The Internet of Things (IoT) has become a revolutionary concept that connects various devices and systems to enable smooth communication and data exchange. In this vast network, unmanned aerial agents (UAAs)-assisted mobile edge computing (MEC) communication plays a crucial role in facilitating direct interaction between edge devices. This aspect of IoT goes beyond traditional interactions between humans and machines. It creates a dynamic environment where devices collaborate autonomously, share information, and perform tasks. UAA-assisted MEC network offers several benefits, such as supports short range communication, reduced delay, improved scalability, and enhanced energy efficiency. Furthermore, for the purpose of enhancing the widespread interconnection and exceptionally dependable minimal delay in the fifth generation (5G) and beyond network, the utilization of nonorthogonal multiple access (NOMA) can be considered. Within this context, the impact of federated learning (FL) on NOMA-based UAV-assisted MEC network in wirelesspowered communication networks is examined. Initially, the transmitters extract energy from the radio frequency signals emitted by the MEC server. Subsequently, the transmitters utilize NOMA to establish communication with the receivers by utilizing the stored harvested energy. The formulation of a stochastic optimization problem is proposed with the aim of improving energy consumption (EC) and minimizing delay. Results indicate that the proposed scheme exhibit superior accuracy compared to baseline schemes, achieving an accuracy 98.37% after 59 communication rounds. The FL is employed to attain the objective and accelerate the local training data across the UAA-assisted MEC network.

Electric vehicle (EV) charging stations (CSs) are increasingly prevalent due to the growing adoption of renewable energy. Solar CSs’ main difficulties are energy efficiency, security, traceability, and sustainability. This article presents a novel blockchain-enabled EV charging framework that addresses these challenges using the Ethereum virtual machine (EVM), the Metamask wallet, and smart contracts (SCs). This article introduces solarcoins, a digital currency for trading solar energy, which reduces human intervention while fostering trust, transparency, and privacy among EV users. The proposed solution ensures secure communication between CS operators and EV users, enhancing both security and traceability. The proposed solution ensures secure communication between CS operators and EV users, enhancing both security and traceability. To quantify the sustainability and efficiency of the proposed system, the framework performances are tested and evaluated by varying numbers and types (Read, Write, and Transfer) of transactions using Hyperledger caliper and Go Ethereum. The overhaul Performance metrics were measured under varied transaction rates and control parameters by varying the number of validator nodes (1 node to 5 nodes), such as transaction latency, throughput, resource utilization, and so on. The performance of three major functions—open, query, and transfer—was recorded and analyzed. The results show that the query transaction is faster than open and transfer and the latency increases linearly with increased transaction rate. At 1000 transaction per second, the open function has a latency of 260.22 s, whereas the query function has a latency of 104.12 s and the transfer function has a latency of 345.73 s. The average memory usage for 1node-clique is 1224.0 MB, while it is 76.8 MB for 5node-clique. Results reveal that with an increase in the number of cliques (Validator CSs), memory utilization decreased linearly. This happens because all framework transactions are distributed across each EV CS network. The SCs deployment and operational costs were measured. Complexity analysis reveals that functions, such as getStation, getUser, getStationState, etc., exhibit constant time complexity O(1), while the registerUser and addStation functions have linear space and time complexity O(n).

Uncrewed aerial vehicles (UAVs) have made a substantial contribution to vehicle communications in recent times, and they provide viable ways to improve connection in contemporary transportation networks. However, maintaining consistent signal coverage, the limited computation capacity of UAVs, and getting past obstructions to maintain direct communication with vehicles is still tedious. To address the same, In this paper, an edge-enabled digital twin (DT) of UAV with an intelligent reflecting surface (IRS)-aided vehicular network is investigated. We specifically concentrate on the issue of minimizing the net energy consumption of the system in task-offloading while simultaneously optimizing IRS phase-shift, power allocation and task-offloading parameters through the use of DT architecture. We first describe the specified non-convex optimization issue as a Markov decision process (MDP) to address it. Eventually, we propose a hybrid federated learning (HFL) algorithm that aims to maximize energy efficiency (EE) by optimising related parameters. This method also enhances the system’s overall performance by lowering energy consumption and using the combined experiences of several agents. Compared to the benchmark schemes, HFL proves to be 20.5% and 47.6% more efficient than MAD2PG and DQN respectively. Simulation results affirm that the suggested method outperforms the benchmark techniques in terms of EE and learning accuracy.

Unmanned aerial vehicles (UAVs) in beyond 5G (B5G) are crucial for revolutionizing various industries, including surveillance, agriculture, and logistics, by enabling high-speed data transfer, ultra-low latency communication, and ultra-reliable connectivity. However, integrating UAVs into B5G networks poses various security and privacy concerns. These risks encompass the possibility of unauthorized access, breaches of data, and cyber-physical attacks which jeopardize the integrity, confidentiality, and availability of UAV operations. Moreover, UAVs in B5G networks are also at high risk from the application of machine learning (ML)-based attacks by exploiting vulnerabilities in ML models, leading to adversarial manipulation, data poisoning and model evasion techniques, which can compromise the integrity of UAV operations, lead to navigation errors, and expose sensitive data collected by UAVs. Considering the aforementioned security and privacy concerns, this review article presents emerging security and privacy solutions for UAVs in B5G networks. Firstly, We introduce the essential background of integrating UAVs into B5G networks and discuss the advantages and security challenges which the emerging integrated network architecture have. Then, we proceed to analyze and examine the security and privacy landscape by including threats and requirements of UAVs in B5G networks. Based on these threats and requirements, solutions from physical layer security (PLS), blockchain (BC), federated learning (FL) and post-quantum cryptography (PQC) are discussed and explored in details. Moreover, potential future research directions are discussed in details as open research issues.

The vehicle-road cooperation system (VRCS) facilitates vehicle-to-vehicle (V2V) communication for future vehicle usage in sixth generation (6G) networks. The implementation of the 6G network has made it possible for V2V communication to enhance network density, optimize transmission mode selection, and offer connectivity between vehicles while guaranteeing Quality of Service (QoS). However, there are inherent challenges, such as limited bandwidth, diverse QoS requirements, interference, and power constraints, associated with resource allocation and mode selection in V2V and vehicle-to-everything (V2X) communication. In this article, we jointly optimized the mode selection and resource allocation problems in VRCS by using simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS). The proposed model utilizes quantum federated reinforcement-learning (QFRL)-based augmented intelligence algorithms within the STAR-RIS VRCS framework. The proposed QFRL algorithm is a promising solution for advanced decision making, automation to improve traffic flow, reduces traffic congestion, and improve safety in the STAR-RIS assisted VRCS. Additionally, by leveraging the unique processing advantage of quantum computing will make the VRCS more capable of handling the enormous amount of real-time data that IoT devices send, which is necessary for the intelligent services it offers. The proposed model QFRL-based STAR-RIS assisted VRCS approach maximizes vehicle-to-infrastructure (V2I) user capacity while meeting the reliability requirement of V2V pairs. Finally, the simulation results prove the superiority of the QFRL algorithm against baseline schemes like quantum federated learning (QFL), federated reinforcement learning (FRL), and federated learning (FL) algorithms for V2V pairs. Furthermore, the performance evaluation findings indicate that the proposed STAR-RIS assisted QFRL algorithm performs 20.5%, 32.2%, and 46.7% better than QFL, FRL, and FL.

Mobile authentication is an essential service to ensure the security of engaging parties in a ubiquitous wireless network environment. Several solutions have been proposed mainly based on both centralised and distributed authentication models to allow ubiquitous mobile access authentication; however, limitations still exist in these approaches, namely flexibility, security and performance issues and vulnerabilities. These shortcomings are influenced by the resource limitations of both wireless networks and the mobile devices together with inter-technology and inter-provider challenges. In this paper, the authors reviewed the major techniques in the field of ubiquitous mobile access authentication, which has attracted many researchers in the past decade. After investigating existing mobile authentication models and approaches, the common challenges are summarised to serve as the solution key requirements. The identified key solution requirements allow analysing and evaluating mobile authentication approaches.

Mobile Internet access has significantly increased over the past few years. Roaming into foreign networks becomes necessary in order to access mobile-services. Traditionally, this done based on roaming agreement between the home network and the foreign network to extend trust for mobile users. However, secured mobile authentication is a challenge especially in two-party authentication environment where only the visited user and the foreign network get involved. This paper proposes two-party secure roaming protocols based on direct negotiation with potential foreign networks regarding quality of service, pricing and other billing related features, in order to establish service agreement and get the authorization token. The paper provides better performance to check the revocation status of the mobile user using recency evidence compare to the previously used revocation list. Identity Based Signature is used to stop impersonation attack. The security and performance analysis indicates that our protocol is resistant to well-known attacks, and it ensures efficient roaming.

The inherent limitations of mobile devices (MD) increase the gap between security and performance, and this gap increases with the growing heterogeneity of computing environments. As we are moving to the 4G network and Mobile Internet, there will be a need to deliver an intelligence tradeoff between security and performance. The main aim of this paper is to identify challenges in MD research and to propose practical solution to maintain the balance between efficiency and protection to secure mobile communication. We propose a Dynamic Key Size (DKS) architecture which can be integrated into security protocols to provide an efficient and secure mobile communication. Applications are provided with an interface for selectively securing information at different levels of protection. Our architecture makes use of both the information sensitivity and MD capabilities performance levels classifications in making a decision for suitable algorithm key length. As a possible application, we outline the integration of DKS over SSL protocol to demonstrate the flexibility features that improves the protocol security performance.

Flooding is one type of Denial of Service (DoS) attacks which can cause significant financial losses. This paper presents a new security approach which prevents flooding in the government critical systems and focuses in preventing flooding in non-authenticated client area. A new cooperation with local service providers has been suggested to make the prevention of flooding attacks easier. In addition, dynamic key encryption technique is adapted as a part of the proposed approach to enhance its functionality.

The development in mobile devices and wireless technologies opens up unlimited choices of mobile services such as mobile commerce. These advances make access services available and convenient everywhere at any time. Since mobile users usually move, accessing services becomes unavailable especially in some locations that are not covered by their home networks. Therefore, it becomes necessary to roam into foreign networks in order to access such services. However, authenticating visiting users by a foreign network results in some security concerns. This challenge lies in the fact that a foreign network provider does not initially have the authentication credentials of the mobile users. The existing approaches are either roaming agreement-based in exchanging authentication information between the home network and a foreign network or vulnerable to some security attacks. This paper proposes a roaming agreement-less approach based on our ubiquitous mobile access model. This approach consists of two tokens: Passport (identification token) and Visa (authorisation token) to provide a flexible authentication method for foreign network to authenticate mobile users. The security analysis indicates that our proposal is more secure and suitable for ubiquitous mobile communications specially in roaming agreement-less environment.