ABSTRACTThe 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.

Abstract 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.

Abstract 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.

ABSTRACTThe 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.

Abstract Intrusion detection systems built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. The authors used a qualitative method for analysing and evaluating the performance of network intrusion detection system (NIDS) in a systematic way. However, their approach has limitations as it only identifies gaps by analysing and summarising data comparisons without considering quantitative measurements of NIDS's performance. The authors provide a detailed discussion of various deep learning (DL) methods and explain data intrusion networks based on an infrastructure of networks and attack types. The authors’ main contribution is a systematic review that utilises meta‐analysis to provide an in‐depth analysis of DL and traditional machine learning (ML) in notable recent works. The authors assess validation methodologies and clarify recent trends related to dataset intrusion, detected attacks, and classification tasks to improve traditional ML and DL in NIDS‐based publications. Finally, challenges and future developments are discussed to pose new risks and complexities for network security.

Abstract The rapid increment of mobile device usage and the corresponding huge data volume generated afterwards, necessitated the utilisation of the 5G network spectrum. This is deployed today in terrestrial communication in a non‐stand‐alone (NSA) architectural mode; where 5G networks are supported by 4G LTE networks. Hence, the current 5G implementation with the gargantuan number of mobile subscribers, poses challenges to the choice of network Radio Access Technology (RAT) selection between 4G and 5G networks, among available multiple base‐stations to mobile (travelling) users, with respect to their location, bandwidth requirement, and mobility style. Hence, to address the scenario presented above, the authors record live signal measurements of 4G and 5G networks by a travelling user, that transversed multiple 5G NSA base stations. RAT selection implementations were carried out with support vector machine (SVM), deep neural network (DNN), and eXtreme Gradient Boosting (XGBoost) algorithms to select an appropriate RAT between 4G and 5G RATs, for effective resource allocation for travelling users’ requirements. Evaluation of results with standard classification metrics shows XGBoost with overall outstanding accuracy performance at 99.64%.

Abstract Cross‐site scripting (XSS) attack has been one of the most dangerous attacks in cyberspace security. Traditional methods essentially discover XSS attack by detecting malicious payloads in requests, which is unable to distinguish attacking attempts with the attacking reality. The authors collect responses from a web server and train a bagging‐based PU learning model to determine whether the XSS vulnerability is truly triggered. To validate the authors’ proposed framework, experiments are performed on 5 popular web applications with 11 specified CVE recorded vulnerabilities and 32 vulnerable inputs. Results show that the authors’ approach outperforms existing research studies, effectively identifies the attacking reality from attacking attempts, and meanwhile reduces the number of worthless security alarms.

An even faster and more heterogeneous communication infrastructure is planned the 6G network, based on 5G in a way that leads us to much more deeply connected, programmable, intelligent, and sensing devices, with excellent network performance and coverage, and new dimensions of functionality. Therefore, 6G brings even greater challenges to network/ traffic engineering and optimisation. This virtual collection on Network/Traffic Optimisation towards 6G Network brings together the best six research papers submitted from academia, and reflects some of the latest and original achievements, concentrating on the performance of a mobile hotspot in vehicular communication, on the mobility modelling and ad hoc routing in Flying Ad-hoc NETworks (FANETs), on the performance of a joint antenna and relay selection Multiple-Input Multiple-Output (MIMO) system for cooperative Non-Orthogonal Multiple Access (NOMA) networks, on optimal resource optimisation based on multi-layer monitoring and Machine Learning (ML), on Voice over Wi-Fi Security Threats-Address Resolution Protocol (ARP) attacks and countermeasures-and on the management of 5G and Beyond networks through cloudnative deployments and end-to-end monitoring. Although the rapid and substantial changes in networking technologies towards the 6G Network over the recent years could readily justify this virtual issue, our real motivation was the 13th event of the International Symposium of Communications Systems, Networks and Digital Signal Processing, held in Porto, Portugal (20-22 July 2022), and the IET's open call. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.