these errors, including the use of photonic crystal and hollow fibers (Air-Core/Hollow-Core), are also considered in this work. The central place in the review is occupied by the analysis of the technological paradigm shift from bulky discrete circuits to hybrid integrated photonics (Indium Phosphide, Silicon Nitride, Lithium Niobate) and hybrid architectures to reduce weight and size characteristics. The role of artificial intelligence (Deep Learning, Long Short-Term Memory) methods in nonlinear drift compensation and calibration is discussed. The usage of the Brillouin effect and optomechanics promising areas are outlined, necessary to create a new generation of navigation systems operating in the absence of Global Navigation Satellite Systems signals.

Network doi: 10.3390/network6020027

Authors: Ming Che

A discrete Hartley transform (DHT)-based orthogonal frequency division multiplexing (OFDM) scheme is investigated for intensity modulation/direct detection (IM/DD) visible light communication (VLC) systems, where transmitted signals are required to be real-valued and non-negative. To address this constraint, a practical unipolar transmission framework with corresponding bipolar reconstruction is developed. By exploiting the real-valued and self-inverse properties of the DHT, the proposed scheme removes the need for Hermitian symmetry and enables full utilization of available subcarriers. Under equal-bandwidth conditions, this results in an approximately 50% reduction in computational complexity compared with conventional DCO-OFDM and ACO-OFDM schemes. Theoretical analysis and numerical results further show that the proposed approach achieves comparable bit error rate (BER) performance while exhibiting improved spectral confinement, as reflected by reduced out-of-band sidelobes under identical filtering conditions. In addition, it maintains spectral efficiency equivalent to DCO-OFDM under the same bandwidth constraint. These advantages are achieved at the cost of restricting subcarrier modulation to real-valued constellations, which may reduce flexibility in frequency-selective channels. Overall, these findings support DHT-OFDM as a low-complexity, spectrally confined multicarrier waveform for IM/DD VLC systems, particularly in scenarios where efficient spectrum utilization and reduced adjacent-channel interference are required.

Network doi: 10.3390/network6020026

Authors: Francesca Lodato Pierpaolo Salvo Marcello Folli Simona Valbonesi Andrea Garzia Giuseppe Ruello Riccardo Suman Massimo Perobelli Rita Massa Antonio Iodice

The use of millimeter-wave spectrum in fifth-generation (5G) systems is increasing the need for accurate prediction of received power and coverage in real deployment scenarios. In this context, ray tracing (RT) is a promising approach for site-specific analysis, although its reliability depends on how accurately different tools reproduce measurements in complex urban environments. This work presents a comparative assessment at 27 GHz of three RT tools: in-house Exact tool based on Vertical Plane Launching (VPL), Matlab 5G and open-source Sionna RT based on Shooting and Bouncing Rays (SBR). The comparison relies on a large outdoor walk-test campaign, including about 14,725 measurement points collected in a real urban area around a 27 GHz mMIMO base station, using real operator-provided antenna radiation patterns. Measured and simulated power levels are compared using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and a planning-oriented coverage-rate metric. The results show a reasonable agreement between simulations and measurements, with RMSE and MAE values around 10–12 dB, highlighting tool-specific behaviors related to boundary effects, interaction modeling, and high-power overestimation. This work confirms that RT is a flexible support for 5G preliminary network design, reducing the need for extensive drive tests.

Network doi: 10.3390/network6020025

Authors: Tahira Mahboob Mingwei Li Awais Aziz Shah Dimitrios Pezaros

Digital substations (DSs) play a critical role in modern Energy and Power Electrical Systems (EPESs), enabling intelligent control, monitoring, and automation. With increased reliance on communication and sensing technologies, DSs are vulnerable to cyberattacks such as False Data Injection (FDI). An adversary may falsify transformer temperature readings, misleading protection mechanisms and resulting in incorrect disconnection actions. These false disconnections may disrupt power delivery, cause economic losses, and reduce equipment lifespan. To address these challenges, we propose a reinforcement learning-based approach for cyber protection of smart grids against false temperature data injection attacks. Specifically, this work designs a Long Short-Term Memory Deep Deterministic Policy Gradient (LSTM-DDPG) deep reinforcement learning algorithm that learns to detect normal patterns and responds to suspicious thermal patterns by dynamically adjusting disconnection decisions. The agents process sequential state features to differentiate between legitimate overload conditions and sudden anomalies caused by FDI attacks. We implement the proposed approach on the IEEE 30-bus distribution network using the Pandapower simulator. The experimental results indicate that the LSTM-DDPG controller outperforms conventional DDPG and DQN baselines, achieving a recall of 0.897, F1 of 0.945, precision of 1.00 and accuracy of 0.981 with a confidence interval of 95%. In addition, grid stability reaches up to 0.9815, 1.0, 1.0, 0.9926 with respect to the voltage stability score, transformer stability value, disconnection stability, and stability index, respectively. The proposed method led to fewer false disconnections, providing improved robustness against sensor manipulations.

Network doi: 10.3390/network6020024

Authors: Jing Bai Xiaohan Ge Liangbin Yang Chunding Wang Ziyue Yin

The integration of service function chains (SFCs) and unmanned aerial vehicles (UAVs) lays a crucial technological foundation for achieving efficient, reliable, and adaptive future airborne service networks. Service functions (SFs) in the SFC will be deployed on UAVs; this type of SFC is called unmanned aerial vehicle-based service function chains (USFCs). However, due to the combined effects of open hardware and software architectures, exposed communication links, and complex mission environments, UAVs have become ideal targets for attackers. Once a vulnerability is successfully injected into a UAV, data from the SFs running on it will be stolen, seriously threatening the dependability of the USFC. Therefore, it is necessary to conduct a quantitative evaluation of the USFC dependability to provide insights for further improving its dependability. This paper develops a USFC dependability evaluation model based on a semi-Markov process (SMP) to capture the dynamic interaction between attacker behavior and USFC system recovery behavior. The dependability of the USFC is comprehensively evaluated from two perspectives: availability and security. Extensive numerical analysis experiments are conducted, and the results not only demonstrate the changing trends of various dependability metrics under different parameters but also show parameter combinations for synergistic optimization among metrics.

Network doi: 10.3390/network6020023

Authors: Joseph P. Mchina Neema Mduma Ramadhani S. Sinde

Network Intrusion Detection Systems (NIDS) face increasing challenges from sophisticated cyber threats, particularly zero-day attacks that evade signature-based methods. While supervised learning is effective for known attack classification, it struggles with novel threats, whereas anomaly-based approaches suffer from high false positive rates and unstable thresholds. To address these limitations, this paper proposes a decision-level adaptive intrusion-detection framework combining hierarchical CNN-based closed-set classification with autoencoder-based zero-day detection in a cascade architecture. The framework enables deployment-time adaptation by dynamically adjusting class-specific confidence thresholds and fusion parameters without model retraining. Experiments on the CSE-CIC-IDS2018 dataset demonstrate strong closed-set performance, achieving 98.98% accuracy and a macro-F1-score of 0.9342, with improved recall for minority attack classes under adaptive thresholding. Under a zero-day evaluation protocol in which Web_Attacks and Infiltration are excluded from training and validation, the proposed approach achieves an F1-score of 0.9319 while maintaining a low false positive rate of 0.0019. The framework is further evaluated on the Simulated University Network Environment (SUNE) dataset representing campus network traffic, achieving 96.18% closed-set accuracy and 97.54% accuracy in the integrated cascade setting. These results demonstrate that the proposed framework effectively balances minority attack detection, zero-day identification, and false-alarm control in dynamic and resource-constrained network environments.

Network doi: 10.3390/network6020022

Authors: Kehinde O. Babaagba Zhiyuan Tan

Antivirus (AV) solutions remain a core defence mechanism against malicious software. However, many of these engines struggle to detect metamorphic malware, which continually alters its internal form in unpredictable ways. To address this limitation, we present an adversarially oriented approach that automatically generates novel malicious variants of existing malware that evade detection by a substantial proportion of AV systems, thereby providing material for strengthening defensive techniques. In this work, an Evolutionary Algorithm (EA) is used to evolve undetectable variants, guided by three fitness criteria: the evasiveness of the produced samples, and their behavioural and structural similarity to the original malware. The proposed method is assessed across three malware families to evaluate the effectiveness of the EA-generated variants. Results indicate that the EA produces diverse mutant variants capable of evading up to 94% of AV detectors for a given malware family, significantly surpassing the evasion rate of the original malware. Furthermore, we evaluated whether the mutants produced by the EA could enhance the training of machine learning models. In this context, a pretrained Natural Language Processing (NLP) transformer was employed within a transfer learning framework to improve the classification of metamorphic malware. When the evolved variants were incorporated into the training data, the approach achieved classification accuracies of up to 93%. These results highlight the value of using diverse EA-generated samples to strengthen malware classifiers, thereby improving the robustness of security systems against evolving threats.

Network doi: 10.3390/network6020021

Authors: Atef Ibrahim Fayez Gebali

The rapid development of the Internet of Things (IoT) is transforming various economic and industrial sectors by embedding interconnected devices within their operational processes. However, security and privacy risks associated with these interconnected devices pose significant barriers to widespread adoption, particularly in light of potential quantum threats. To mitigate these challenges, it is imperative to employ post-quantum cryptographic schemes. However, essential constraints on IoT edge nodes complicate the effective implementation of such schemes. Among the most promising approaches in post-quantum cryptography are lattice-based schemes, which rely heavily on polynomial multiplication operations at their core. Improving the implementation of polynomial multiplication will significantly enhance the performance of these schemes. Therefore, this paper proposes an efficent low-complexity serial systolic array optimized for polynomial multiplication, particularly tailored for the Binary Ring Learning With Errors (BRLWE) scheme. Designed for cryptographic processors targeting capable IoT edge nodes, the proposed architecture demonstrates remarkable performance improvements, achieving a maximum operating frequency of 280 MHz for a field size of 256, while requiring only 8232 lookup tables (LUTs) and 2616 flip-flops (FFs). These results reflect a 16.8% reduction in LUT usage and a 19% reduction in FFs compared to the nearest competing designs, all while maintaining high throughput and low area utilization. This work significantly advances the establishment of secure and efficient infrastructure for IoT systems, bolstering their resilience against post-quantum attacks and supporting the growth of a robust digital economy. Furthermore, it aligns with sustainable development goals 8 and 9 by fostering trust and facilitating the adoption of cutting-edge IoT technologies, ultimately promoting more resilient and innovative economic activities.

Network doi: 10.3390/network6020020

Authors: Sherif M. Aboul Hala M. Abd El Kader Esraa M. Eid Shimaa S. Ali

Fifth-generation (5G) mobile networks must simultaneously satisfy stringent latency targets, high user density, and energy-aware operation across heterogeneous services. Cloud Radio Access Networks (C-RAN) provide architectural flexibility through centralized baseband processing, but they also introduce new control challenges related to fronthaul constraints, dynamic traffic variations, and joint radio–compute coordination with Mobile Edge Computing (MEC). This paper proposes a unified AI-driven optimization framework for adaptive 5G C-RAN management, where the controller dynamically tunes key system decisions—including functional split selection, TDD downlink ratio, user–RU association, fronthaul load management, and MEC offloading proportion. To enable fair benchmarking under identical simulation settings, a static baseline policy is compared against five adaptive control strategies: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Multi-Objective Reinforcement Learning (MORL), and a Deterministic Service-Level Agreement (SLA)-aware controller Penalty-Constrained Hierarchical Action Controller (PCHAC). Performance evaluation across techno-economic and service KPIs shows that intelligent control significantly improves operational profit, tail-latency behavior, and energy efficiency while enhancing SLA compliance compared with non-adaptive operation. The results highlight the practicality of multi-objective and constraint-aware learning for next-generation C-RAN orchestration under scaling traffic demand.

Network doi: 10.3390/network6020019

Authors: Debarun Das Taieb Znati

Dynamic Spectrum Access (DSA) has emerged as a viable solution to address spectrum scarcity in shared-spectrum networks. In response, the FCC established the Citizens Broadband Radio Service (CBRS) to manage and facilitate shared use of the federal and non-federal spectrum in a three-tiered access and authorization framework. However, due to the open nature of spectrum access and the usually limited coverage of the monitoring infrastructure, enforcing access rights in a shared-spectrum network becomes a daunting challenge. In this paper, we stipulate the use of crowdsourcing as a viable approach to engaging volunteers in spectrum monitoring in order to enforce spectrum access rights robustly and reliably. The success of this approach, however, hinges strongly on ensuring that spectrum access enforcement is carried out by reliable and trustworthy volunteers within the monitored area. To this end, a hybrid spectrum monitoring framework is proposed, which relies on opportunistically recruiting volunteers to augment the otherwise limited infrastructure of trusted devices. Although a volunteer’s participation has the potential to enhance monitoring significantly, their mobility may become problematic in ensuring reliable coverage of the monitored spectrum area. To ensure continued monitoring, inspite of volunteer mobility, deep learning-based models are used to predict the likelihood that a volunteer will be available within the monitoring area. Three models, namely LSTM, GRU, and Transformer, are explored to assess their feasibility and viability to predict a volunteer’s availability likelihood over an extended time interval, in a given spectrum monitoring area. Recurrent Neural Networks (RNNs) such as GRU and LSTM are effective for tasks involving sequential data, where both spatial and temporal patterns matter, which is the focus of volunteer availability prediction in spectrum monitoring. Transformers, on the other hand, excel at handling long range dependencies and contextual understanding. Furthermore, their parallel processing capabilities allows faster training and inference compared to RNN-based models like GRU and LSTM. A simulation-based study is developed to assess the performance of these models, and carry out a comparative analysis of their ability to predict volunteers’ availability to monitor the spectrum reliably. To this end, a real-world trace dataset of volunteers’ location, collected over five years, is used. The simulation results show that the three models achieve high prediction accuracy of volunteers’ availability, ranging from 0.82 to 0.92. The results also show that a GRU-based model outperforms LSTM and Transformer-based models, in terms of accuracy, Root Mean Square Error (RMSE), geodesic distance, and execution time.

Network doi: 10.3390/network6010018

Authors: Raja Waseem Anwar Mohammad Abrar Abdu Salam Faizan Ullah

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments.

Network doi: 10.3390/network6010017

Authors: Adriana Rangel Ribeiro Ana Clara Santos Andrade Gabriel Leal dos Santos Guilherme Dinarte Marcondes Lopes Edvard Martins de Oliveira Adler Diniz de Souza Jeremias Barbosa Machado

This paper presents a Systematic Literature Review (SLR) on green scheduling and task offloading strategies for energy optimization in edge computing environments. The evolution of low-latency, high-performance applications has driven the widespread adoption of distributed computing paradigms such as Edge Computing, Fog-Cloud architectures, and the Internet of Things (IoT). In this context, Mobile Edge Computing (MEC) is often combined with Unmanned Aerial Vehicles (UAVs) to extend computational capabilities to areas with limited infrastructure, bringing processing closer to the data source to reduce latency and improve scalability. Nevertheless, these systems encounter substantial energy-related challenges, particularly in battery-powered or resource-constrained environments. To address these concerns, green computing strategies—especially energy-efficient scheduling and task offloading—have emerged as promising approaches to optimize energy usage in edge environments. Green scheduling optimizes task allocation to minimize energy consumption, whereas offloading redistributes workloads from resource-constrained devices to edge or cloud servers. Increasingly, these techniques are enhanced through artificial intelligence (AI) and machine learning (ML), enabling adaptive and context-aware decision-making in dynamic environments. This paper conducts a systematic literature review (SLR) to synthesize the most widely adopted strategies for energy-efficient scheduling and task offloading in edge computing, highlighting their impact on sustainability and performance. The analysis provides a comprehensive view of the state of the art, examines how architectural contexts influence energy-aware decisions, and highlights the role of AI/ML in enabling intelligent and sustainable edge systems. The findings reveal current research gaps and outline future directions to advance the development of robust, scalable, and environmentally responsible computing infrastructures.

Network doi: 10.3390/network6010016

Authors: Giuseppina Maria Rizzi Vittorio Curri

The constant growth of IP data traffic, driven by sustained annual increases surpassing 26%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising cost-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation variations with frequency and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation, as well as the amount of ASE noise. In this work, we review a set of analytical approximations, based on phenomenological approaches, for frequency-dependent attenuation and Raman scattering gain, and analyze their impact on achieving an effective balance between computational efficiency and physical fidelity. Through extensive analyses performed with the open-source software GNPy (version 2.12, Telecom Infra Project) on an optical line system exploring multi-band scenarios spanning C+L+S, C+L+E, and U-to-E transmission, we demonstrate that the proposed approximations reproduce the reference SRS power evolution and NLI profiles with root mean square errors (RMSEs) consistently below 0.03 dB, and down to the 10−3–10−2 dB range for the most accurate configurations. Although the current implementation does not yet provide a direct reduction in computational time, the proposed framework lays the groundwork for future developments toward closed-form or semi-analytical solutions, enabling more efficient modeling and optimization of ultra-wideband optical transmission.

Network doi: 10.3390/network6010015

Authors: Askar Abdykadyrov Moldir Kuatova Nurzhigit Smailov Zhandos Dosbayev Sunggat Marxuly Maxat Mamadiyarov Ainur Kuttybayeva Nurlan Kystaubayev Amirkhan Bekmurza

This study investigates radio-frequency signal propagation in underground metro tunnels with a focus on distributed antenna system (DAS) deployment. Deterministic simulations were performed using Altair WinProp 2024.1 (ProMan) with a 3D ray-tracing engine (GO + UTD) at 2.4 GHz in a reinforced concrete tunnel model of 900 m length. Two antenna configurations (B3: 8 dBi directional; B8: 5 dBi wide-beam) were evaluated under identical geometric and material conditions. Results show that path loss varies from 42 to 65 dB over 850 m, with estimated attenuation exponents lower than free-space values due to quasi-waveguide effects. The B3 configuration provides higher near-field received power (up to −7.5 dBm) but exhibits stronger attenuation over long distances. In contrast, the B8 configuration ensures a more uniform spatial power distribution and a reduced path-loss growth rate beyond 500 m. The findings confirm that antenna radiation pattern significantly influences underground communication performance and demonstrate the engineering suitability of distributed antenna systems for stable metro tunnel coverage.

Network doi: 10.3390/network6010014

Authors: Aleksei Demin Viktoriia Vasileva Dmitrii Chaikovskii

In the realm of high-speed Ethernet networks, alien crosstalk (AXT) significantly undermines the integrity and efficiency of data transmission. While existing works mostly focus on modeling and physical-layer mitigation techniques such as PAM16/DSQ128 modulation and LDPC coding, there is a lack of experimental evidence on how severe AXT affects commodity 10GBASE-T equipment in realistic, densely cabled installations. In this study, we assemble and evaluate the experimental testbed that emulates a highly adverse AXT environment by tightly bundling up to seven 60 m twisted-pair Ethernet cables and using only off-the-shelf 10GBASE-T network cards. We quantitatively characterize how increasing cable density leads to automatic speed downgrades, connection failures, and non-linear saturation of the aggregate throughput, and relate these effects to the observed link quality on individual ports. Our results demonstrate that, even in the presence of standard crosstalk mitigation and error-correction mechanisms, severe AXT can force commodity 10GBASE-T links to fall back from 10 Gbit/s to 1 Gbit/s or below. Based on these findings, we derive practical guidelines for dense-cabling deployments and identify key requirements for experimental testbeds that can more reliably quantify AXT severity and its impact on commodity 10GBASE-T link stability (rate fallback and link loss) under realistic conditions.

Network doi: 10.3390/network6010013

Authors: Zeyad AlJundi Taqwa A. Alhaj Fatin A. Elhaj Inshirah Idris Tasneem Darwish

Digital twin systems are becoming an important tool in intelligent transportation management, as they provide simulation-based environments for monitoring, analyzing, and predicting traffic behavior. However, the predictive performance of traffic digital twins is often limited by the quality and temporal consistency of sensor-level data generated from microscopic simulations. Most current calibration methods focus mainly on matching macroscopic traffic indicators, such as vehicle count and speed, without explicitly addressing the requirements of multi-horizon forecasting. This creates a gap between achieving realistic simulations and building reliable predictive models. This research proposes a forecasting-aware digital traffic twin framework that integrates microscopic SUMO simulation, controlled sensor-level observation modeling through geometric misalignment and noise injection, behavioral calibration, and deep temporal forecasting within a unified end-to-end structure. Unlike traditional calibration approaches, the proposed Genetic Algorithm (GA) reformulates calibration as a multi-step predictive optimization task. Simulation parameters are optimized by minimizing forecasting error produced by a lightweight proxy sequence model embedded within the calibration loop. In this way, calibration moves beyond simple statistical matching and instead emphasizes temporal learnability and forecasting stability, enabling the digital twin to generate traffic patterns more suitable for long-term prediction. Based on the calibrated traffic time series, both convolutional and recurrent deep learning models are evaluated under single-step and multi-step forecasting scenarios. To further examine generalizability, external validation is performed using the real-world PEMS-BAY dataset. The experimental findings demonstrate that forecasting-aware calibration reduces macroscopic traffic signal errors by around 50% for vehicle count and around 40% for average speed, improves temporal stability, and significantly enhances forecasting accuracy across both short-term and long-term horizons.

Network doi: 10.3390/network6010012

Authors: Souhaima Stiri Maria Rita Palattella Juan David Niebles Castano Christos Politis

Limited terrestrial network coverage in rural and remote areas constitutes a significant barrier to the digital transformation of the agricultural sector. Smart and precision farming applications, ranging from conventional environmental monitoring systems to advanced Digital Twin solutions, rely on the reliable transmission of sensor data, images, and video streams from geographically isolated farms. Such data-intensive services cannot be effectively supported without a robust communication infrastructure. Non-Terrestrial Networks (NTNs), particularly satellite systems, offer both narrowband and broadband connectivity, enabling the transmission of low-rate sensor measurements, as well as high-throughput multimedia data from the field. This paper presents an experimental performance evaluation of two satellite backhauling solutions: a Geostationary Earth Orbit (GEO) system provided by SES and a Low Earth Orbit (LEO) system from Starlink. The networks were first deployed and tested in a laboratory environment and subsequently validated in an operational agricultural field setting. Their performance is benchmarked against a terrestrial cellular network to assess their suitability for supporting advanced agricultural applications. The performance assessment results indicate that both satellite backhauling solutions are reliable and capable of meeting the bandwidth and latency requirements of delay-tolerant agricultural applications. In addition to the technical evaluation, this work presents a cost–benefit analysis that further underscores the advantages of NTN-based solutions. Despite higher initial expenditures, they provide extended coverage in remote areas and enable cost sharing across multiple users, improving overall economic viability.

Network doi: 10.3390/network6010011

Authors: Zineddine Bettouche Khalid Ali Andreas Fischer Andreas Kassler

Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific models incur prohibitive training and maintenance costs, while global models often fail to capture heterogeneous spatial dynamics. Recent spatiotemporal architectures based on attention or graph neural networks improve accuracy but introduce high computational overhead, limiting their applicability in large-scale or real-time settings. We propose HiSTM (Hierarchical SpatioTemporal Mamba), a spatiotemporal forecasting architecture built on state-space modeling. HiSTM combines spatial convolutional encoding for local neighborhood interactions with Mamba-based temporal modeling to capture long-range dependencies, followed by attention-based temporal aggregation for prediction. The hierarchical design enables representation learning with linear computational complexity in sequence length and supports both grid-based and correlation-defined spatial structures. Cluster-aware extensions incorporate spatial regime information to handle heterogeneous traffic patterns. Experimental evaluation on large-scale real-world cellular datasets demonstrates that HiSTM achieves better accuracy, outperforming strong baselines. On the Milan dataset, HiSTM reduces MAE by 29.4% compared to STN, while achieving the lowest RMSE and highest R2 score among all evaluated models. In multi-step autoregressive forecasting, HiSTM maintains 36.8% lower MAE than STN and 11.3% lower than STTRE at the 6-step horizon, with a 58% slower error accumulation rate compared to STN. On the unseen Trentino dataset, HiSTM achieves 47.3% MAE reduction over STN and demonstrates better cross-dataset generalization. A single HiSTM model outperforms 10,000 independently trained cell-specific LSTMs, demonstrating the advantage of joint spatiotemporal learning. HiSTM maintains best-in-class performance with up to 30% missing data, outperforming all baselines under various missing data scenarios. The model achieves these results while being 45× smaller than PredRNNpp, 18× smaller than xLSTM, and maintaining competitive inference latency of 1.19 ms, showcasing its effectiveness for scalable 5/6G traffic prediction in resource-constrained environments.

Network doi: 10.3390/network6010010

Authors: Hassan Rizky Putra Sailellah Hilal Hudan Nuha Aji Gautama Putrada

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions (×10 and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, C=102) achieves R2=0.9999, MAPE=0.0018, MAE=966.04, and RMSE=1589.64 on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability.

Network doi: 10.3390/network6010009

Authors: Mehrnoush Vaseghipanah Sam Jabbehdari Hamidreza Navidi

Network operators increasingly rely on abstracted telemetry (e.g., flow records and time-aggregated statistics) to achieve scalable monitoring of high-speed networks, but this abstraction fundamentally constrains the forensic and security inferences that can be supported from network data. We present a design-time audit framework that evaluates which threat hypotheses become non-supportable as network evidence is transformed from packet-level traces to flow records and time-aggregated statistics. Our methodology examines three evidence layers (L0: packet headers, L1: IP Flow Information Export (IPFIX) flow records, L2: time-aggregated flows), computes a catalog of 13 network-forensic artifacts (e.g., destination fan-out, inter-arrival time burstiness, SYN-dominant connection patterns) at each layer, and maps artifact availability to tactic support using literature-grounded associations with MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK). Applied to backbone traffic from the MAWI Day-In-The-Life (DITL) archive, the audit reveals selectiveinference loss: Execution becomes non-supportable at L1 (due to loss of packet-level timing artifacts), while Lateral Movement and Persistence become non-supportable at L2 (due to loss of entity-linked structural artifacts). Inference coverage decreases from 9 to 7 out of 9 evaluated ATT&CK tactics, while coverage of defensive countermeasures (MITRE D3FEND) increases at L1 (7 → 8 technique categories) then decreases at L2 (8 → 7), reflecting a shift from behavioral monitoring to flow-based controls. The framework provides network architects with a practical tool for configuring telemetry systems (e.g., IPFIX exporters, P4 pipelines) to reason about and provision the minimum forensic coverage.

Network doi: 10.3390/network6010008

Authors: Masaharu Hirota

Devices equipped with the Global Positioning System (GPS) generate massive volumes of trajectory data on a daily basis, imposing substantial computational, network, and storage burdens. Online trajectory simplification reduces redundant points in a streaming manner while preserving essential spatial and temporal characteristics. A representative method in this line of research is Directed acyclic graph-based Online Trajectory Simplification (DOTS). However, DOTS does not preserve stay-related information and can incur high computational cost. To address these limitations, we propose Directed acyclic graph-based Online Trajectory Simplification with Stay Areas (DOTSSA), a fast online simplification method that integrates DOTS with an online stay area detection algorithm (SA). In DOTSSA, SA continuously monitors movement patterns to detect stay areas and segments the incoming trajectory accordingly, after which DOTS is applied to the extracted segments. This approach ensures the preservation of stay areas while reducing computational overhead through localized DAG construction. Experimental evaluations on a real-world dataset show that, compared with DOTS, DOTSSA can reduce compression time, while achieving comparable compression ratios and preserving key trajectory features.

Network doi: 10.3390/network6010007

Authors: Kaoru Takabatake Tomofumi Matsuzawa

In recent years, with the improvement of unmanned aerial vehicle (UAV) performance, various applications have been explored. In environments such as disaster areas, where existing infrastructure may be damaged, alternative uplink communication for transmitting observation data from UAVs to the ground station (GS) is critical. However, conventional mobile ad hoc network (MANET) routing protocols do not sufficiently account for GS-oriented traffic or the highly mobile UAV topology. This study proposed a flying ad hoc network (FANET) routing protocol that introduces a control option called GS flood, where the GS periodically disseminates routing information, enabling each UAV to efficiently acquire fresh source routes to the GS. Evaluation using NS-3 in a disaster scenario confirmed that the proposed method achieves a higher packet delivery ratio and practical latency compared to the representative MANET routing protocols, namely DSR, AODV, and OLSR, while operating with fewer control IP packets than existing methods. Furthermore, although the multihop throughput between UAVs and the GS in the proposed method plateaued at approximately 40% of the physical-layer maximum, it demonstrated performance exceeding realistic satellite uplink capacities ranging from several hundred kbps to several Mbps.

Network doi: 10.3390/network6010006

Authors: Abdul Manan Sheikh Md. Rafiqul Islam Mohamed Hadi Habaebi Suriza Ahmad Zabidi Athaur Rahman bin Najeeb Mazhar Baloch

The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device level due to limited computing resources, mobility, and the large diversity of devices, as well as at the network level, where the use of varied protocols by different vendors introduces further vulnerabilities. Physical Unclonable Functions (PUFs) provide a lightweight, hardware-based security primitive that exploits inherent device-specific variations to ensure uniqueness, unpredictability, and enhanced protection of data and user privacy. Additionally, modeling attacks against PUF architectures is challenging due to the random and unpredictable physical variations inherent in their design, making it nearly impossible for attackers to accurately replicate their unique responses. This study collected approximately 80,000 Challenge Response Pairs (CRPs) from a Ring Oscillator (RO) PUF design to evaluate its resilience against modeling attacks. The predictive performance of five machine learning algorithms, i.e., Support Vector Machines, Logistic Regression, Artificial Neural Networks with a Multilayer Perceptron, K-Nearest Neighbors, and Gradient Boosting, was analyzed, and the results showed an average accuracy of approximately 60%, demonstrating the strong resistance of the RO PUF to these attacks. The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. The p-values from the 15 statistical tests confirm that the CRP data exhibit true randomness, with most values exceeding the 0.01 threshold and supporting the null hypothesis of randomness.

Network doi: 10.3390/network6010005

Authors: Mohamad Nurkamal Fauzan Rendy Munadi Sony Sumaryo Hilal Hudan Nuha

Efficient clustering is essential for reducing energy consumption and extending the operational lifetime of Wireless Sensor Networks. Classical protocols such as LEACH, PEGASIS, HEED, and EEHC frequently exhibit unbalanced energy usage, resulting in early node failures and reduced communication reliability. This study introduces an Enhanced Grey Wolf Optimization-based Neural Network (EGWO-NN) designed to adaptively select cluster heads by continuously optimizing decision parameters according to real-time network conditions. The proposed method is evaluated against four benchmark protocols using statistical comparisons of node survivability, transmission energy, and communication performance. Results show that EGWO-NN sustains significantly more alive nodes per round, with strong statistical differences compared with LEACH, PEGASIS, HEED, and EEHC (t = 18.27, 9.94, 18.91, 18.93; p < 10−22). Transmission energy analysis similarly indicates significant improvements across all pairwise tests (|t| = 4.12–46.34; p < 10−4), supported by an overall ANOVA result (F = 14.74, p = 1.42×10−10). EGWO-NN also enhances data delivery, outperforming baseline protocols in both packets sent and Packet Delivery Ratio, with highly significant differences (t = 17.62–19.75 and 11.25–22.89). These findings demonstrate that EGWO-NN provides a robust and scalable approach for improving energy efficiency and communication reliability in WSNs.

Network doi: 10.3390/network6010004

Authors: Bzav Shorsh Sabir Aree Ali Mohammed

One of the main sources of entertainment is live video streaming platforms, which allow viewers to watch video streams in real time. However, because of the increasing demand for high quality content, the vulnerability of streaming systems against cyberattacks highlights how crucial it is to implement strong security mechanisms without sacrificing performance. Therefore, the safeguard of video streams against cyberthreats such as content tampering and interception is a top priority while still maintaining robustness against network fluctuations. Two distinct scenarios are proposed to test AES-128 encryption in securing HTTP live streaming segments against content tampering and resilience to packet loss. Results show that AES-128 encryption provides confidentiality and successfully prevents meaningful manipulation of the video content, confirming its reliability as segment encryption does not significantly alter packet loss-induced playback behavior compared to unencrypted streaming under the tested conditions, Performance analysis shows that AES-128 has no significant difference in data loss for up to 4% of network packet loss compared to unencrypted segments.

Network doi: 10.3390/network6010003

Authors: Dionysia Varvarigou Kostas Lampropoulos Spyros Denazis Paris Kitsos

The expansion of 5G networks has led to larger attack surfaces due to more applications and use cases, more IoT connections, and the distributed 5G system architecture. Existing security frameworks often lack the ability to perform real-time, context-aware risk assessments that are specifically adapted to dynamic 5G environments. In this paper, we present an integrated framework that combines Snort intrusion detection with a risk and impact assessment model to evaluate threats in real time. By correlating intrusion alerts with contextual risk metrics tied to 5G core functions, the framework prioritizes incidents and supports timely mitigation. Evaluation in a controlled testbed shows the framework’s stability, scalability, and effective risk classification, thereby strengthening cybersecurity for next-generation networks.

Network doi: 10.3390/network6010002

Authors: A. Rehman L. Galluccio

Underwater networks are crucial for monitoring the marine ecosystem, enabling data collection to support the preservation and protection of natural resources. Among the various technologies available, acoustic and optical communications stand out for their superior performance in underwater environments. Acoustic technologies are suitable for long-range communications, typically operating over hundreds of meters up to several kilometers, albeit with low data rates ranging from a few hundred bps to few tens of kbps. In contrast, optical technologies excel in providing high data rates, often between 1 and 10 Mbps, but only over short distances (e.g., 50 m) in controlled conditions. To leverage the strengths of these technologies, recent research has proposed multi-modal underwater systems; however, these solutions generally rely on single-level or at most dual-level architectures, limiting the benefits of a structured hierarchical approach. In this review paper, after discussing related work on multi-technology acoustic and optical networks, we highlight relevant design guidelines for multi-technology, multi-level underwater architectures, explicitly considering three layers: a deep acoustic layer, an intermediate optical layer, and an upper RF-enabled surface layer. For illustration, we also discuss a PoC of such a hierarchical architecture under development at the University of Catania, Italy, in the Area Marina Isole dei Ciclopi natural reserve. The PoC includes optical nodes capable of transmitting up to 10 Mbps over short ranges and acoustic nodes (both software defined and not) supporting rates of tens of kbps over hundreds of meters and being adaptive to network conditions, interconnected through hybrid multi-technology nodes deployed across the three network levels. By assigning specific technologies to appropriate layers, the architecture enhances scalability, robustness, and adaptability to dynamic underwater conditions. This design strategy not only improves data transmission efficiency but also ensures seamless operation across diverse marine scenarios, making it an effective solution for a wide range of underwater monitoring applications.

Network doi: 10.3390/network6010001

Authors: Jose Alvarez Matheus Santos David May Gerard Dooly

IoT applications are increasingly common, yet they often rely on expensive, externally managed authentication services. This paper introduces a novel, self-contained authentication method for IoT applications which leverages fog computing principles to lower operational costs and infrastructure complexity. The proposed system, fogauth, combines device serial numbers with cryptographically generated UUIDs to establish secure identification without third-party services. A static cloud-side architecture coupled with a lightweight, locally hosted API enables secure authentication through object-storage operations. Performance testing demonstrates comparable security performance to commercial cloud-based authentication while reducing long-term operational costs and maintaining latency at below 2 minutes in production conditions. fogauth provides a scalable and economically viable alternative for companies seeking to reduce cloud dependency and minimize long-term costs associated with IoT application security. To support reproducibility, a complete open-source implementation and validation dataset are provided, allowing independent replication and extension of the system.

Network doi: 10.3390/network5040054

Authors: Kritsanapong Somsuk Suchart Khummanee Panida Songram

Future programmable networks such as 5G/6G and large-scale IoT deployments demand dynamic and intelligent bandwidth control mechanisms to ensure stable Quality of Service (QoS) under highly variable traffic conditions. Conventional queue-based schedulers and emerging machine learning techniques still struggle with slow reaction to congestion, unstable fairness, and high computational costs. To address these challenges, this paper proposes a Dynamic Predictive Feedback (DPF) mechanism that integrates clustered-LSTM based short-term traffic prediction with meta-control driven adaptive bandwidth adjustment in a Software-Defined Networking (SDN) architecture. The prediction module proactively estimates future queue depth and arrival rates using in-band network telemetry (INT), while the feedback controller continuously adjusts scheduling weights based on congestion risk and fairness metrics. Extensive emulation experiments conducted under Static, Bursty IoT, Mixed, and Stress workloads show that DPF consistently outperforms state-of-the-art solutions, including A-WFQ and DRL-based schedulers, achieving up to 32% higher throughput, up to 40% lower latency, and 10–12% lower CPU and memory usage. Moreover, DPF demonstrates strong fairness (Jain’s Index ≥ 0.96), high adaptability, and minimal performance variance across scenarios. These results confirm that DPF is a scalable and resource-efficient solution capable of supporting the demands of future programmable, 5G/6G-ready network infrastructures.

Network doi: 10.3390/network5040053

Authors: Shan Zeng Tao Cui Yanming Wang Mengyao Qi Fazhi Qi

Among the 15 beamlines in the first phase of the High-Energy Photon Source (HEPS) in China, the maximum peak data generation volume can reach 1 PB per day, with the maximum peak data generation rate reaching 3.2 Tb/s. This poses significant challenges to the underlying network system. To meet the storage, computing, and analysis needs of HEPS scientific data, this paper designed a high-performance and scalable network architecture based on RoCE (RDMA over Converged Ethernet). Test results demonstrate that the RoCE-based HEPS data center network system achieves high bandwidth and ultra-low latency, stably maintains reliable transmission performance during the interaction of scientific data storage, computing, and analysis, and exhibits excellent scalability to adapt to the future expansion of HEPS beamlines.

Network doi: 10.3390/network5040052

Authors: Abdulrahman M. Abdulghani Azizol Abdullah A. R. Rahiman Nor Asilah Wati Abdul Hamid Bilal Omar Akram

Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability.

Network doi: 10.3390/network5040051

Authors: Hussain M. J. Almohri

Current federated identity management systems lack contextual awareness of workflows across independent systems, creating security gaps and workflow integrity challenges. This article details the design and implementation of Alohomora, a distributed workflow-aware authentication system that maintains cross-system workflow context through path-bound tokens. Alohomora complements existing identity providers such as OAuth and SAML by adding workflow orchestration capabilities while leveraging standard authentication protocols for initial user verification. The system introduces workflow graphs as a formal model for representing dependencies between functions across heterogeneous systems and employs a distributed caching architecture with collaboration groups for scalable session management. In a typical deployment scenario, an employee onboarding workflow across human resources services, account provisioning, and benefits systems forms a trust group where Alohomora enforces ordered step execution, validates prerequisite completion at each transition, and generates cryptographic completion assertions upon workflow finalization. Extensive performance evaluation under concurrent user requests demonstrates polynomial performance characteristics with superior scalability compared to centralized OAuth introspection. The results show that Alohomora maintains high throughput under heavy load while providing strong, secure access control through workflow path binding and distributed trust orchestration. The prototype implementation is available as open source.

Network doi: 10.3390/network5040050

Authors: Sarah Abdulelah Abbas Leili Farzinvash Mina Zolfy

Heterogeneous wireless sensor networks (HWSNs), comprising super nodes and normal sensors, offer a promising solution for monitoring diverse environments. However, their deployment is constrained by the limited battery life of sensors. To address this issue, clustering and routing techniques have been employed to conserve energy. Nevertheless, existing approaches often struggle with suboptimal energy distribution and weak network coverage. Additionally, they mostly failed to exploit other energy saving techniques such as sleep scheduling. This paper proposes a novel genetic algorithm (GA)-based approach to optimize sleep scheduling, routing, and clustering in HWSNs. The method comprises two phases, namely join sleep scheduling and tree construction, and clustering of normal nodes. Inspired by the concept of unequal clustering, the HWSN is split into some rings in the first phase, and the number of awake super nodes in each ring keeps the same. This approach addresses the challenges of balancing energy consumption and network lifetime. Furthermore, including network coverage and energy-related criteria in the proposed GA yields long-lasting network operation. Through rigorous simulations, we demonstrate that, on average, our algorithm reduces energy consumption and improves network coverage by 23% and 21.9%, respectively, and extends network lifetime by 501 rounds, compared to the state-of-the-art methods.

Network doi: 10.3390/network5040049

Authors: Mohammad A. Massad Abdallah Y. Alma’aitah Hossam S. Hassanein

This paper presents a real-time handover and link assignment framework for low-Earth-orbit (LEO) satellite networks operating in dense urban canyons. The proposed Markov chain-guided simulated annealing (MCSA) algorithm optimizes user-to-satellite assignments under dynamic channel and capacity constraints. By incorporating Markov chains to guide state transitions, MCSA achieves faster convergence and more effective exploration than conventional simulated annealing. Simulations conducted in Ku-band urban canyon environments show that the framework achieves an average user satisfaction of about 97%, providing an approximately 10% improvement over genetic algorithm (GA) results. It also delivers 10–15% higher resource utilization, lower blocking rates comparable to integer linear programming (ILP), and superior runtime scalability with linear complexity O(k·|U|·|S|). These results confirm that MCSA provides a scalable and robust real-time mobility management solution for next-generation LEO satellite systems.

Network doi: 10.3390/network5040048

Authors: Qiuli Wu Yufei Zhao Shicheng Li Yiqi Li Deyu Lin Xuefeng Jiang

Orbital angular momentum (OAM) beams exhibit divergence during transmission, which constrains the capacity of communication system channels. To address these challenges, intelligent reflecting surfaces (IRSs), which can independently manipulate incident electromagnetic waves by adjustment of their amplitude and phase, are employed to construct IRS-assisted OAM communication systems. By introducing additional information pathways, IRSs enhance diversity gain. We studied the simulations of two placement methods for an IRS: arbitrary placement and standard placement. In the case of arbitrary placement, the beam reflected by the IRS can be decomposed into different OAM modes, producing various reception powers corresponding to each OAM mode component. This improves the signal-to-noise ratio (SNR) at the receiver, thereby enhancing channel capacity. In particular, when the IRS is symmetrically and uniformly positioned at the center of the main transmission axis, its elements can be approximated as a uniform circular array (UCA). This configuration not only achieves optimal reception along the direction of the maximum gain of the orbital angular momentum beam but also reduces the antenna radius required at the receiver to half or even less.

Network doi: 10.3390/network5040047

Authors: Abdul Karim Kazi Muhammad Umer Farooq Raheela Asif Saman Hina

Vehicular Ad-Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS), enabling communication between vehicles and roadside infrastructure. This paper proposes an Adaptive Context-Aware VANET Routing (ACAVR) protocol designed to handle the challenges of high mobility, dynamic topology, and variable vehicle density in urban environments. The proposed protocol integrates context-aware routing, dynamic clustering, and geographic forwarding to enhance performance under diverse traffic conditions. Simulation results demonstrate that ACAVR achieves higher throughput, improved packet delivery ratio, lower end-to-end delay, and reduced routing overhead compared to existing routing schemes. The proposed ACAVR outperforms benchmark protocols such as DyTE, RGoV, and CAEL, improving PDR by 12–18%, reducing delay by 10–15%, and increasing throughput by 15–22%.

Network doi: 10.3390/network5040046

Authors: Rudolf Kovacs Bogdan Iancu Vasile Dadarlat Adrian Peculea

This paper investigates cooperative behavior among Autonomous Systems (ASs) within a federated network environment designed to support collaborative shared-technology deployment. It makes use of the concept of an AS federation, where independently managed systems adhere to a shared standard while maintaining implementation flexibility. Using a systematic game-theoretic framework, the study models various coalition structures—including full cooperation, partial coalitions, and defection—across several canonical cooperative games. The analysis evaluates the effects of different cooperation strategies and resource-sharing schemes on payoff distribution and coalition stability. Simulation results over short- and medium-to-long-term horizons demonstrate that cooperative coalition formation, especially with fair payoff allocation, consistently outperforms solitary strategies. The study also identifies key thresholds affecting partial coalition viability and explores the impact of defection on overall federation performance. By linking theoretical game models with practical deployment challenges in heterogeneous networked systems, this work offers valuable insights for designing mechanisms that promote effective cooperation in complex, resource-constrained environments.

Network doi: 10.3390/network5040045

Authors: Yifan Wu Lei Xiao Xia Du

As network automation and self-organizing networks (SONs) rapidly evolve, edge devices increasingly demand lightweight, real-time, and high-precision classification algorithms to support critical tasks such as traffic identification, intrusion detection, and fault diagnosis. In recent years, cross-entropy (CE) loss has been widely adopted in deep learning classification tasks due to its computational efficiency and ease of optimization. However, traditional CE methods primarily focus on class separability without explicitly constraining intra-class compactness and inter-class boundaries in the feature space, thereby limiting their generalization performance on complex classification tasks. To address this issue, we propose a novel classification loss framework—Contrastive Geometric Cross-Entropy (CGCE). Without incurring additional computational or memory overhead, CGCE explicitly introduces learnable class representation vectors and constructs the loss function based on the dot-product similarity between features and these class representations, thus explicitly reinforcing geometric constraints in the feature space. This mechanism effectively enhances intra-class compactness and inter-class separability. Theoretical analysis further demonstrates that minimizing the CGCE loss naturally induces clear and measurable geometric class boundaries in the feature space, a desirable property absent from traditional CE methods. Furthermore, CGCE can seamlessly incorporate the prior knowledge of pretrained models, converging rapidly within only a few training epochs (for example, on the CIFAR-10 dataset using the ViT model, a single training epoch is sufficient to reach 99% of the final training accuracy.) Experimental results on both text and image classification tasks show that CGCE achieves accuracy improvements of up to 2% over traditional CE methods, exhibiting stronger generalization capabilities under challenging scenarios such as class imbalance, few-shot learning, and noisy labels. These findings indicate that CGCE has significant potential as a superior alternative to traditional CE methods.

Network doi: 10.3390/network5040044

Authors: Farzaneh Nourmohammadi Jaume Comellas Uzay Kaymak

Elastic optical networks (EONs) must allocate resources dynamically to accommodate heterogeneous, high-bandwidth demands. However, the continuous setup and teardown of connections with different bit rates can fragment the spectrum and lead to blocking. The blocking predictors enable proactive defragmentation and resource reallocation within network controllers. In this paper, we propose two novel deep learning models (based on CNN–BiLSTM and CNN–LSTM) to predict blocking in EONs by combining spatial feature extraction from spectrum snapshots using 2D convolutional layers with temporal sequence modeling. This hybrid spatio-temporal design learns how local fragmentation patterns evolve over time, allowing it to detect impending blocking scenarios more accurately than conventional methods. We evaluate our model on the simulated NSFNET topology and compare it against multiple baselines, namely 1D CNN, 2D CNN, k-nearest neighbors (KNN), and support vector machines (SVMs). The results show that the proposed CNN–BiLSTM/LSTM models consistently achieve higher performance. The CNN–BiLSTM model achieved the highest accuracy in blocking prediction, while the CNN–LSTM model shows slightly lower accuracy; however, it has much lower complexity and a faster learning time.

Network doi: 10.3390/network5040043

Authors: Ahmad Aloqaily Emad E. Abdallah Aladdin Baarah Mohammad Alnabhan Esra’a Alshdaifat Hind Milhem

Connected and Autonomous Vehicles are promising for advancing traffic safety and efficiency. However, the increased connectivity makes these vehicles vulnerable to a broad array of cyber threats. This paper presents a novel hybrid approach for intrusion detection in in-vehicle networks, specifically focusing on the Controller Area Network bus. Ensemble learning techniques are combined with sophisticated optimization techniques and dynamic adaptation mechanisms to develop a robust, accurate, and computationally efficient intrusion detection system. The proposed system is evaluated on real-world automotive network datasets that include various attack types (e.g., Denial of Service, fuzzy, and spoofing attacks). With these results, the proposed hybrid adaptive system achieves an unprecedented accuracy of 99.995% with a 0.00001% false positive rate, which is significantly more accurate than traditional methods. In addition, the system is very robust to novel attack patterns and is tolerant to varying computational constraints and is suitable for deployment on a real-time basis in various automotive platforms. As this research represents a significant advancement in automotive cybersecurity, a scalable and proactive defense mechanism is necessary to safely operate next-generation vehicles.

Network doi: 10.3390/network5040042

Authors: Omesh A. Fernando Joseph Spring Hannan Xiao

As 5G and beyond networks grow in heterogeneity, complexity, and scale, traditional Intrusion Detection Systems (IDS) struggle to maintain accurate and precise detection mechanisms. A promising alternative approach to this problem has involved the use of Deep Learning (DL) techniques; however, DL-based IDS suffer from issues relating to interpretation, performance variability, and high computational overheads. These issues limit their practical deployment in real-world applications. In this study, CiNeT is introduced as a novel DL-based IDS employing Convolutional Neural Networks (CNN) within a bijective encoding–decoding framework between network traffic features (such as IPv6, IPv4, Timestamp, MAC addresses, and network data) and their RGB representations. This transformation facilitates our DL IDS in detecting spatial patterns without sacrificing fidelity. The bijective pipeline enables complete traceability from detection decisions to their corresponding network traffic features, enabling a significant initiative towards solving the ‘black-box’ problem inherent in Deep Learning models, thus facilitating digital forensics. Finally, the DL IDS has been evaluated on three datasets, UNSW NB-15, InSDN, and ToN_IoT, with analysis conducted on accuracy, GPU usage, memory utilisation, training, testing, and validation time. To summarise, this study presents a new CNN-based IDS with an end-to-end pipeline between network traffic data and their RGB representation, which offers high performance and enhanced interpretability through revisable transformation.

Network doi: 10.3390/network5030041

Authors: David Naseh Arash Bozorgchenani Swapnil Sadashiv Shinde Daniele Tarchi

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are scalable, private, and efficient. Existing methods, such as Federated Learning (FL) and Split Learning (SL), face critical limitations in terms of client computation burden and latency. To address these challenges, this paper proposes a novel hierarchical DML paradigm. We first introduce Federated Split Transfer Learning (FSTL), a foundational framework that synergizes FL, SL, and Transfer Learning (TL) to enable efficient, privacy-preserving learning within a single client group. We then extend this concept to the Generalized FSTL (GFSTL) framework, a scalable, multi-group architecture designed for complex and large-scale networks. GFSTL orchestrates parallel training across multiple client groups managed by intermediate servers (RSUs/HAPs) and aggregates them at a higher-level central server, significantly enhancing performance. We apply this framework to a unified T/NTN architecture that seamlessly integrates vehicular, aerial, and satellite assets, enabling advanced applications in 6G ITS and EO. Comprehensive simulations using the YOLOv5 model on the Cityscapes dataset validate our approach. The results show that GFSTL not only achieves faster convergence and higher detection accuracy but also substantially reduces communication overhead compared to baseline FL, and critically, both detection accuracy and end-to-end latency remain essentially invariant as the number of participating users grows, making GFSTL especially well suited for large-scale heterogeneous 6G ITS deployments. We also provide a formal latency decomposition and analysis that explains this scaling behavior. This work establishes GFSTL as a robust and practical solution for enabling the intelligent, connected, and resilient ecosystems required for next-generation transportation and environmental monitoring.

Network doi: 10.3390/network5030040

Authors: Börge Kordts Lea C. Brandl Andreas Schrader

The Internet of Things allows us to implement concepts such as Education 4.0 by connecting sensors, actuators, and applications. In the case of direct and explicit connections, we refer to ensembles that can consist of devices and applications. When realizing spatially distributed applications, there are scenarios in which these ensembles must coordinate with each other. In software development, this process is referred to as orchestration or choreography. This paper describes a software framework that provides orchestration or choreography for self-explaining ensembles using predefined rules based on a self-description of all involved components. The framework is capable of generating user instructions or explanations for smart environments that cover interaction details. The approach also forms a basis to provide information about event-based coordination. In a case study, we investigated the technical perception of a coordinated spatial learning game application (an ambient serious game). Most participants perceived the application as cohesive and found it responsive. These results suggest that our framework provides a solid foundation for implementing coordinated applications within smart environments that appear as unified applications.

Network doi: 10.3390/network5030039

Authors: Xing Xu Jianbin Zhao Yu Zhang Rongpeng Li

The rapid expansion of communication networks and increasingly complex service demands have presented significant challenges to the intelligent management of network resources. To address these challenges, we have proposed a network self-optimization framework integrating the predictive capabilities of the Large Language Model (LLM) with the decision-making capabilities of multi-agent Reinforcement Learning (RL). Specifically, historical network traffic data are converted into structured inputs to forecast future traffic patterns using a GPT-2-based prediction module. Concurrently, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm leverages real-time sensor data—including link delay and packet loss rates collected by embedded network sensors—to dynamically optimize bandwidth allocation. This sensor-driven mechanism enables the system to perform real-time optimization of bandwidth allocation, ensuring accurate monitoring and proactive resource scheduling. We evaluate our framework in a heterogeneous network simulated using Mininet under diverse traffic scenarios. Experimental results show that the proposed method significantly reduces network latency and packet loss, as well as improves robustness and resource utilization, highlighting the effectiveness of integrating sensor-driven RL optimization with predictive insights from LLMs.

Network doi: 10.3390/network5030038

Authors: Nofel Yaseen

Network monitoring is becoming increasingly challenging as networks grow in scale, speed, and complexity. The evolution of monitoring approaches reflects a shift from device-centric, localized techniques toward network-wide observability enabled by modern networking paradigms. Early methods like SNMP polling and NetFlow provided basic insights but struggled with real-time visibility in large, dynamic environments. The emergence of Software-Defined Networking (SDN) introduced centralized control and a global view of network state, opening the door to more coordinated and programmable measurement strategies. More recently, programmable data planes (e.g., P4-based switches) and in-band telemetry frameworks have allowed fine grained, line rate data collection directly from traffic, reducing overhead and latency compared to traditional polling. These developments mark a move away from single point or per flow analysis toward holistic monitoring woven throughout the network fabric. In this survey, we systematically review the state of the art in network-wide monitoring. We define key concepts (topologies, flows, telemetry, observability) and trace the progression of monitoring architectures from traditional networks to SDN to fully programmable networks. We introduce a taxonomy spanning local device measures, path level techniques, global network-wide methods, and hybrid approaches. Finally, we summarize open research challenges and future directions, highlighting that modern networks demand monitoring frameworks that are not only scalable and real-time but also tightly integrated with network control and automation.

Network doi: 10.3390/network5030037

Authors: Vindula L. Jayaweera Chamodi Peiris Dhanushika Darshani Sampath Edirisinghe Nishan Dharmaweera Uditha Wijewardhana

The hybridization of Near-Field Communication (NFC) with Visible Light Communication (VLC) presents a promising framework for robust, secure, and efficient wireless transmission. By combining proximity-based authentication of NFC with high-speed and interference-resistant data transfer of VLC, this approach mitigates the inherent limitations of each technology, such as the restricted range of NFC and authentication challenges of VLC. The resulting hybrid system leverages NFC for secure handshaking and VLC for high-throughput communication, enabling scalable, real-time applications across diverse domains. This study examines integration strategies, technical enablers, and potential use cases, including smart street poles for secure citizen engagement, patient authentication and record access systems in healthcare, personalized retail advertising, and automated attendance tracking in education. Additionally, this paper addresses key challenges in hybridization and explores future research directions, such as the integration of Artificial Intelligence and 6G networks.

Network doi: 10.3390/network5030036

Authors: Alfred Stephen Tonge Babu Kaji Baniya Deepak GC

In this paper, we introduce a novel, self-hosted Syslog collection platform designed specifically to address the challenges that small and medium enterprises (SMEs) face in implementing comprehensive syslog monitoring solutions. Our analysis begins with an assessment of current network observability practices, evaluating enterprise solutions, on-premises systems, and Software as a Service (SaaS) offerings to identify features crucial for SME environments. The proposed platform represents an advancement in the field through the incorporation of modern practices, including GitOps and continuous integration and continuous delivery/deployment (CI/CD), and its implementation onto a self-managed Kubernetes platform, which is an approach not commonly explored in SME-focused solutions. We will explore its scalability by leveraging dynamic templates, which allow us to select the number and type of nodes when deploying networks of various sizes. This architecture ensures organisations can deploy a pre-designed, scalable network monitoring solution without extensive external support. The resilience of the proposed platform is assessed by providing empirical evidence of the scaling performance and reliability under various failure scenarios, including node failure and high network throughput stress.

Network doi: 10.3390/network5030035

Authors: Waseem Al Aqqad

Cascading link failures continue to imperil power grids, transport networks, and cyber-physical systems, yet the relationship between a network’s robustness at the moment of attack and its subsequent resiliency remains poorly understood. We introduce a dynamic framework in which connectivity-based cascades and distributed self-healing act concurrently within each time-step. Failure is triggered when a node’s active-neighbor ratio falls below a threshold φ; healing activates once the global fraction of inactive nodes exceeds trigger T and is limited by budget B. Two real data sets—a 332-node U.S. airport graph and a 1133-node university e-mail graph—serve as testbeds. For each graph we sweep the parameter quartet (φ, B, T,attackmode) and record (i) immediate robustness R, (ii) 90% recovery time T90, and (iii) cumulative average damage. Results show that targeted hub removal is up to three times more damaging than random failure, but that prompt healing with B≥0.12 can halve T90. Scatter-plot analysis reveals a non-monotonic correlation: high-R states recover quickly only when B and T are favorable, whereas low-R states can rebound rapidly under ample budgets. A multiplicative fit T90∝B−βg(T)h(R) (with β≈1) captures these interactions. The findings demonstrate that structural hardening alone cannot guarantee fast recovery; resource-aware, early-triggered self-healing is the decisive factor. The proposed model and data-driven insights provide a quantitative basis for designing infrastructure that is both robust to failure and resilient in restoration.

Network doi: 10.3390/network5030034

Authors: Mahafuja Khatun Tasneem Darwish

Blockchain’s decentralized, immutable, and transparent nature offers a promising solution to enhance security, trust, and efficiency in supply chains. While integrating blockchain into the SCM process poses significant challenges, including technical, operational, and regulatory issues, this review analyzes blockchain’s potential in SCM with a focus on the key challenges encountered when applying blockchain in this domain—such as scalability limitations, interoperability barriers, high implementation costs, and privacy as well as data security concerns. The key contributions are as follows: (1) applications of blockchain across major SCM domains—including pharmaceuticals, healthcare, logistics, and agri-food; (2) SCM functions that benefit from blockchain integration; (3) how blockchain’s properties is reshaping modern SCM processes; (4) the challenges faced by businesses while integrating blockchain into supply chains; (5) a critical evaluation of existing solutions and their limitations, categorized into three main domains; (6) unresolved issues highlighted in dedicated “Critical Issues to Consider” sections; (7) synergies with big data, IoT, and AI for secure and intelligent supply chains, along with challenges of emerging solutions; and (8) unexplored domains for blockchain in SCM. By synthesizing current research and industry insights, this study offers practical guidance and outlines future directions for building scalable and resilient global trade networks.

Network doi: 10.3390/network5030033

Authors: Afkar Mohamed Ismail Yufei Zhao Gaohua Ju

This paper presents a long-range experimental demonstration of multi-mode multiple-input multiple-output (MIMO) transmission using orbital angular momentum (OAM) waves for Line-of-Sight (LoS) wireless backhaul applications. A 4 × 4 MIMO system employing distinct OAM modes is implemented and shown to support multiplexing data transmission over a single frequency band without inter-channel interference. In contrast, a 2 × 2 plane wave MIMO configuration fails to achieve reliable demodulation due to mutual interference, underscoring the spatial limitations of conventional waveforms. The results confirm that OAM provides spatial orthogonality suitable for high-capacity, frequency-efficient wireless backhaul links. Experimental validation is conducted over an 100 m outdoor path, demonstrating the feasibility of OAM-based MIMO in practical wireless backhaul scenarios.

Network doi: 10.3390/network5030032

Authors: Adel A. Ahmed

Nowadays, internet connectivity suffers from instability and slowness due to optical fiber cable attacks across the seas and oceans. The optimal solution to this problem is using the Low Earth Orbit (LEO) satellite network, which can resolve the problem of internet connectivity and reachability, and it has the power to bring real-time, reliable, low-latency, high-bandwidth, cost-effective internet access to many urban and rural areas in any region of the Earth. However, satellite orbital placement (SOP) and navigation should be carefully designed to reduce signal impairments. The challenges of orbital satellite placement for LEO include constellation development, satellite parameter optimization, bandwidth optimization, consideration of signal impairment, and coverage optimization. This paper presents a comprehensive review of SOP and coverage optimization, examines prevalent issues affecting LEO internet connectivity, evaluates existing solutions, and proposes novel solutions to address these challenges. Furthermore, it recommends a machine learning solution for coverage optimization and SOP that can be used to efficiently enhance internet reliability and reachability for LEO satellite networks. This survey will open the gate for developing an optimal solution for global internet connectivity and reachability.

Network doi: 10.3390/network5030031

Authors: Urvashi Rahul Saxena Rajan Kadel

In the original publication [...]

Network doi: 10.3390/network5030030

Authors: Urvashi Rahul Saxena Yogita Khatri Rajan Kadel Samar Shailendra

Cloud computing has increasingly adopted multi-tenant infrastructures to enhance cost efficiency and resource utilization by enabling the shared use of computational resources. However, this shared model introduces several security and privacy concerns, including unauthorized access, data redundancy, and susceptibility to malicious activities. In such environments, the effectiveness of cloud-based recommendation systems largely depends on the trustworthiness of participating nodes. Traditional collaborative filtering techniques often suffer from limitations such as data sparsity and the cold-start problem, which significantly degrade rating prediction accuracy. To address these challenges, this study proposes a Trusted Graph-Based Collaborative Filtering Recommender System (TGBCF). The model integrates graph-based trust relationships with collaborative filtering to construct a trust-aware user network capable of generating reliable service recommendations. Each node’s reliability is quantitatively assessed using a trust metric, thereby improving both the accuracy and robustness of the recommendation process. Simulation results show that TGBCF achieves a rating prediction accuracy of 93%, outperforming the baseline collaborative filtering approach (82%). Moreover, the model reduces the influence of malicious nodes by 40–60%, demonstrating its applicability in dynamic and security-sensitive cloud service environments.

Network doi: 10.3390/network5030029

Authors: Gabriele Merlach Martino Trevisan Danilo Giordano

The Encrypted Client Hello (ECH) extension to Transport Layer Security (TLS) and the new type of Domain Name System (DNS) records called HTTPS represent the latest efforts to improve user privacy by encrypting the server’s domain name during the TLS handshake. While prior studies have assessed ECH adoption from the server perspective, little is known about its usage in the wild from a passive network standpoint. In this paper, we present the first passive analysis of ECH and HTTPS DNS adoption using a month-long dataset collected from an operational network. We find that HTTPS DNS queries already make up approximately 8% of total DNS traffic, although responses to those queries are often incomplete, leading to increased query volume. Furthermore, 59% of QUIC flows include ECH, although only a negligible fraction is directed to servers supporting it. The remaining ECH flows are composed of GREASE values, intended to prevent protocol ossification. Our findings provide new insights into the current state and challenges in deploying privacy-enhancing protocols at scale.

Network doi: 10.3390/network5030028

Authors: Abiola Ifaloye Haifa Takruri Rabab Al-Zaidi

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services.

Network doi: 10.3390/network5030027

Authors: Vinay Sriram Iyer Yasantha Samarawickrama Giovani Estrada

The widespread adoption of cloud computing has amplified the demand for electric power. It is strategically important to address the limitations of reliable sources and sustainability of power. Research and investment in data centres and power infrastructure are therefore critically important for our digital economy. A novel heuristic for the minimisation of energy consumption in cloud computing is presented. It draws similarities to the concept of “network slices”, in which an orchestrator enables multiplexing to reduce the network “churn” often associated with significant losses of energy consumption. The novel network slicing–ring fencing ratio is a heuristic calculated through an iterative procedure for the reduction in cloud energy consumption. Simulation results show how the non-convex equation optimises power by reducing energy from 10,680 kJ to 912 kJ, which is a 91.46% efficiency gain. In comparison, the Heuristic AUGMENT Non-Convex algorithm (HA-NC, by Hossain and Ansari) reported a 312.74% increase in energy consumption from 2464 kJ to 10,168 kJ, while the Priority Selection Offloading algorithm (PSO, by Anajemba et al.) also reported a 150% increase in energy consumption, from 10,738 kJ to 26,845 kJ. The proposed network slicing–ring fencing ratio is seen to successfully balance energy consumption and computing performance. We therefore think the novel approach could be of interest to network architects and cloud operators.

Network doi: 10.3390/network5030026

Authors: Abdulla Juwaied Lidia Jackowska-Strumillo

Wireless Sensor Networks (WSNs) are specialised ad hoc networks composed of small, low-power, and often battery-operated sensor nodes with various sensors and wireless communication capabilities. These nodes collaborate to monitor and collect data from the physical environment, transmitting it to a central location or sink node for further processing and analysis. This study proposes two machine learning-based enhancements to the DEEC protocol for Wireless Sensor Networks (WSNs) by integrating the K-Nearest Neighbours (K-NN) and K-Means (K-M) machine learning (ML) algorithms. The Distributed Energy-Efficient Clustering with K-NN (DEEC-KNN) and with K-Means (DEEC-KM) approaches dynamically optimize cluster head selection to improve energy efficiency and network lifetime. These methods are validated through extensive simulations, demonstrating up to 110% improvement in packet delivery and significant gains in network stability compared with the original DEEC protocol. The adaptive clustering enabled by K-NN and K-Means is particularly effective for large-scale and dynamic WSN deployments where node failures and topology changes are frequent. These findings suggest that integrating ML with clustering protocols is a promising direction for future WSN design.

Network doi: 10.3390/network5030025

Authors: Oscar Baselga Anna Calveras Joan Adrià Ruiz-de-Azua

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services.

Network doi: 10.3390/network5030024

Authors: Jorg Wieme Mathias Baert Jeroen Hoebeke

Digital Twin Networks are advanced digital replicas of physical network infrastructures, offering real-time monitoring, analysis, and optimization capabilities. Despite their potential, the absence of a standardized definition and implementation guidelines complicates practical deployment. The existing literature often lacks clarity on tool selection and implementation specifics. In response, this paper aims to address these challenges by providing a complete guide and reference list of essential tools to implement Digital Twin Networks. Following the current research and work-in-progress from the definition initiative, including our own contributions, we propose a structured approach to Digital Twin Network implementation. Our methodology integrates insights from diverse sources to establish a coherent framework for developers and researchers. By synthesizing insights from the literature and practical experience, we define key components and functionalities critical to Digital Twin Network architecture. Additionally, we highlight challenges inherent to Digital Twin Network implementation and offer strategic approaches and mindsets for addressing them. This includes considerations for scalability, interoperability, real-time communication, data modeling, and security, ensuring a holistic approach to building effective Digital Twin Network systems.

Network doi: 10.3390/network5030023

Authors: Michel Dossou Steaven Chédé Anne-Carole Honfoga Marianne Balogoun Péniel Dassi François Rottenberg

The Internet of Things (IoT) is an upcoming technology that is increasingly being used for monitoring and analysing environmental parameters and supports the progress of farm machinery. Agriculture is the main source of living for many people, including, for instance, farmers, agronomists and transporters. It can raise incomes, improve food security and benefit the environment. However, food systems are responsible for many environmental problems. While the use of IoT in agriculture and environment is widely deployed in many developed countries, it is underdeveloped in Africa, particularly in West Africa. This paper aims to provide a systematic review on this technology adoption for agriculture and environment in West African countries. To achieve this goal, the analysis of scientific contributions is performed by performing first a bibliometric study, focusing on the selected articles obtained using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, and second a qualitative study. The PRISMA analysis was performed based on 226 publications recorded from one database: Web Of Science (WoS). It has been demonstrated that the annual scientific production significantly increased during this last decade. Our conclusions highlight promising directions where IoT could significantly progress sustainability.

Network doi: 10.3390/network5030022

Authors: Zekai Wang Yuming Nie

In this study, to address the problems of high feedback latency and redundant codebook traversal in traditional Reconfigurable Intelligent Surface (RIS) beam tracking systems, two novel experimental schemes are proposed: the Edge-Integrated RIS Control Mechanism (EIR-CM) and the Visually Steered RIS Control Mechanism (VSR-CM). The EIR-CM eliminates the feedback latency of the remote server and optimizes the local computation by integrating the RIS control system and the User Equipment (UE) into the same edge server to reduce the beam tuning time by 50%. The VSR-CM realizes beam tracking based on visual perception, and directly maps the UE position to the optimal RIS codebook with a response speed as low as milliseconds. Experimental results show that the EIR-CM reduces the RIS feedback latency to 1–2 s, and the VSR-CM can be further optimized to less than 0.5 s. The two mechanisms are applicable to 6G communications, smart transport, and drone networks, providing feasibility verification for low-latency and efficient RIS deployment.

Network doi: 10.3390/network5020021

Authors: Omesh A. Fernando Hannan Xiao Joseph Spring Xianhui Che

The exponential growth in the number of devices connected via the internet has led to the need to achieve granular programmability for increased performance, resilience, reduced latency, and jitter. Software Defined Networking (SDN) and Programming Protocol independent Packet Processing (P4) are designed to introduce programmability into the control and data plane of networks, respectively. Despite their individual potential and capabilities, the performance of combining SDN and P4 remains underexplored. This study presents a comprehensive evaluation of SDN with data plane programmability using P4 (SDN+P4) against traditional SDN with Open vSwitch (SDN+OvS), aimed at answering the hypothesis that combining SDN and P4 strengthens the control and data plane programmability and offers improved management and adaptability, which would provide a platform with faster packet processing with reduced jitter, loss, and processing overhead. Mininet was employed to emulate three distinct topologies: multi-path, grid, and transit-stub. Various traffic types were transmitted to assess performance metrics across the three topologies. Our results demonstrate that SDN+P4 outperform SDN+OvS significantly due to parallel processing, flexible parsing, and reduced overhead. The evaluation demonstrates the potential of SDN+P4 to provide a more resilient and stringent service with improved network performance for the future internet and its heterogeneity of applications.