IET Networks最新文献

Software-defined wide area networks (SD-WAN) change how networks are operated to be more flexible, cost-conscious and efficient than traditional VPNs. However, significant challenges face the successful deployment of SD-WAN because of vulnerabilities in security, consistent quality of service (QoS) compliance and seamless connectivity for hybrid and multi-cloud environments. This paper discusses the most important ones: approaches to implementing IPsec encryption and zero trust framework security for the protection of SD-WAN networks. It further mentions connectivity solutions through other sophisticated routing mechanisms, such as segment routing (SR) and path computation element (PCE), with more emphasis on latency and scalability issues. QoS issues are addressed, notably jitter management, packet loss and latency reduction through dynamic path selection and traffic optimisation. The findings advocate the ability of SD-WAN to change the parent enterprise networks while consolidating continuous monitoring, optimisation and update with recent technology. Future work is set to explore the techniques within the deep learning community applicable in better resource allocation and scalability in SD-WAN implementations.

The rapid growth of interconnected devices and data traffic necessitates robust mobile networks, especially in rural areas with unreliable grid power. This paper introduces an optimisation framework for the off-grid green open radio access network architecture utilising renewable energy to meet this demand. The framework aims to optimise power and bandwidth allocation, ensuring high data rates and reliability from sustainable sources. In addition, the CO₂ emission factor and the quality of service are also optimised in which several assumptions and network metrics have been incorporated such as power consumption (PC), load demand, number of virtual machines (VMs) and maintenance cost. The problem is formulated as the weighted nonlinear problem. First, the model has been formulated using simplified data rate assumption and has been solved mathematically using Lagrange multipliers. Subsequently, the mathematical problem has been solved using the gradient descent numerical solution and then compared with sequential quadratic programming (SQP) and interior point algorithms. The second assumption is formulated as the data rate considering the noise and interference factors; the comparative analysis of SQP, active set and interior point algorithms has been presented. The third case assumed the dynamic behaviour of the network of users and VMs, where the problem is also solved mathematically and numerically. Empirical evidence from rural deployments confirms the enhancement of mobile coverage and service provision through the integration of solar and wind-powered base stations.

The increasing proliferation of Internet of Things (IoT) networks has resulted in rising energy demands, making energy management a crucial challenge for sustainable development. This paper presents a comprehensive survey of energy management strategies in IoT, focusing on energy harvesting and energy-saving techniques aimed at reducing energy consumption and extending the lifespan of IoT networks. In particular, the authors explore the integration of renewable energy sources, such as solar, thermal, and mechanical energy, to reduce the carbon footprint and improve the efficiency of IoT devices. Additionally, the survey highlights the security vulnerabilities that arise from energy management practices and proposes mitigation strategies to ensure secure and sustainable IoT ecosystems. The findings emphasise the importance of optimising both energy efficiency and security in future IoT deployments.

A Mobile Ad hoc Network (MANET) is a self-configuring, infrastructure-less network of mobile devices connected wirelessly. In this paper, a novel approach, the Predictive Energy-efficient Adaptive Routing Technique (PEAR) approaches to the infrastructure-less network of mobile devices, addressing the intrinsic challenges of MANETs. PEAR is a network management system that uses real-time predictive analytics to forecast and adapt to topological changes due to node mobility, ensuring robust and proactive route management. It incorporates energy-aware routing decisions, conserving battery life and extending operational longevity, and uses a multi-factor routing algorithm. PEAR ensures loop-free routing to minimize latency and maintain scalability, making it adept at handling the growing complexity of MANET applications. PEAR outperformed Mobile Ad hoc On-demand Routing, showing throughput improvements of 2.5% at 5 m/s and 3.125% at 30 m/s, routing overhead reductions of 66.67% at 5 m/s and 50% at 30 m/s, energy consumption decreases by 66.67% at 5 m/s and 55.56% at 30 m/s, and a significant reduction in average delay by 66.67% at 5 m/s and 55.56% at 30 m/s, proving its superior efficiency and reliability in mobile networks.

The advent of 5G networks has precipitated an unparalleled surge in demand for mobile communication services, propelled by the advent of sophisticated wireless technologies. An increasing number of countries are moving from fourth generation (4G) to fifth generation (5G) networks, creating a new expectation for services that are dynamic, transparent, and differentiated. It is anticipated that these services will be adapted to a multitude of use cases and will become a standard practice. The diversity of these use cases and the increasingly complex network infrastructures present significant challenges, particularly in the management of resources and the orchestration of services. Network Slicing is emerging as a promising approach to address these challenges, as it facilitates efficient Resource Allocation (RA) and supports self-service capabilities. However, effective network segmentation implementation requires the development of robust algorithms to guarantee optimal RA. In this regard, artificial intelligence and machine learning (ML) have demonstrated their utility in the analysis of large datasets and the facilitation of intelligent decision-making processes. However, certain ML methodologies are limited in their ability to adapt to the evolving environments characteristic of 5G networks and beyond (B5G/6G). This paper examines the specific challenges associated with the evolution of 5G and B5G/6G networks, with a particular focus on ML solutions for RA and dynamic network slicing orchestration requirements. Moreover, the article presents potential avenues for further research in this domain with the objective of enhancing the efficiency of next-generation mobile networks through the adoption of innovative technological solutions.

Industrial applications demand ultra-reliable and low latency communications, especially in Industrial Internet of Things (IIoT) environments. To meet these expectations, telecommunication network technologies have evolved, culminating in the deployment of fifth-generation (5G) networks by telecom operators worldwide. However, 5G and beyond networks, operating in millimetre-wave frequency bands, can suffer signal degradation caused by atmospheric phenomena, such as rainfall and atmospheric gases. This article presents a significant contribution to scientific research by proposing a methodology that leverages ITU-R recommendations (P.530, P.837, P.838, P.618-13, P.839-4, P.1511-2) for rain-induced attenuation and incorporates local meteorological data using the Gumbel Distribution. Also, the methodology considers ITU-R Recommendations (P.676, P.836, P.835) for atmospheric gas-induced attenuation. A comprehensive flowchart demonstrates the practical application of these recommendations, enabling accurate assessment of signal attenuation for various cities around the world with distinct geographical coordinates. The dataset is obtained from local Meteorological Stations or using digital maps from ITU recommendations. The results obtained with the approximate values of precipitation rates using the proposed method were compared with values from the ITU's study group and verified the effectiveness of the proposed approach, with relative errors not exceeding 1.07%. These analyses indicated the potential impact of atmospheric conditions on 5G network performance, offering valuable insights for optimising network design and improving communication reliability that is relevant to IIoT applications.

In emergencies where several ground base stations (GBS) are no longer available, mobile base stations based on unmanned aerial vehicles (UAVs) can efficiently resolve coverage issues in remote areas due to their cost-effectiveness and versatility. Natural disasters, such as a deluge, cause damage to the terrestrial wireless infrastructure. The main challenge in these systems is to determine the optimal 3D placement of UAVs to meet the dynamic demand of users and minimise interference. Various mathematical frameworks and efficient algorithms are suggested for designing, optimising, and deploying UAV-based communication systems. This paper investigates the challenges of 3D UAV placement through machine learning (ML) and enhanced affinity propagation (EAP). Lastly, the simulation results indicate that the proposed approach improves the system sum rate, interference, and coverage performance compared to DBSCAN, k-means, and k-means++ methods. Therefore, this paper identifies UAVs' most effective 3D placement, including minimising the number of UAVs, maximising the number of covered users, and maximising the system sum rate for an arbitrary distribution of users in the disaster area. Additionally, this paper addresses the issue of interference minimisation.

The future sixth-generation (6G) networks are envisioned to integrate satellites, aerial, ground, and sea networks to provide seamless connectivity. However, some challenges are associated with integrated networks, including optimal resource utilisation, energy efficiency, delay, higher data rates, heterogeneity, and on-demand connectivity. This paper focuses on optimising energy efficiency, resource utilisation, and task priority-based user association. To achieve this, a mathematical framework is formulated to maximise energy efficiency, resource utilisation, and user connectivity in integrated networks while satisfying constraints related to transmit power, data rate, and computation resources. The formulated problem is a binary linear programming problem, as the decision variable is binary and the constraints are linear. The authors solve this optimisation problem using three methods: the branch and bound algorithm (BBA), the interior point method (IPM), and the barrier simplex algorithm (BSA). The authors use the results obtained from BBA as a benchmark to evaluate the performance of IPM and BSA. Simulation results show that the performance of IPM and BSA is comparable to the BBA but with lower complexity.

A decode and forward relay assisted device-to-device (D2D) communications over a novel multicluster two-wave (MTW) fading is analysed with Terahertz (THz) channel conditions. Co-channel interference (CCI) over a recently proposed multi-cluster fluctuating two-ray (MFTR) fading is also present at relay and receiver. Maximal ratio combining (MRC) and power harvesting based on power splitting are present at relay and receiver. A characteristic function based approach is used here to derive success probability, outage probability and capacity with outage expressions. The expressions depend on THz channel parameters, distances between various nodes in D2D system, power harvesting parameter and fading channel conditions. The system is numerically analysed under various D2D network conditions.

A peer-to-peer (P2P) network is defined as a distributed network in which nodes with similar characteristics exchange data with others. Efficient data sharing in P2P systems is essentially faced with challenges due to uncertainties of erratic node failures and unreliable network connectivity. Data replication on multiple nodes can improve availability and alleviate latency. However, the number of created replicas and the corresponding location of these replicas need to be determined optimally. In this paper, a novel framework for data replication is proposed in P2P networks, in which optimal allocations are achieved based on the two objectives including the minimisation of delay and maximisation of hit rate of queries. The proposed model finds allocations that specify the number and location of each replica of existing files in the network based on two considered objectives. A multi-objective technique based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is utilised to solve the proposed model and to extract non-dominated Pareto-optimal solutions of the allocations. Finally, one of the allocations of the obtained Pareto optimal set is applied to the network and the delay and hit rate of queries pertaining to this situation is compared with two models: i) initial model of the network, that is, the state where there is no replica of the files except the original version of the file, and ii) the random replication model to verify the efficiency of the presented method. All simulations in this paper have been done using C++ programming language.