Researchers from both academia and industry have started exploring the potential of sixth generation cellular networks, envisioning novel concepts and futuristic capabilities. An empirical analysis of real-world fifth generation (5G) deployments serves a compass to direct the next stage of evolution and provides insights on the additional improvements required for the future services and applications. However, acquiring real-world measurement data at a city or county scale poses substantial challenges in terms of time and cost. To address this issue, this paper presents a practical and cost-effective data collection testbed and a methodology that harnesses the existing services provided by municipal council authorities, including curbside waste collections to generate large-scale realtime network coverage maps. Rich datasets of measurement data collected from multiple fourth generation (4G) and 5G cells over seven months in Nottingham, United Kingdom (UK) for all four major UK network operators, namely EE, Vodafone, O2, and Three Mobile, are provided. These large datasets can be utilized for analyzing network deployment options, coverage, and future service provisioning as well as designing and training artificial intelligence and machine learning algorithms to further optimize the mobile networks. In addition, the paper reviews the latest empirical 5G network analysis tools and techniques, which were not seen in previous generations.

Mobile network performance plays a crucial role in ensuring seamless connectivity for users, yet significant regional disparities persist across urban and rural areas. This study evaluates the coverage quality of four major mobile network operators (MNOs) across multiple counties around the River Severn Catchment Area, including performance metrics based on Acceptable Voice, Essential Data, Good Data, and Excellent Data etc. However, one limitation is that only the best available network connection is measured, meaning that areas where a handset holds onto a 4G connection despite reverting to 2G for voice calls (due to VoLTE limitations) may not be fully accounted for. Furthermore, call setup times and failure rates, key indicators of real-world voice service reliability, are not explicitly captured in this study, although they remain essential factors for future research. The study uses coverage measurements and correlation matrices to highlight network uniformity, infrastructure sharing patterns, and independent deployment strategies. The findings indicate that urban centres exhibit strong inter-provider correlations, suggesting some degree of infrastructure sharing. Many operators argue that network density remains insufficient due to high contention levels and regulatory constraints, such as planning departments restricting new infrastructure deployment. Particularly with EE, which operates with a more independent deployment model. Vodafone consistently provides superior data coverage, whereas EE leads in essential connectivity, ensuring basic service availability. These findings underscore the need for targeted rural investments, infrastructure-sharing policies, and AI-driven network optimisation to enhance service equality. The study provides valuable insights for policymakers, telecom providers, and researchers to bridge the digital divide and improve nationwide network reliability.

The increasing demand for emerging vehicular services, such as immersive entertainment, safety applications, and enhanced infotainment, has driven the development of Vehicle-to-Everything communication. However, vehicular networks face significant challenges due to stringent Quality of Service (QoS) requirements and the highly dynamic nature of wireless environments. Traditional Radio Access Network (RAN) architectures struggle to adapt to these conditions, necessitating more flexible and intelligent solutions. Open RAN, with its virtualised and intelligent architecture, offers a promising approach by incorporating Artificial Intelligence and Machine Learning for real-time network optimisation. This paper proposes a Deep Reinforcement Learning (DRL)-based Modulation and Coding Scheme (MCS) selection algorithm within an Open RAN-enabled vehicular network to optimise resource usage while ensuring QoS compliance. The proposed algorithm leverages the flexibility of Open RAN and the adaptability of DRL to dynamically configure MCS parameters, enhancing the Quality of Experience for users in challenging vehicular scenarios. Simulation results demonstrate that the DRL-based approach reduces network resource usage by 33% compared to conventional SNR-based MCS selection while improving QoS satisfaction by approximately 5%.

Network slicing enables 5G/6G networks to support ultrareliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). However, while this virtual networking technology enhances network efficiency, it also adds substantial signaling overhead. Maintaining submillisecond latency and managing dense deployments require continuous signaling at high resolution, which keeps hardware components active, leading to increased energy consumption. In this article, we introduce a novel network controller that manages slice congestion and admission, designed to meet flexible Quality-of-Experience requirements for both priority and nonpriority traffic. Utilizing metadata from Internet of Things (IoT) device applications and network characteristics, we introduce adaptability and elasticity features, enabled by transfer and reinforcement learning, significantly lowering signaling overhead and network resources. Further, analytical results show the proposed framework effectively reduces rejection rates and congestions across varying mMTC and eMBB traffic loads.

With UAVs becoming increasingly prevalent across a diverse array of industries, their extensive adoption has encouraged a great increase in research attention. Combining multiple UAVs together to form UAV swarms provides increased mission performance over singular UAVs. Moreover, the use of multiple UAV’s brings the ability to distribute computation for enhanced efficiency, or widen perspectives for UAV applications with sensors. This paper proposes a novel leader-follower structured UAV swarm along with a suitable network for inter-UAV coordination and formation control. A simulation of the swarm’s kinematics will be completed using Simulink to gain insight into the accuracy of the proposed formation. Additionally a simulation of the formation control network using COOJA will be executed to investigate and analyse it’s effectiveness. Results suggest that the UAV’s have excellent tracking abilities of instructed trajectories and when incorporating the formation control network, transmission delays have a negligible effect on performance.

The 5G era has seen the largest number of studies around Radio Environment Maps (REM) than in the previous three generations combined. Visualization of network coverage on interactive maps provides contextual information and numerous benefits to operators, regulators and to the public. In this context, spatial interpolation and extrapolation techniques are used to add synthetic data points between measurements to fill gaps in the data, where techniques such as machine learning, Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) are used to enhance the quality of REMs. In this paper we present a state-of-the-art software package, which integrates a series of interpolation methods, augmented with polygon intersection queries functionality to control data used for estimation of coverage on the roads. The proposed web-based application is powered by a set of modular Python packages, making it future-proof and real-world ready, enabling efficient and precise network management.

With the rise of requirements for high-speed and low-latency connectivity, innovative approaches such as network slicing, Quality of Service (QoS) Provisioning, and reinforcement learning-based resource allocation, including the use of resource blocks (RBs) including radio resources, have to keep pace with these evolving requirements. By utilising network intelligence through machine learning and deep reinforcement learning, there is a potential to enhance QoS provisioning, expand the current network capacity, reduce congestion and latency, improve energy efficiency, and thus support new business models and revenue sources. The progress so far suggests that while considering RBs, network-slicing datasets, intertwined with QoS provisioning, have not been explored comprehensively, and packet drop probability or rate has not been taken into account in resource allocation. This paper proposes a network slicing method that uses seven machine learning algorithms and demonstrates its efficiency and accuracy compared to benchmarks in the literature with respect to QoS. Moreover, a priority algorithm is developed to ensure that packets with a high chance of being dropped (affecting QoS) are queued first. A resource allocation algorithm considering QoS provisioning and RBs is utilised to improve network performance based on a mathematical derivation of packet drop rate. Furthermore, a virtualisation of the processing between Cloud and Edge depends on the network slice. By intelligently distributing tasks between Cloud and Edge resources using deep reinforcement learning and genetic algorithms, an offloading script ensures uninterrupted service availability even when all network resources (i.e., RBs) are in use, thus maintaining the desired QoS.

With the growing popularity of the Internet of Things (IoT), billions of devices are anticipated to be deployed in various industries without establishing trust between them. In environments without pre-established trust, reputation systems provide an effective method of assessing the trustworthiness of IoT devices. There has been considerable literature on deploying reputation systems in industries that have not yet established trust among themselves. Therefore, the article reviews published studies on reputation systems for IoT applications to date, focusing on decentralised systems and decentralised systems using blockchain technology. These studies are evaluated regarding security (including integrity and privacy) and non-security requirements to highlight open research challenges. In alignment with this, an analysis and summary of the existing review studies on reputation systems for particular IoT applications are presented, demonstrating the need for a review article to consider all IoT applications and those that have not been explored. The IoT applications and sub-applications are described, and their problem statement, literature to date and research gap are comprehensively evaluated. Finally, the open research challenges concerning reputation systems are reviewed and addressed to provide the researcher with a road map of potential research directions.

Blockchain technology has emerged as a potential solution to address the imperative need for enhancing security, transparency, and efficiency in the maritime industry, where increasing reliance on digital systems and data prevails. However, the integration of blockchain in the maritime sector is still an underexplored territory, necessitating a comprehensive investigation into its impact, challenges, and implementation strategies to harness its transformative potential effectively. This survey paper investigates the impact of Maritime Blockchain on Supply Chain Management, shedding light on its ability to enhance transparency, traceability, and overall efficiency in the complex realm of maritime logistics. Furthermore, the paper offers a practical roadmap for the integration of blockchain technology into the Maritime Industry, presenting a comprehensive framework that maritime stakeholders can adopt to unlock the advantages of blockchain in their operations. In addition to these aspects, the study conducts a thorough examination of the current network infrastructure in Ports and Vessels. This assessment provides a holistic view of the technological landscape within the maritime sector, which is crucial for understanding the challenges and opportunities for the successful implementation of blockchain technology. Moreover, the research identifies and analyzes specific Blockchain cybersecurity challenges that are pertinent to the Maritime Industry.

The Preponderance of literature has established that most of the metaheuristic algorithms were associated with identified challenges in solving the Controller Placement Problem in SD-WAN. This study proposed a Stochastic Computational Graph Model with an Ensemble Learning (SCGMEL) approach to address the scalability, intelligence, and high computational complexity challenges experienced by the existing metaheuristic algorithms. The proposed SCGMEL used stochastic gradient descent with momentum and learning rate decay, a computational graph model, and the eXtreme Gradient Boosted Trees (XGBoost) algorithm as the optimization and machine learning approaches. The proposed solution was tested using datasets from Internet Zoo topology with six objective functions: load balancing, maximum controller failure, average controller-to-controller latency, average switch-to-controller latency, and maximum controller-to-controller latency. The XGBoost outperformed other regression models, in predicting the number of controllers, with mean absolute error of 1.855751 versus 1.883536, 3.729863, and 3.829268 for the random forest, logistic regression, and K-nearest neighbor, respectively. Furthermore, the execution time, average and total CPU usages of the algorithms demonstrated the computational efficiency of the proposed SCGMEL over ANSGA-III, NSGA-II, and MOPSO with percentage decreases of 99.983%, 99.985%, and 99.446%, respectively. Consequently, the proposed SCGMEL was recommended for controller placement in SD-WAN, subject to the usage conditions.

The Internet of Things (IoT) can provide intelligent and effective solutions to various applications with higher accuracy that requires less or no human intervention. Smart Cities are one of the significant applications of the IoT comprising a collection of various services such as intelligent transportation, waste management, smart homes, etc. These heterogeneous services offer a wide range of collaborative applications in smart cities. A smart municipality in a smart city is a concept in which a digital municipal corporation is developed to provide comprehensive local government collaboration services based on digitization and automation aiming towards raising the living standards of citizens. Interoperability between heterogeneous services for collaborative tasks creates challenges for data security and privacy. Ensuring integrity and confidentiality of information is critical, and reliable data is essential to both the government and its citizens. In this paper, we proposed a service security architecture based on authentication and authorization for constrained environments during collaborative tasks for Software Defined Networking (SDN) and smart contract-enabled municipal smart cities. The proposed collaborative service security framework is being tested on the Multichain Blockchain networks. We present a novel method for using smart contracts in multichain blockchains for data security during collaborative tasks in smart city municipal architecture. The proposed security solution is based on the dynamism of smart contracts to govern and control all interactions and transactions securely between different heterogeneous IoT networks. We implemented a supportive use case for collaborative services in an SDN-enabled IoT architecture to evaluate the feasibility of the proposed service security architecture.

Abstract Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN.

WebRTC is a Google-developed project that allows users to communicate directly. It is an open-source tool supported by all major browsers. Since it does not require additional installation steps and provides ultra-low latency streaming, smart city and social network applications such as WhatsApp, Facebook Messenger, and Snapchat use it as the underlying technology on the client-side both on desktop browsers and mobile apps. While the open-source tool is deemed to be secure and despite years of research and security testing, there are still vulnerabilities in the real-time communication application programming interface (API). We show in this paper how eavesdropping can be enabled by exploiting weaknesses and loopholes found in official WebRTC specifications. We demonstrate through real-world implementation how an eavesdropper can intercept WebRTC video calls by installing a malicious code onto the WebRTC webserver. Furthermore, we identify and discuss several, easy to perform, ways to detect wiretapping. Our evaluation shows that several indicators within webrtc-internals API traces can be used to detect anomalous activities, without the need for network monitoring tools.

V2V messaging systems enable vehicles to exchange safety related information with each other and support road safety and traffic efficiency applications. The effectiveness of these applications depends on the correctness of the information reported in the V2V messages. Consequently, the possibility that malicious agents may send false information is a major concern. The physical features of a transmission are relatively difficult to fake, and one of the most effective ways to detect lying is to check for consistency of these features with vehicle position information in the message. In this paper, we propose a message consistency checking scheme whereby a vehicle acting independently can utilise the strength and variability of received signals to estimate the distance from a transmitting vehicle without prior knowledge of the environment (building density, traffic conditions, etc.). The distance estimate can then be used to check the correctness of the reported position. We show through simulation that our RMCSS method can detect false information with an accuracy of about 90% for separation distances less than 100m. We believe this is sufficient for the method to be a valuable adjunct to use of digital signatures to establish trust.

The exponential growth of wireless network technologies (e.g., 5th generation (5G) and beyond wireless communication) demand to provide a reliable backhaul to accommodate the ever-increasing use-cases, with better QoE beyond the current networks. The increasing demand for services from these use-cases is starting to drive new control signaling traffic, primarily due to a rapid increase in the number of devices, both individuals and machines. This constant connectivity demand tends to occupy significant Erlang capacity of the network. In this paper, we have proposed a dynamically reconfigurable cluster-based end-use aware control signaling optimization scheme to accommodate more user-specific data traffic. In this approach, it is proposed to exploit pre-clustering end-use analysis, usage specific clustering, and clustering based on end-use application and device-specific resource demand. In doing so, a dynamically reconfigurable QoE based slice performance bounds are considered for the user admission to the network. For the performance evaluation for the proposed signaling optimization and user admission framework, a set of comparative results have been attained and compared with the existing work. The achieved results suggest that the proposed signaling optimization and admission control scheme is superior in performance compared to the existing results. This is due to the attainment of reduced control signaling and a softer approach in user admission with the proposed framework.

Road safety applications such as intersection collision warning, emergency brake warnings, etc., rely on the periodic broadcast of messages by vehicles and roadside infrastructure. PKI-based approaches ensuring the integrity of messages and the legitimacy of the sender are computationally expensive and result in long messages. Approaches based on hashed key chains such as Timed Efficient Stream Loss-tolerant Authentication (TESLA) offer an alternative solution. Because they use symmetric-key cryptography, the messages are shorter and less expensive to verify. However, they bring their own challenges. This paper focuses on one challenge, the problem of distributing key chain commitments required for message verification. We propose and evaluate two techniques, respectively involving periodic broadcast of commitment keys by the vehicles themselves and selective unicasting by a central V2X Application Server (VAS). We find that the VAS-centric solution has advantages over the vehicle-centric solution and a related solution proposed by other researchers.

Network applications in SDN environment operate without any threat prevention mechanism or access control to checkmate what functions or operations they can execute within the network. This gives room for malicious applications to implement an offensive attack against the network or install exploits that can compromise the confidentiality, integrity or availability of network resources. In order to address this problem, this paper proposes a threat mitigation model based on trust that introduces a token-based authentication method that enables the controller to verify and validate every network application that makes changes in the network. The paper contributes in providing an authorisation method Boolean Access Matrix that constrains what operation, functions or privileges every network application can execute within the network. To assess the trustworthiness of network applications, a trust evaluation method based on Subjective Logic Reasoning which is a belief learning model is proposed. Results from tests and experiments show how scalable and efficient the proposed trust framework is.

Serving heterogeneous traffic demand requires efficient resource utilization to deliver the promises of 5G wireless network towards enhanced mobile broadband, massive machine type communication and ultra-reliable low-latency communication. In this paper, an integrated user application-specific demand characteristics as well as network characteristics evaluation based online slice allocation model for 5G wireless network is proposed. Such characteristics include, available bandwidth, power, quality of service demand, service priority, security sensitivity, network load, predictive load etc. A degree of intra-slice resource sharing elasticity has been considered based on their availability. The availability has been assessed based on the current availability as well as forecasted availability. On the basis of application characteristics, an admission control strategy has been proposed. An interactive AMF (Access and Mobility Function)- RAN (Radio Access Network) information exchange has been assumed. A cost function has been derived to quantify resource allocation decision metric that is valid for both static and dynamic nature of user and network characteristics. A dynamic intra-slice decision boundary estimation model has been proposed. A set of analytical comparative results have been attained in comparison to the results available in the literature. The results suggest the proposed resource allocation framework performance is superior to the existing results in the context of network utility, mean delay and network grade of service, while providing similar throughput. The superiority reported is due to soft nature of the decision metric while reconfiguring slice resource block-size and boundaries.