The rapid development of 5G and the upcoming transition to Beyond 5G and 6G networks are accelerating the emergence of the interactive metaverse. Digital twin (DT) is a cornerstone of the interactive metaverse, facilitating real-time dynamic interactions. Task-offloading to edge servers enables metaverse applications to mitigate the resource limitations of mobile devices. Most current research prioritizes latency and energy efficiency over security in offloading systems, neglecting the trustworthiness of offloading actors, necessitating improved security measures. This paper investigates a trustworthy computation offloading system for the DT-empowered interactive metaverse. We propose a DT-assisted task-offloading architecture and a DRL-based solution to optimize task-offloading decisions. Digital twins collect and update real-time data providing the DRL agent with up-to-date view of the system's status. This agent uses the data to make informed task-offloading decisions, enhancing efficiency and decision-making. A bidirectional blockchain-based trust management mechanism ensures secure interactions between mobile devices and edge servers, which is crucial for effective task-offloading and resource management. The performance evaluation of the DRL-based trustworthy offloading system demonstrates its efficient ability to facilitate real-time, reliable offloading decisions.

This paper tackles the pressing concern of establishing trust within the context of Mobile Edge Computing (MEC) to enhance security in task-offloading interactions. MEC’s potential to optimize service performance is contingent on seamless and secure interactions among its diverse entities, comprising mobile terminals and edge servers. To bridge this trust gap, we propose a robust trust management system that exploits the capabilities of blockchain technology. This system encompasses a trust management mechanism founded on a reputation metric, serving as the cornerstone for engendering trust in MEC transactions. In addition, the proposed solution is completed by an incentive approach embedded within game theory, meticulously designed to augment block mining efficiency. We adopt a Stackelberg game to model the intricate dynamics of interaction within this context, aptly capturing the verification incentive problem. This game orchestrates the actions of a leader (edge server) and subsequent followers (mobile terminals), leveraging a trustworthy offloading strategy formulation that culminates in optimal block validation decision-making. Our proposal guarantees the reliability of the offloading process and establishes a favorable relationship of confidence between the offloading system entities.

Network slicing security in 5G and beyond 5G (B5G) networks is critical due to the wide range of supported services and applications. Existing literature focuses on reactive AI-based security that can detect and respond to threats after occurrence. In contrast, proactive security solutions, such as moving target defense (MTD), possess great promise. MTD involves constantly altering system configurations to increase uncertainty for attackers. Despite its potential, existing work that incorporates MTD often overlooks the intricate balance between enhancing security and maintaining network operational effi-ciency. This work proposes a novel approach to integrating Deep Reinforcement Learning (DRL) with MTD for network slicing security, our approach creates a moving target by dynamically reconfiguring IP addresses, complicating reconnaissance efforts, and thwarting potential attacks. Experimental results show that our solution achieves approximately 98 % effectiveness against Distributed Denial of Service (DDoS) attacks, demonstrating its efficacy in proactively mitigating threats.

Nowadays, there is significant growth in mobile data traffic, mostly due to the explosion in demand for high-bandwidth applications. This increased traffic volume threatens to saturate the backhaul links between the access network and the core network. Edge caching has been proposed as an innovative solution to this problem, by moving popular content near the end-users to reduce access/ downloading delays and relieve backhaul pressure. Most existing caching solutions propose to store the popular contents in edge caches deployed on cellular base stations. In this paper, we study an incentive caching policy in the context of the Internet of vehicles. To this end, we consider a market consisting of several content providers (CPs) that own a set of popular files and wish to bring them closer to their end-users. Besides, at several Public Transportation Companies (PTCs) that own embedded caches on their mobile vehicles. The PTCs offer to lease their on-board cache storage space to the CPs to monetize the caches and earn monetary profit. In our caching policy, CPs and PTCs interact and cooperate to improve the performance of the caching process. We formulated this strategic interaction using a Stackelberg game with multiple leaders (PTCs) and multiple followers (CPs) and derive optimal strategies. To validate and prove the effectiveness of the proposed model, several simulations have been performed. The obtained results showed that our Stackelberg game-based model allows CPs and PTCs to reach optimal utilities in a more efficient caching process.

Fast advances in wireless technologies and the automotive industry have propelled the proliferation of vehicular ad hoc networks (VANETs). These latter aim to increase road safety and reduce traffic accidents, as well as to provide drivers and passengers with entertainment features. Although, the amalgamated VANETs applications, still face challenging requirements to achieve reliable communication between vehicles or/and vehicles and infrastructure. This is mainly due to their high mobility. Hence, the location information of the vehicles quickly becomes outdated and false. Therefore efficient mechanisms are needed to keep it up to date. The simplest solution consists of increasing the frequency of Hello messages exchanged between vehicles. Unfortunately, this will largely occupy the communication channel and, consequently, may lead to a lot of collisions. In this paper, we propose a novel Extended Kalman Filter Greedy Perimeter Stateless Routing protocol (EKF-GPSR), to address this issue. EKF-GPSR uses a stochastic prediction model based on an EKF to obtain the user information when transmitting data instead of using information from the last beacon exchanged messages which is most likely outdated. The simulation results in Omnet++ and Sumo showed that our proposed protocol EKF-GPSR outperforms the GPSR protocol in terms of packet delivery rate, delay, and throughput.

Software-defined networking (SDN) is considered as one of the main enabler technologies of 5G that is expected to propel the penetration of vehicular networks. The rapid development of wireless technology has generated an avalanche demand for bandwidth-intensive applications (e.g., video-on-demand, streaming video, etc.) causing an exponential increase in mobile data traffic. Moreover, the use of edge caching technique enhances network resource utilization and reduce backhaul traffic. Many incentive mechanisms have been developed to encourage caching actors to enhance the caching process. In this paper, we propose an SDN based incentive caching mechanism for a 5G-enabled vehicular network. Our caching strategy consists of a small base station (SBS) that encourages mobile vehicles equipped with embarked caches to store and share its popular contents using vehicle to vehicle (V2V) communication. SBS aims to offload the cellular core links and reduce traffic congestion, where cache-enabled vehicles compete to earn more SBS reward. The interaction between the SBS and the cache-enabled vehicles is formulated using a Stackelberg game with a non-cooperative sub-game to model the conflict between cache-enabled vehicles. The SBS acts first as a leader by announcing the number of popular contents that it wants to cache and the cache-enabled vehicles respond after by the optimal number of contents they accept to cache and the corresponding caching price. Two optimization problems are investigated and the Stackelberg equilibrium is derived. The simulation results demonstrated the efficiency of our game theoretical based incentive V2V caching strategy.

Small drones are currently emerging as versatile nascent technology that can be used in exploration and surveillance missions. However, most of the underlying applications require very often complex and time-consuming calculations. Although, the limited resources available onboard the small drones, their mobility, the computation delays and energy consumption make the operation of these applications very challenging. Nevertheless, computation-offloading solutions provide feasible resolves to mitigate the issues facing these constrained devices. In this context, we address in this paper the problem of offloading highly intensive computation tasks, performed by a fleet of small drones, in order to improve the energy overhead and decrease the execution delay. We adopt a theoretical methodology based on a sequential game where three different types of players (drone, base station and edge server) carry out the heavy computation tasks. Compared to literature, as far as we know, we are the first to consider a computation- offloading problem with three different devices. Each player has a set of possible strategies, depending on the previous actions that the other players might undertake in a sequential game. Furthermore, we prove the existence of a Nash Equilibrium and design an offloading algorithm that converges to this optimal point. Extensive simulations gave promising results where the sequential game based model outperforms comparable approaches in terms of global utility, which pledges the best possible tradeoff between energy consumption and achievable delay.