关键词： Autonomous aerial vehicles   Trajectory   Task analysis   Resource management   Delays   Satellites   Servers   LEO satellite communications   UAV trajectory   mobile edge computing   cloud computing   non-convex mixed integer non-linear programming  

摘要： In this article, we study the computation offloading problem in hybrid edge-cloud based space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, user scheduling, edge-cloud computation, radio resource allocation, and admission control is performed. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by ground users (GUs) via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve four sub-problems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and multi-UAV trajectory control until convergence. Moreover, feasibility verification and admission control strategies are proposed to handle overloaded network scenarios. Furthermore, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex computation resource and bandwidth allocation and UAV trajectory control sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines.

关键词： Mobile edge computing   Path planning   Resource allocation   Task offloading   Unmanned aerial vehicles   User equipments  

摘要： As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption.

关键词： Task analysis   Servers   Reinforcement learning   Mobile handsets   Cloud computing   Performance evaluation   Energy consumption   Mobile edge computing   multiagent reinforcement learning   offloading strategy   wireless communication   internet of vehicles  

摘要： The emergence of the Internet of Vehicles (IoV) introduces challenges regarding computation-intensive and time-sensitive related services for data processing and communication. Limited resource availability increases the processing latency and may cause application interruption due to the mobility of vehicles. To address the real-time requirements of users and tasks, mobile edge computing (MEC), in which data are processed at the network edge, has been proposed to collaborate with the cloud to provide better performance. However, the offloading strategies proposed previously have some shortcomings in addressing issues such as task dependency and resource competition. In this article, we propose a novel offloading strategy for MEC, Com-DDPG, in which multiagent reinforcement learning is used to enhance the offloading performance. Within the IoV transmission radius, multiple agents work together to learn the changes in the environment, such as the number of mobile devices and the queue of tasks, and take appropriate action in the form of a strategy for offloading to an edge server. First, we discuss models of task dependency, task priority, and resource consumption from the perspective of server clusters and multiple dependencies among tasks. In the proposed method, the communication behavior among multiple agents is formulated;then, the policy determined through reinforcement learning is executed as an offloading strategy to obtain the corresponding results. Second, to enhance the communication of information among multiple agents, a long short-term memory (LSTM) network is employed as an internal state predictor to provide a more complete environmental state, and a bidirectional recurrent neural network (BRNN) is used to learn and enhance the features obtained from the agents' communication. Finally, experiments carried out based on the Alibaba Cluster Dataset are presented. The results show that our method is superior to baseline methods in terms of energy consumpt

关键词： Fog Computing   Mobile Edge Computing   Mobile Cloud Computing  

摘要： Today, cloud computation offloading may not be an appropriate solution for delay-sensitive applications due to the long distance between end-devices and remote datacenters. In addition, offloading to a remote cloud can consume bandwidth and dramatically increase costs. However, end-devices such as sensors, cameras, and smartphones have limited computing and storage capacity. Processing tasks on such battery-powered and energy-constrained devices becomes even more complex. To address these challenges, a new paradigm called Edge Computing (EC) emerged nearly a decade ago to bring computing resources closer to end-devices. Here, edge servers located between the end-device and the remote cloud perform user tasks. Recently, several new computing paradigms such as Mobile Edge Computing (MEC) and Fog Computing (FC) have emerged to complement Cloud Computing (CC) and EC. Although these paradigms are heterogeneous, they can further reduce energy consumption and task response time, especially for delay-sensitive applications. Computation offloading is a multi-objective, NP-hard optimization problem. A significant part of previous research in this field is devoted to Machine Learning (ML) methods. One of the essential types of ML is Reinforcement Learning (RL), in which an agent learns how to make the best decision using the experiences gained from the environment. This article provides a systematic review of the widely used RL approaches in computation offloading. It covers research in complementary paradigms such as mobile cloud computing, edge computing, fog computing, and the Internet of Things. We explain the reasons for using various RL methods in computation offloading from a technical point of view. This analysis includes both binary offloading and partial offloading techniques. For each method, the essential elements of RL and the characteristics of the environment are discussed regarding the most important criteria. Research challenges and Future trends are also ment

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： Internet of vehicles   mobile edge computing   blockchain   double deep Q-networks   task offloading  

摘要： The Internet of Vehicles (IoV) represents a paradigm shift in vehicular communication, aiming to enhance traffic efficiency, safety, and the driving experience by leveraging interconnected vehicles. Despite its promise, the IoV faces challenges such as efficient task offloading, energy management, and data security. Mobile Edge Computing (MEC) emerges as a solution to some of these challenges by bringing computational resources closer to the vehicular network's edge, yet it raises critical concerns regarding resource management, service continuity, and scalability in dynamic vehicular environments. Addressing both IoV and MEC challenges necessitates robust and dynamic optimization mechanisms. In response to these challenges, our study introduces a multi-objective approach using Double Deep Q-Networks (DDQN). This algorithm combines the strengths of Deep Neural Networks (DNNs) and Deep Learning (DL) techniques, enabling dynamic decision-making that can adapt to changing conditions. By considering multiple objectives, the DDQN algorithm allows for a sophisticated trade-off analysis, efficiently balancing between the different objectives to optimize overall system performance. Through the use of Blockchain technology, known for its secure, decentralized structure, our model enhances the integrity of data, providing a reliable and efficient solution for IoV-MEC systems. We conducted a comparative analysis of our model against the standard Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms, which are prevalent in this field. Our model demonstrated significant improvements over these traditional methods: energy consumption was reduced by 26.4%, latency decreased by 6.87%, and the cost was minimized by 7.41%.

关键词： Sensors   Deep learning   Roads   Computational modeling   Cloud computing   Training   Servers   Multi-access edge computing   Internet of Vehicles   mobile edge computing   the Internet of Vehicles  

摘要： Recently, the internet of vehicles (IoVs), mobile edge computing (MEC), and deep learning have attracted many research attentions in the applications of autonomous driving. MEC can help to reduce the network load and transmission delay by offloading the computing tasks to the powerful edge servers while deep learning can effectively improve the accuracy of obstacle detection to enhance the stability and safety of automatic driving. In this article, we first present a comprehensive overview of distributed deep learning based on edge computing over IoVs. Then, the related key techniques, including the distributed characteristics of IoVs, mobile edge collaborative computing architecture for high quality of service (QoS) requirements in terms of vehicle transmission delay and energy consumption, distributed deep learning, and its applications for vehicular networking, are discussed. Finally, the article identifies several important open challenges, opportunities, and potential research directions to provide a reference for readers in this field.

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： Computation offloading   Genetic algorithm   Mobile edge computing   Particle swarm optimization  

摘要： In recent years, edge computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited devices. The main feature of edge computing is to push computation, networking, and storage facilities closer to the network edge. This enables user equipment (UE) to profit from the edge computing paradigm by mainly offloading their intensive computation tasks to edge resources. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behaviour of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in Metaheuristic-based computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the Metaheuristic-based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: genetic algorithm (GA)-based mechanisms, Partical Swarm Optimization (PSO)-based mechanisms, and Hybrid GA with PSO-based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded. © 2024, Semarak Ilmu Publishing. All rights reserved.

关键词： clustering   Internet of Vehicles   machine learning   mobile edge computing   multi-hierarchical caching   Thompson sampling learning  

摘要： This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments. © 2024 National Institute of Telecommunications. All rights reserved.