Network

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Recent advances in quantum computing have prompted urgent consideration of the migration of classical cryptographic systems to post-quantum alternatives. However, it is impossible to fully understand the impact that migrating to current Post-Quantum Cryptography (PQC) algorithms will have on various applications without the actual implementation of quantum-resistant cryptography. On the other hand, PQC algorithms come with complexity and long processing times, which may impact the quality of service (QoS) of many applications. Therefore, PQC-based protocols with practical implementations across various applications are essential. This paper introduces a new framework for PQC standalone and PQC–AES (Advanced Encryption Standard) hybrid public-key encryption (PKE) protocols. Building on prior results, we focus on securing applications such as file transfer, video streaming, and chat-based communication using enhanced PQC-based protocols. The extended PQC-based protocols use a sequence number-based mechanism to effectively counter replay and man-in-the-middle attacks and mitigate standard cybersecurity attack vectors. Experimental evaluations examined encryption/decryption speeds, throughput, and processing overhead for the standalone PQC and the PQC–AES hybrid schemes, benchmarking them against traditional AES-256 in an existing client–server environment. The results demonstrate that the new approaches achieve a significant balance between security and system performance compared to conventional deployments. Furthermore, a comprehensive security analysis confirms the robustness and effectiveness of the proposed PQC-based protocols across diverse attack scenarios. Notably, the PQC–AES hybrid protocol demonstrates greater efficiency for applications handling larger data volumes (e.g., 10–100 KB) with reduced latency, underscoring the practical necessity of carefully balancing security and operational efficiency in the post-quantum migration process. Full article

Cloud computing has witnessed rapid growth and notable technological progress in recent years. Nevertheless, it is still regarded as being in its early developmental phase, with substantial potential remaining to be explored—particularly through integration with emerging technologies such as the Metaverse, Augmented Reality (AR), and Virtual Reality (VR). As the number of service users increases, so does the demand for computational resources, leading data owners to outsource processing tasks to remote cloud servers. The internet-based delivery of cloud computing services consequently expands the attack surface and impacts the trust relationship between the service user and the service provider. To address these challenges, this study proposes a restricted access control framework based on homomorphic encryption (HE) and identity-based encryption (IBE) mechanisms. A formal analysis of the proposed model is also conducted under an unauthenticated communication model. Simulation results indicate that the proposed approach achieves a 20–40% reduction in encryption and decryption times, respectively, compared with existing state-of-the-art homomorphic encryption schemes. The simulation was performed using a 2048-bit key and data size, consistent with current industry standards, to improve key management efficiency. Additionally, the role-based hierarchy was implemented in a Salesforce cloud environment to ensure secure and restricted access control. Full article

As optical networks operate using light-based transmission, assigning wavelengths to the paths taken by traffic demands is a key aspect of their design. This paper revisits the wavelength assignment problem in optical backbone networks, focusing on survivability via 1 + 1 Optical Chanel (OCh) protection, which ensures fault tolerance by duplicating data over two disjoint optical paths. The analysis gives great emphasis to studying the influence of topological parameters on wavelength requirements, with algebraic connectivity being identified as the most significant parameter. The results show that, across a set of 27 real-world networks, the wavelength increment factor, defined as the ratio between the number of wavelengths required with protection and without protection, ranges from 1.49 to 3.07, with a mean value of 2.26. Using synthetic data, formulas were derived to estimate this factor from network parameters, resulting in a mean relative error of 12.7% and errors below 15% in 70% of the real-world cases studied. Full article

Cloud computing faces growing challenges in energy consumption due to the increasing demand for services and resource usage in data centers. To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. The proposed method, RLVMP, classifies VMs as sensitive or insensitive and dynamically allocates resources to minimize energy consumption while ensuring compliance with service level agreements (SLAs). Experimental results using the CloudSim simulator, adapted with data from Microsoft Azure, show that our model significantly reduces energy consumption. Specifically, under the lr_1.2_mmt strategy, our model achieves a 5.4% reduction in energy consumption compared to PABFD, 12.8% compared to PSO, and 12% compared to genetic algorithms. Under the iqr_1.5_mc strategy, the reductions are even more significant: 12.11% compared to PABFD, 15.6% compared to PSO, and 18.67% compared to genetic algorithms. Furthermore, our model reduces the number of live migrations, which helps minimize SLA violations. Overall, the combination of Q-learning and the Firefly algorithm enables adaptive, SLA-compliant VM placement with improved energy efficiency. Full article

The explosion of connected devices and data transmission in the Internet of Things (IoT) era brings substantial burden on the capability of cloud computing. Moreover, these IoT devices are mostly positioned at the edge of a network and limited in resources. To address these challenges, edge cloud-distributed computing networks emerge. Because of the distributed nature of edge cloud networks, many research works considering software defined networks (SDNs) and network–function–virtualization (NFV) could be key enablers for managing, orchestrating, and load balancing resources. This article provides a comprehensive survey of these emerging technologies, focusing on SDN controllers, orchestration, and the function of artificial intelligence (AI) in enhancing the capabilities of controllers within the edge cloud computing networks. More specifically, we present an extensive survey on the research proposals on the integration of SDN controllers and orchestration with the edge cloud networks. We further introduce a holistic overview of SDN-enabled edge cloud networks and an inclusive summary of edge cloud use cases and their key challenges. Finally, we address some challenges and potential research directions for further exploration in this vital research area. Full article

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is widely recognized as an effective multi-criteria decision-making algorithm for handover management in terrestrial cellular networks, especially in scenarios involving dynamic and multi-faceted criteria. While TOPSIS is widely adopted in terrestrial cellular networks for handover management, its application in satellite networks, particularly in Low Earth Orbit (LEO) constellations, remains limited and underexplored. In this work, the performance of three TOPSIS algorithms is evaluated for handover management in LEO satellite networks, where efficient handover management is crucial due to rapid changes in satellite positions and network conditions. Sensitivity analysis is conducted on Standard Deviation TOPSIS (SD-TOPSIS), Entropy-TOPSIS, and Importance-TOPSIS in the context of LEO satellite networks, assessing their responsiveness to small variations in key performance metrics such as upload speed, download speed, ping, and packet loss. This study uses real-world data from “Starlink-on-the-road-Dataset”. Results show that SD-TOPSIS effectively optimizes handover management in dynamic LEO satellite networks thanks to its lower standard deviation scores and reduced score variation rate, thus demonstrating superior stability and lower sensitivity to small variations in performance metrics values compared to both Entropy-TOPSIS and Importance-TOPSIS. This ensures more consistent decision-making, avoidance of unnecessary handovers, and enhanced robustness in rapidly-changing network conditions, making it particularly suitable for real-time services that require stable, low-latency, and reliable connectivity. Full article

The integration of Internet of Things (IoT) technology into the agricultural sector enables the collection and analysis of large amounts of data, facilitating greater control over internal processes, resulting in cost reduction and improved quality of the final product. One of the main challenges in designing an IoT system is the need for interoperability among devices: different sensors collect information in non-homogeneous formats, which are often incompatible with each other. Therefore, the user of the system is forced to use different platforms and software to consult the data, making the analysis complex and cumbersome. The solution to this problem lies in the adoption of an IoT standard that standardizes the output of the data. This paper first provides an overview of the standards and protocols used in precision farming and then presents a system architecture designed to collect measurements from sensors and translate them into a standard. The standard is selected based on an analysis of the state of the art and tailored to meet the specific needs of precision agriculture. With the introduction of a connector device, the system can accommodate any number of different sensors while maintaining the output data in a uniform format. Each type of sensor is associated with a specific connector that intercepts the data intended for the database and translates it into the standard format before forwarding it to the central server. Finally, examples with real sensors are presented to illustrate the operation of the connectors and their role in an interoperable architecture, aiming to combine flexibility and ease of use with low implementation costs. Full article

The increase in new cyber threats is the result of the rapid growth of using the Internet, thus raising questions about the effectiveness of traditional Intrusion Detection Systems (IDSs). Machine learning (ML) technology is used to enhance cybersecurity in general and especially for reactive approaches, such as traditional IDSs. In several instances, it is seen that a single assailant may direct their efforts towards different servers belonging to an organization. This behavior is often perceived by IDSs as infrequent attacks, thus diminishing the effectiveness of detection. In this context, this paper aims to create a machine learning-based IDS model able to detect malicious traffic received by different organizational network interfaces. A centralized proxy server is designed to receive all the incoming traffic at the organization’s servers, scan the traffic by using the proposed IDS, and then redirect the traffic to the requested server. The proposed IDS was evaluated by using three datasets: CIC-MalMem-2022, CIC-IDS-2018, and CIC-IDS-2017. The XGBoost model showed exceptional performance in rapid detection, achieving 99.96%, 99.73%, and 99.84% accuracy rates within short time intervals. The Stacking model achieved the highest level of accuracy among the evaluated models. The developed IDS demonstrated superior accuracy and detection time outcomes compared with previous research in the field. Full article

With the widespread deployment of various emerging intelligent applications, information timeliness is crucial for intelligent decision-making in vehicular networks, where vehicular edge computing (VEC) has become an important paradigm to enhance computing capabilities by offloading tasks to edge nodes. To promote the information timeliness in VEC, an optimization problem is formulated to minimize the age of information (AoI) by jointly optimizing task offloading and subcarrier allocation. Due to the time-varying channel and the coupling of the continuous and discrete optimization variables, the problem exhibits non-convexity, which is difficult to solve using traditional mathematical optimization methods. To efficiently tackle this challenge, we employ a hybrid proximal policy optimization (HPPO)-based deep reinforcement learning (DRL) method by designing the mixed action space involving both continuous and discrete variables. Moreover, an action masking mechanism is designed to filter out invalid actions in the action space caused by limitations in the effective communication distance between vehicles. As a result, a mask-assisted HPPO (MHPPO) method is proposed by integrating the action masking mechanism into the HPPO. Simulation results show that the proposed MHPPO method achieves an approximately 28.9% reduction in AoI compared with the HPPO method and about a 23% reduction compared with the mask-assisted deep deterministic policy gradient (MDDPG). Full article

Localization plays a crucial role in wireless sensor networks (WSNs) and it has sparked significant research interest. GPSs provide quite accurate positioning estimations, but they are ineffective indoors and in environments like underwater. Power usage and cost are further disadvantages, and so many alternatives have been proposed. Many works in the literature still base localization on RSSI measurements and often rely on methods to mitigate the effects of fluctuations in values, so it is important to know real values of RSSIs measured using common devices. This work presents the main localization techniques and exploits a real testbed to collect and evaluate RSSI measurements. An accuracy evaluation and a comparison among several localization techniques are also provided. Full article

Information-centric networking (ICN) is a promising approach to address the limitations of current host-centric IP-based networking. ICN models feature ubiquitous in-network caching to provide faster and more reliable content delivery, name-based routing to provide better scalability, and self-certifying contents to ensure better security. Due to the differences in the core architecture of ICN compared to existing IP-based networks, it requires special considerations to provide quality-of-service (QoS) or quality-of-experience (QoE) support for applications based on ICNs. This paper discusses the latest advances in QoS and QoE research for ICNs. First, an overview of ICN architectures is given, followed by a summary of different factors that influence QoS and QoE. Approaches for improving QoS and QoE in ICNs are then discussed in five main categories: in-network caching, name resolution and routing, transmission and flow control, software-defined networking, and media-streaming-based strategies. Finally, open research questions for providing QoS and QoE support in ICNs are outlined for future research. Full article