Search Results (239)

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

Satellite communication protocols are increasingly optimized in software-defined, multiorbital networks that combine broadband satellite systems, non-terrestrial 5G components, and inter-satellite transport. This review examines intelligent optimization across the physical, medium-access, network, and transport layers, with emphasis on what can be measured, what can be controlled, and what can be safely deployed under standards and operational constraints. This paper first positions the literature across DVB/ETSI, 3GPP NTN, CCSDS/DTN, LEO routing, and recent AI and digital-twin research. It then links standards-defined control surfaces to layer-specific measurements, feedback delays, and safety constraints and compares optimization families using deployment-relevant criteria such as observability, runtime predictability, verification burden, and robustness. The review argues that the central challenge is not only a simulation-to-reality gap but an evidence gap between experimental gains and operational trust. To address this gap, this paper analyzes delayed observability, rare events, bounded onboard compute, action surface mismatch, certification, and security; formalizes a generic constrained optimization problem with delayed observations and standards-compliant actions; and proposes a digital-twin-assisted research methodology supported by a worked beam-hopping example. The main conclusion is that future progress is most likely to come from hybrid, standards-compliant, and twin-assisted optimization methods whose performance claims are tied to calibration, traceability, and explicit rollback logic. Full article

The emergence of wireless sensor network (WSN) technology is accompanied by intrinsic constraints and vulnerabilities that render it susceptible to malicious exploitation by intruders. The primary objective of this article is to address security issues caused by black hole attacks, which disrupt the proper functioning of the network and may result in data leakage and loss. We provide a control mechanism named “IDSHNAODV” to specifically counteract the effects of malicious nodes by controlling and removing the first Route Reply (RREP) coming from the black hole attack. This strategy will be put into practice and compared with the “HNAODV” protocol using the NS2 simulator. Three performance metrics will be used, along with a quantity of malicious black hole nodes. Full article

Tomatoes are highly vulnerable to a wide range of leaf diseases, which significantly reduce agricultural yield and quality. Timely and precise detection of these diseases is essential for sustainable crop management and food security. This study analyzes configuration-level bidirectional multi-scale feature propagation within the native YOLOv12-s architecture, with emphasis on architectural behavior under controlled experimental conditions. The computational topology and parameterization of YOLOv12 are preserved, while bidirectional feature aggregation is activated at configuration level to examine its influence on cross-scale semantic consistency and localization reliability. The framework was trained and evaluated on a curated dataset of 4030 annotated RGB images spanning ten tomato leaf disease categories. All models were trained under an augmentation-free protocol and unified evaluation settings to isolate architectural effects from data-driven performance inflation. Under these controlled conditions, configuration-level bidirectional activation yields measurable improvements in detection consistency and spatial agreement while maintaining identical model complexity. Performance is evaluated using mAP, precision, recall, F1-score, and error-type decomposition within a measurement-consistency framework. The proposed configuration achieves 95.9% mAP@50 and 87.1% mAP@50–95 under identical experimental conditions, providing empirical evidence that topology-preserving feature routing influences multi-scale semantic stability in lesion detection. Full article

Intermittently Connected Wireless Networks (ICWNs) are characterized by dynamic node mobility and the absence of persistent end-to-end paths, making them highly susceptible to security threats. This paper proposes a novel secure routing protocol, called the Evolutionary Game Theoretic model with Reinforcement Learning (EGT-RL), designed to provide adaptive and resilient protection against blackhole attacks in such networks. EGT-RL integrates Q-learning for dynamic threat assessment with evolutionary game theory to model and influence node behavior over time. Simulation results, based on both synthetic and real-world mobility traces, show that EGT-RL significantly outperforms three benchmark protocols in delivery ratio, packet drops, end-to-end latency, and communication overhead. Full article

Wireless sensor networks comprise many resource-constrained nodes that must protect both local readings and routing metadata. The sensors collect data from the environment or from the individual to whom they are attached and transmit it to the nearest gateway node via a wireless network for further delivery to external users. Due to wireless communication, the transmitted messages may be intercepted, rerouted, or even modified by an attacker. Consequently, security and privacy issues are of utmost importance, and the nodes must be protected against unauthorized access during transmission over a public wireless channel. To address these issues, we propose the Probabilistic Bit-Similarity-Based Key Agreement Protocol (PBS-KAP). This novel method enables two nodes to iteratively converge on a shared secret key without transmitting it or relying on pre-installed keys. PBS-KAP enables two nodes to agree on a symmetric session key using probabilistic similarity alignment with explicit key confirmation (MAC). Optimized Garbled Circuits facilitate secure computation with minimal computational and communication overhead, while Secure Sketches combined with Fuzzy Extractors correct residual errors and amplify entropy, producing reliable and uniformly random session keys. The resulting protocol provides a balance between security, privacy, and usability, standing as a practical solution for real-world WSN and IoT applications without imposing excessive computational or communication burdens. Security relies on standard computational assumptions via a one-time elliptic–curve–based base Oblivious Transfer, followed by an IKNP Oblivious Transfer extension and a small garbled threshold circuit. No pre-deployed long-term keys are required. After the bootstrap, only symmetric operations are used. We analyze confidentiality in the semi-honest model. However, entity authentication, though feasible, requires an additional Authenticated Key Exchange step or malicious-secure OT/GC. Under the semi-honest OT/GC assumption, we prove session-key secrecy/indistinguishability; full entity authentication requires an additional AKE binding step or malicious-secure OT/GC. Full article

Controlling pre-analytical conditions for medical biology tests, particularly during transport, is crucial for complying with the ISO 15189 standard and ensuring high-quality medical services. The use of drones, also known as unmanned aerial vehicles, to transport clinical samples is growing in scale, but requires prior validation to verify that there is no negative impact on the test results provided to doctors. This study aimed to establish a secure, high-quality solution for transporting biological samples by drone in a coastal region of France. The 80 km routes passed over several densely populated urban areas, with take-off and landing points within hospital grounds. The analytical and clinical impact of this mode of transport was compared according to two protocols: an interventional clinical trial on 30 volunteers compared to the reference transport by car, and an observational study on samples from 126 hospitalized patients compared to no transport. The system enabled samples to be transported without damage by maintaining freezing, refrigerated, and room temperatures throughout the flight, without any significant gain in travel time. Analytical variations were observed for sodium, folate, GGT, and platelet levels, with no clinical impact on the interpretation of the results. There is a risk of time-dependent alterations of blood glucose measurements in heparin tubes, which can be corrected by using fluoride tubes. This demonstrated the feasibility and security of transporting biological samples over long distances in line with the ISO 15189 standard. Controlling transport times remains crucial to assessing the quality of analyses. It is imperative to devise contingency plans for backup solutions to ensure the continuity of transportation in the event of inclement weather. Full article

The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack of in-protocol security, RPL is still quite susceptible to routing-layer attacks like Blackhole, Lowered Rank, version number manipulation, and Flooding despite its lightweight architecture. Lightweight, data-driven intrusion detection methods are necessary since traditional cryptographic countermeasures are frequently unfeasible for LLNs. However, the lack of RPL-specific control-plane semantics in current cybersecurity datasets restricts the use of machine learning (ML) for practical anomaly identification. In order to close this gap, this work models both static and mobile networks under benign and adversarial settings by creating a novel, large-scale multiclass RPL attack dataset using Contiki-NG’s Cooja simulator. To record detailed packet-level and control-plane activity including DODAG Information Object (DIO), DODAG Information Solicitation (DIS), and Destination Advertisement Object (DAO) message statistics along with forwarding and dropping patterns and objective-function fluctuations, a protocol-aware feature extraction pipeline is developed. This dataset is used to evaluate fifteen classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), AdaBoost (AB), and XGBoost (XGB) and several ensemble strategies like soft/hard voting, stacking, and bagging, as part of a comprehensive ML-based detection system. Numerous tests show that ensemble approaches offer better generalization and prediction performance. With overfitting gaps less than 0.006 and low cross-validation variance, the Soft Voting Classifier obtains the greatest accuracy of 99.47%, closely followed by XGBoost with 99.45% and Random Forest with 99.44%. Full article

Industrial wireless sensor and actuator networks (IWSANs) are central to Industry 4.0, supporting distributed sensing, actuation, and communication in cyber-physical production systems. Unlike previous studies, which focus on isolated constraints, this review synthesises recent work across eight coupled dimensions. These span reliability and fault tolerance, security and trust, time synchronisation, energy harvesting and power management, media access control (MAC) and scheduling, interoperability, routing and topology control, and real-world validation, within a unified comparative framework. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a Scopus search identified 60 primary publications published between 2022 and 2025. The analysis shows a clear shift from reactive designs to predictive approaches that incorporate learning methods and energy considerations. Fault detection now relies on deep learning (DL) and statistical modelling, security incorporates trust and intrusion detection, and new synchronisation and MAC schemes approach wired levels of determinism. Regarding applied contributions, the analysis notes that routing and energy harvesting advances extend network lifetime. However, gaps remain in mobility support, interoperability across protocol layers, and field validation. The present work outlines these open issues and highlights research directions needed to mature IWSANs into robust infrastructure for Industry 4.0 and the emerging Industry 5.0 vision. Full article

In recent years, there has been increasing investment in the deployment of massive commercial Low Earth Orbit (LEO) constellations to provide global Internet connectivity. These constellations, now equipped with inter-satellite links, can serve as low-latency Internet backbones, requiring LEO satellites to act not only as access nodes for ground stations, but also as in-orbit core routers. Due to their high velocity and the resulting frequent handovers of ground gateways, LEO networks highly stress mobility procedures at both the sender and receiver endpoints. On the other hand, a growing trend in networking is the use of technologies based on the Information Centric Networking (ICN) paradigm for servicing IoT networks and sensor networks in general, as its addressing, storage, and security mechanisms are usually a good match for IoT needs. Furthermore, ICN networks possess additional characteristics that are beneficial for the massive LEO scenario. For instance, the mobility of the receiver is helped by the inherent data-forwarding procedures in their architectures. However, the mobility of the senders remains an open problem. This paper proposes a comprehensive solution to the mobility problem for massive LEO constellations using the Named-Data Networking (NDN) architecture, as it is probably the most mature ICN proposal. Our solution includes a scalable method to relate content to ground gateways and a way to address traffic to the gateway that does not require cooperation from the network routing algorithm. Moreover, our solution works without requiring modifications to the actual NDN protocol itself, so it is easy to test and deploy. Our results indicate that, for long enough handover lengths, traffic losses are negligible even for ground stations with just one satellite in sight. Full article

The rapid expansion of Internet of Things (IoT) systems in critical infrastructures has raised significant concerns regarding network security and reliability. In particular, RPL (Routing Protocol for Low-Power and Lossy Networks), widely adopted in IoT communications, remains vulnerable to topological manipulation attacks such as Decreased Rank, Increased Rank, and the less-explored Worst Parent Selection (WPS). While several RPL security approaches address rank manipulation attacks, most assume static topologies and offer limited support for mobility. Moreover, trust-based routing and hybrid IDS (Intrusion Detection System) approaches are seldom integrated, which limits detection reliability under mobility. This study introduces a unified IDS framework that combines mobility awareness with trust-based decision-making to detect multiple rank-based attacks. We evaluate two lightweight, rule-based IDS architectures: a fully distributed model and a hybrid model supported by designated monitoring nodes. A trust-based mechanism is incorporated into both architectures, and their performance is assessed under static and mobile scenarios. Results show that while the distributed IDS provides rapid local responsiveness, the hybrid IDS maintains more stable latency and packet delivery under mobility. Additionally, incorporating trust metrics reduces false alerts and improves detection reliability while preserving low latency and energy usage, supporting time-sensitive applications such as healthcare monitoring. Full article

Real-time peer-to-peer communication in web browsers typically relies on centralized signaling servers, creating single points of failure, privacy vulnerabilities, and censorship risks. We present WebRTC Swarms, a fully decentralized signaling architecture integrated into GRIDNET OS that combines onion-routed relay circuits with designated verifier zero-knowledge authentication and cryptoeconomic incentives. The proposed system empowers peers to discover and connect without exposing identities or IP addresses through an overlay of incentivized full nodes that carry signaling traffic using transmission tokens. We introduce a MAC-based designated verifier ZK authentication protocol allowing peers sharing a pre-shared key to mutually authenticate without revealing the key, ensuring only authorized participants can join sessions while preserving unlinkability to outsiders across sessions. Through formal verification using TLA+, we prove key safety and liveness properties of both the signaling protocol and the authentication mechanism. Empirical evaluation demonstrates near-100% NAT traversal success via incentivized decentralized TURN relaying (compared to approximately 85% for STUN-only approaches), join latencies under 2 s for swarms of dozens of peers, and strong resilience against Sybil and denial-of-service attacks through token-based rate limiting. Our work represents the first practical integration of decentralized WebRTC signaling with designated verifier cryptographic authentication and built-in economic incentives, providing a privacy-first substrate for secure, community-governed communication networks. Full article

Depth-based probabilistic routing (DPR) is an efficient underwater acoustic network (UAN) routing protocol which resists the depth-spoofing attack. DPR’s optimal value of the unqualified forwarding probability depends on the UAN topology, condition, and threat state, which are highly dynamic. If the static forwarding probability used in DPR is set too low for the current state, packet delivery ratio (PDR) drops. If it is set too high, unnecessary forwarding occurs when the network is not under attack, thus wasting valuable energy. In this paper, we propose a novel routing protocol, which uses a feedback mechanism that allows the sink to continuously adapt the unqualified forwarding probability according to the current network state. The protocol aims to achieve an application-controlled desired delivery ratio using one of three proposed update algorithms developed in this work. We analyze the performance of the proposed algorithms through simulation. Results demonstrate that the proposed adaptive routing protocol achieves resilience to depth-spoofing attacks by successfully delivering more than 80% of generated packets in more than 95% of simulated networks, while avoiding unnecessary unqualified forwarding in normal conditions. Full article

The Border Gateway Protocol (BGP) is the core protocol for inter-domain routing on the Internet. However, due to its lack of built-in security authentication mechanisms, BGP is highly vulnerable to misconfigurations or malicious route announcements, which can lead to severe incidents such as route hijacking and information leakage. Existing detection methods face two major bottlenecks: First, as the scale of Autonomous System (AS)-level topology continues to grow, conventional graph neural networks struggle to meet the demands of computational resources and latency. Second, the observational data provided by current monitoring systems are inherently localized. To address these challenges, this paper proposes a Graph Learning-driven framework for BGP Anomaly Detection, named GLBAD. The core design of GLBAD comprises three components: First, to handle BGP’s large-scale network topology, we propose a graph partition method to perform a dedicated topological partitioning on the BGP network. Second, to overcome the limitation of localized observational data, we design a graph autoencoder-based approach for adaptive graph learning, enabling topology inference. Finally, integrating the above components, we develop a comprehensive BGP anomaly detection system to achieve real-time and accurate anomaly detection. We evaluate our approach on 20 real-world BGP anomaly events. Experimental results demonstrate that the proposed GLBAD effectively detects anomalies with less time consumption while achieving a lower false positive rate. Full article

With the rapid development of geological blockchains and Internet of Things-based data acquisition technologies, massive amounts of heterogeneous data are constantly emerging. However, this data is stored in a distributed manner across different organizational or business blockchains. Data sharing among multiple geological blockchains faces numerous challenges, either exposing sensitive data during verification or lacking effective authorization mechanisms. Therefore, how to achieve fine-grained access control and privacy protection across multiple blockchains has become a critical issue that must be addressed in geological data sharing. In this paper, we propose GeoCross, a cross-chain geological data sharing framework that enables fine-grained authorization management and privacy protection. First, GeoCross provides a hierarchical hybrid encryption mechanism that uses symmetric encryption for geological data protection and ciphertext-policy attribute-based encryption to enable flexible cross-chain access policies. Second, we integrate a Groth16-based zero-knowledge proof mechanism, which allows a chain to verify the existence, integrity, and accessibility of off-chain data without revealing the content. Furthermore, we introduce a Reputation-based Non-interactive Relay node Selection protocol (RNRS), which enhances the trustworthiness and fairness of cross-chain routing. Finally, we implement GeoCross in a multi-chain Hyperledger Fabric environment and evaluate its performance under real-world workloads. Results show that Groth16 verification requires only three bilinear pairings, achieving a throughput of up to 390 tps on a single chain and 1550 tps in a concurrent multi-chain environment. Even with 50% malicious nodes, the RNRS protocol still maintains a success rate of over 91%. These results demonstrate that GeoCross provides an efficient and practical solution for secure and privacy-preserving cross-chain geological data sharing. Full article