关键词： Authentication   Handover   Servers   Security   Computational modeling   Blockchains   Protocols   Social networking (online)   Multi-access edge computing   Heuristic algorithms   Mobile edge computing   handover authentication   social relationship   heuristic algorithm  

摘要： Handover authentication in mobile scenarios refers to the process of re-authentication when a mobile device switches from one edge server to another. However, existing handover authentication schemes fail to optimize handover latency, security, and energy consumption comprehensively. To ensure continuity, security, and lightweight operation during switching, we propose SREHA, an efficient handover authentication scheme based on social relationships. First, the edge server builds its own social circle through bidirectional identity and integrity authentication, saving the social relationships in the public blockchain. Second, when the mobile device performs handover authentication, it evaluates the trustworthiness of the target node based on the social relationships of the current edge server node and filters the nodes that satisfy the trustworthiness criteria. By transferring the authentication load to the server side, SREHA prevents the device from expending significant resources to maintain reputation and authentication information, thereby achieving a lightweight handover authentication process. Third, we propose a malicious node detection model based on heuristic algorithms, which enhances the security of the handover authentication-free framework. Lastly, we implement a prototype of the proposed scheme and validate its security using the Scyther formal validation tool. The results demonstrate that our scheme effectively reduces the time delay of the handover authentication process, and exhibits lower computational overhead and higher security compared to existing schemes.

关键词： Autonomous aerial vehicles   Optimization   Resource management   Task analysis   6G mobile communication   Wireless networks   Base stations   Edge computing   Reconfigurable intelligent surfaces   Computation offloading   mobile edge computing   reconfigurable intelligent surfaces   resource allocation   unmanned aerial vehicle  

摘要： The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.

关键词： LEO satellite communication   Lyapunov optimization   Mobile edge computing   Satellite edge computing  

摘要： Despite the rapid growth of the Internet industry, the provision of full Internet service to remote regions is still challenging. As a solution, the combination of Low Earth Orbit (LEO) satellite communication and Mobile Edge Computing (MEC) is gaining attention. However, considering the high speed of LEO satellites in network environments remains a significant challenge. To this end, this paper introduces a dynamic computation offloading and resource allocation framework in the LEO satellite MEC architecture. Using Lyapunov optimization, we propose an efficient DCOOL algorithm to minimize average power consumption and propagation delay constrained by queue stability. Finally, comparative analysis and simulations demonstrate the superior performance of DCOOL while achieving lower power consumption and stable workload processing. © 2024 The Authors

关键词： Autonomous aerial vehicles   Multi-access edge computing   Edge computing   Reconfigurable intelligent surfaces   Resource management   Trajectory optimization   Urban areas   Ground support   Energy management   Energy consumption   Lyapunov methods   Lagrangian functions   Stability analysis   Simulation   Unmanned aerial vehicle (UAV) communication   mobile edge computing   reconfigurable intelligent surface   trajectory optimization   resource allocation  

摘要： In urban settings, the highly complex communication environment leads to a frequent occurrence blockage in the link between ground users and unmanned aerial vehicle (UAV), which renders the quality of communication poor. To address this issue, an UAV edge computing system based on reconfigurable intelligent surface (RIS) is proposed in this paper. The goal of this system is to minimize energy consumption by considering various factors such as user transmission power, intelligent reflector phase shift matrix, UAV trajectory, computational resource allocation and the stability of task queues. By applying the Lyapunov optimization theory, the original non-convex problem is decomposed into a series of optimization-related sub-problems. Additionally, it is possible to obtain the closed solution to the original problem through Lagrange dual, Karush-Kuhn-Tucker (KKT) conditions, and mixed differential genetic evolution algorithm (MDGEA). As indicated by the simulation results, the proposed RIS-assisted UAV edge computing system is more effective in reducing energy consumption for the system than traditional UAV mobile edge computing on the basis of maintaining queue stability.

关键词： Mobile edge computing   multi-user collaboration   wireless power transfer   time and data allocation   deep learning  

摘要： The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. This study considers a wireless-powered mobile edge computing system that includes a hybrid access point (HAP) equipped with a computing unit and multiple Internet of Things (IoT) devices. In particular, we propose a novel multi-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Each collaborative cluster comprises a source device (SD) and an auxiliary device (AD), where the SD can partition the computation task into various segments for local processing, offloading to the HAP, and remote execution by the AD with the assistance of the HAP. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network. This involves jointly optimizing collaboration, time and data allocation among multiple IoT devices and the HAP, while considering the energy causality property and the minimum data processing requirement of each device. Initially, an optimization algorithm based on the interior-point method is designed for time and data allocation. Subsequently, a priority-based iterative algorithm is developed to search for a near-optimal solution to the multi-user collaboration scheme. Finally, a deep learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.

关键词： Mobile edge computing  

摘要： A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing  

摘要： The development of the internet of things (IoT) and 6G has given rise to numerous computation-intensive and latency-sensitive applications, which can be represented as directed acyclic graphs (DAGs). However, achieving these applications poses a huge challenge for user equipment (UE) that are constrained in computational power and battery capacity. In this paper, considering different requirements in various task scenarios, we aim to optimize the execution latency and energy consumption of the entire mobile edge computing (MEC) system. The system consists of single UE and multiple heterogeneous MEC servers to improve the execution efficiency of a DAG application. In addition, the execution reliability of a DAG application is viewed as a constraint. Based on the strong search capability and Pareto optimality theory of the cuckoo search (CS) algorithm and our previously proposed improved multiobjective cuckoo search (IMOCS) algorithm, we improve the initialization process and the update strategy of the external archive, and propose a reliability-constrained multiobjective cuckoo search (RCMOCS) algorithm. According to the simulation results, our proposed RCMOCS algorithm is able to obtain better Pareto frontiers and achieve satisfactory performance while ensuring execution reliability. © 2024 Liangbin Zhu et al.

关键词： Task analysis   NOMA   Uplink   Servers   Relays   Hardware   Multi-access edge computing   Power system reliability   Mobile edge computing   offloading delay   energy consumption   non-orthogonal multiple access   transceiver hardware impairments   ergodic sum capacity   outage probability   outage probability  

摘要： Recently, mobile edge computing (MEC) has been proposed to improve wireless devices' computational capabilities by offloading computation-intensive tasks to a network-edge server. Analytical research indicates that the application of non-orthogonal multiple access (NOMA) can significantly reduce the latency and energy consumption of MEC offloading. In order to capture the potential gains of NOMA in the context of MEC, this paper proposes an edge computing-aware coordinated direct and relay transmission (CDRT) based NOMA technique which can enjoy the benefits of uplink NOMA in reducing MEC users' uplink energy consumption and computational delay. By lowering the likelihood of uplink outages for MEC users, the NOMA-assisted MEC system can benefit from uplink NOMA's advantages. In addition, CDRT-based NOMA has the sum capacity scaling of log rho(b) as signal-to-noise-ratio rho(b) increases, but 1/2 log rho(b) for non-CDRT-based NOMA. Hence, applying CDRT-based NOMA in MEC increases the computation capability and decreases communication outage probability by extending the cell coverage of the MEC users. Based on this idea and the superior performance requirements in future wireless networks, we derive new closed-form expressions for the exact ergodic sum capacity and offloading outage probability under both residual hardware impairment and perfect/imperfect successive interference cancellation. Furthermore, to set a benchmark, accurate analytical expressions and numerical results are provided to demonstrate the effectiveness of the NOMA-assisted MEC system over the orthogonal multiple access-based scheme. At the end, we provide the basic guidelines for choosing transceiver hardware that will meet the practical requirements of a CDRT-based uplink-NOMA transmission system for efficient offloading, where the performance of the MEC server is evaluated in terms of latency and energy consumption. The Monte Carlo simulations validate the accuracy of the derived analytical expr

关键词： Mobile edge computing  

摘要： Vehicular Edge Computing (VEC) has garnered substantial attention owing to its capacity to provide ample computational resources for computation-intensive tasks. However, how to flexibly allocate computing tasks within vehicles and efficiently manage the resources consumed by tasks has emerged as a challenge. To tackle this issue, this research advances the proposition of employing an auxiliary vehicle (AV) for task offloading and introduces a novel Auxiliary Vehicle Algorithm (AVA). AVA integrates both federated learning and multi-agent reinforcement learning to fully utilize computing resources in the vehicular environment, and simultaneously achieves task delay reduction, energy consumption minimization, and task completion rate augmentation. Moreover, we establish a federated learning framework to judiciously determine the proportion of resource allocation of AV through the implementation of inventive mechanisms. Experiment results validate that our approach not only leads to the improvement of key system performance indicators, but also ensures the comprehensive exploitation of the computing resources of mobile vehicles. © 1967-2012 IEEE.

关键词： Autonomous aerial vehicles   Servers   Signal to noise ratio   Computational modeling   Real-time systems   Internet of Things   Resource management   Processor scheduling   Data models   Trajectory planning   Mobile edge computing   UAV   deep deterministic policy gradient   task offloading   trajectory planning  

摘要： The proliferation of Internet of Things (IoT) devices has resulted in a massive increase in data generation, necessitating robust solutions for real-time data processing and analysis. The integration of Unmanned Aerial Vehicles (UAVs) with MEC systems presents a promising enhancement, providing dynamic, mobile edge computing capabilities that can adapt to changing conditions and demands. However, efficiently managing the task offloading and trajectory planning of UAVs in such scenarios poses significant challenges, particularly in maximizing coverage while minimizing time and energy consumption. In this context, this paper proposes a novel UAV-based MEC model utilizing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm to optimize task scheduling and UAV trajectory in real-time. Our model enables UAVs to function as mobile edge servers, dynamically processing and routing tasks between terminal devices and edge servers based on real-time device demands. By incorporating a sophisticated reward function that prioritizes the minimization of system costs-including energy, time, and throughput maximization-our approach not only enhances the operational efficiency of UAV-assisted MEC systems but also improves the quality and responsiveness of edge computing services. After training the MADDPG model in the simulated environment, the experiments show that our approach significantly improved the performance of UAV-assisted MEC systems in real-time scenarios, converging after 50 learning episodes and achieving 90% task completion rate.