IET Networks最新文献

Vehicular ad hoc networks (VANETs) are an essential enabler of intelligent transport systems (ITS), facilitating real-time communication among vehicles to enhance traffic safety and mobility. However, challenges such as high node mobility, frequent topology changes, and variable network density continue to impede the design of reliable and efficient routing protocols. This paper proposes CARAC (Cluster-based Ant-colony Routing with Adaptive Cluster Head), a hybrid routing protocol that integrates dynamic clustering using the K-medoids algorithm with Ant Colony Optimisation (ACO) to improve route stability, scalability, and data delivery performance. CARAC forms mobility-aware clusters by grouping vehicles based on spatial proximity and relative velocity. Each node periodically computes a local stability index to evaluate its membership within a cluster, allowing adaptive cluster maintenance. The protocol also incorporates ACO for optimal path selection and utilises Road Side Units (RSUs) as relays when direct communication between cluster heads is not feasible. Simulation experiments conducted in NS-3 with a vehicular network scenario demonstrate that CARAC consistently outperforms benchmark protocols such as AQRV, MetaLearn and CPB. It delivers higher route discovery success, better packet delivery performance, lower latency, and greater throughput. These results validate the advantages of combining clustering, bio-inspired optimisation, and adaptive stability evaluation in VANET routing, positioning CARAC as a robust and scalable solution for next-generation ITS applications.

Large scale software-defined networks have two main concerns, which are scalability and reliability. One of the problems with multi-controller architectures in these networks is the static mapping between SDN switches and controllers, which prevents the control plane from adapting to traffic changes. The dynamic mapping between switches and controllers by migrating switches from highly loaded controllers to lightly loaded controllers can provide compatibility for the control plane. This paper first discusses multi-controller load balancing as a key research challenge and then explores switch migration as a solution. A variety of load balancing methods based on switch migration are investigated and then a comprehensive comparison is made between them.

This study experiments with machine learning algorithms for detecting distributed denial of service attacks as a multiclass classification problem. The algorithms included the K-nearest neighbours, decision trees, support vector machines, random forests, extreme gradient boosting, gradient boosting machines and multilayer perceptron. We validated the models using the hold-out and cross-validation methods, performed class and model ablation analysis to evaluate performance impacts and applied feature selection techniques, feature importance and statistical tests. For instance, using 10-fold cross-validation with 79 features, 11 attack types and regular network traffic, the tree-based models achieved accuracies ranging from 75.69% to 76.24%. When using 15 features, seven attacks and regular network traffic, model accuracy improved significantly, ranging from 97.77% to 98.08%. Furthermore, in specific application scenarios, some models achieved near-perfect classification performance. Decision tree achieved the highest accuracy score for the local network communication scenario, reaching 99.86%, followed by software distribution or updates at 99.70%, web platforms and online applications at 98.25%, video streaming or online gaming at 97.06%, infrastructure monitoring and management at 95.00% and directory services and corporate authentication at 87.15%. Depending on the application scenario, our results indicate that specialised models can support classification tasks targeting specific system components with high performance.

This paper proposes a strategy for designing Open Radio Access Networks (ORAN) to maximise their energy efficiency using solar power, supplemented by Reconfigurable Intelligent Surfaces (RIS) and Mobile Edge Computing (MEC). Because grid power is not always available where these ORAN systems are built, our approach manages the difficulties created by dynamic energy and timing issues found in isolated environments. The approach concentrates on allocating energy to all transmitters, CPU speed and RIS phases in real time, subject to strict rules on power use, latency issues and heat. The primal-dual algorithm we propose reacts to queue and energy changes to update the dual variables and control policies without access to every channel parameter. Our combined (composite) cost function measures energy use, delays encountered by users, reliability of the SINR and fairness. Results from the simulation indicate that using the proposed method lowers energy usage by 25% and average delay by 18%, outperforming baseline models under varying solar and traffic patterns. Robustness is further validated through sensitivity and ablation analyses. This work demonstrates the feasibility of deploying sustainable, intelligent ORAN infrastructures in remote 6G scenarios where conventional power and connectivity are unavailable.

This work presents a novel machine learning (ML)-driven framework to optimise reactive routing protocols (RRPs) in mobile ad hoc networks (MANETs), tackling congestion control through intelligent, real-time protocol selection. Building on our prior Adaptive Expanding Ring Search (AERS) method enhanced with random early detection (RED), the study introduces an ML classification system that dynamically identifies the most efficient RRP based on network conditions. High-accuracy classifiers, AdaBoost (95%), K-Nearest Neighbours (93%), and Decision Trees (92%), enable data-driven decision-making, systematically evaluating protocols across diverse topologies to maximise performance. The framework ensures context-aware routing, significantly improving Quality of Service (QoS) through enhanced packet delivery, reduced latency, and robust congestion mitigation. Rigorous NS-3 simulations validate the approach, demonstrating measurable gains over conventional methods. By integrating predictive analytics into routing strategy, this research advances the design and deployment of RRPs, bridging algorithmic innovation with practical implementation. The results offer high-impact insights for both academic research and real-world MANET applications, establishing a new paradigm for adaptive, efficient routing in dynamic wireless environments.

5G networks and beyond set a critical requirement for both massive capacity and seamless connectivity to all users. This implies that, the network must have both very high capacity and users' fairness. Non-Orthogonal Multiple-Access (NOMA)-based Multibeam Satellite Networks (MBSNs) are designed for 5G networks, and are thus, required to provide both high capacity and high users' fairness. Most existing power-allocation (PA) algorithms for NOMA-MBSNs focus on maximising the network's throughput alone, with not much attention on user's fairness. This leaves a critical requirement of 5G networks unaddressed. As an attempt to close this gap, this paper, proposes a PA algorithm that maximises the users' fairness of a 2Users NOMA-MBSN. To do so, the maximisation request is formulated as an optimisation problem. The original problem being NP-hard, it is decomposed into two sub-problems; namely, the intra-beam and the inter-beam fairness maximisation. Each of these problem is independently solved by means of numerical search methods. In this regards, the concept of converging the Offered-Capacity to Traffic-Request (OCTR) ratios of respective users, in order to maximise the system's fairness, is employed. Thus, based on the OCTR-ratios convergence concept, the fairness maximisations power-allocation algorithms, which respectively solve the two sub-problems, are designed. The two algorithms include the intra-beam and the inter-beam power-allocation algorithms. A global power-allocation algorithm (PAA-1) combines the two algorithm, to yield a solution to the original problem. Numerical results confirm that, the algorithm makes all the OCTR-ratios of all user converge; thus maximising the network's users-fairness. Results also demonstrate the superiority of the proposed PAA-1 with respect to achieved system's fairness, compared to some other existing PA algorithms.

This paper investigates the characteristics of slotted ALOHA and power-domain nonorthogonal multiple access (PD-NOMA) hybrid schemes, focusing on two key performance metrics, namely device transmission success probability and base station throughput, while also considering distance-based unfairness among devices. We analyse three distinct hybrid schemes: a scheme where all devices transmit signals with equal power without utilising received signal strength (RSS) targets (Scheme 1), a scheme where devices randomly select one of multiple RSS targets at the nearest base station (Scheme 2), and a scheme where devices select one of multiple RSS targets based on their proximity to the nearest base station (Scheme 3). Our findings reveal that while Scheme 1 has the highest performance, it also has the most pronounced distance-based unfairness. Conversely, Scheme 2 has the lowest performance but effectively mitigates distance-based unfairness. The performance of Scheme 3 improves as the number of RSS targets increases; however, this coincides with an increase in distance-based unfairness. These results suggest that achieving a balance between fairness and performance improvement may be inherently challenging in grant-free access schemes.

A Software-Defined Network (SDN)-based Vehicular Ad Hoc Network (VANET) plays a crucial role in Intelligent Transport Systems (ITS) by enhancing road safety for drivers and vehicles through the periodic exchange of messages and data related to traffic, vehicle status, and weather conditions. Additionally, it offers entertainment services for passengers. However, SDN-based VANETs face security challenges, particularly in the central control unit, making them vulnerable to Distributed Denial-of-Service (DDoS) attacks, which can disrupt the entire network. Moreover, due to the programmability of SDN infrastructure, injection attacks can manipulate traffic or generate false crisis events. The network is also susceptible to various cyber threats, including man-in-the-middle (MITM), tracking, and replay attacks, necessitating robust security measures. Several security frameworks have been proposed to mitigate these risks, but many authentication mechanisms suffer from high computational and communication costs or provide protection against specific attacks while remaining ineffective against others. To address these limitations, we introduce a hybrid security framework integrating an authentication system between the trusted authority (TA), lead vehicle (LV), and other vehicles, along with an intrusion detection system (IDS). The authentication process involves key generation by the TA, mutual authentication between the TA and LV, as well as between the LV and other vehicles, while ensuring secure encryption using the AES-ECDH algorithm. To enhance security further, the proposed IDS utilises Fuzzy C-Means clustering to detect malicious activities and network threats. Performance analysis demonstrates that our approach effectively improves security, privacy, and efficiency while maintaining a low computational overhead, outperforming existing solutions.

The transformation of industrial systems through real-time analytics, autonomous control, and intelligent data acquisition has underscored the pivotal role of Industrial Edge Computing (IEC) in next-generation Industrial Internet of Things (IIoT) environments. By enabling decentralized processing close to data sources, IEC enhances responsiveness, supports latency-sensitive applications, and reduces the strain on centralized infrastructure. However, as IIoT ecosystems grow in scale and complexity, traditional edge solutions face increasing challenges related to device heterogeneity, dynamic network conditions, bandwidth constraints, and energy efficiency. This special issue explores emerging advancements in edge intelligence that address these pressing challenges. It brings together innovative research that leverages artificial intelligence, advanced communication technologies, and architectural innovations to improve the adaptability, resilience, and performance of edge-enabled industrial systems. The featured studies contribute novel techniques for real-time data processing, secure and efficient communication, and intelligent decision-making at the edge, all of which are essential for supporting industrial automation, predictive maintenance, and cyber-physical operations. Collectively, these contributions highlight the immense potential of edge intelligence to redefine the operational landscape of industrial systems. This issue is intended to support ongoing research and practical innovation in the evolving domain of edge-enabled IIoT technologies.

The introduction of vehicle clouds, whose processing, sensor, and networking capabilities are forcefully dispersed to authorised users, is prompted by the rising popularity of cloud computing. When certain vehicles enter and exit the cloud, new computational resources become available, creating an unpredictable environment that makes measuring key performance indicators like job completion time very challenging. As part of a Nomadic Vehicular Cloud (NVC) system that includes automobiles on a roadway, our main contribution is to reduce job migrations. It has been calculated how long it will take to complete the task without spending more money because of vehicular cloud operations. The simulation outcome showed that the suggested approach successfully reduces task completion time and enhances NVC performance. According to the performance data, our technique shortens work completion time by 25% compare to other approaches.