关键词： UAV communication   mobile edge computing   delay   UAV altitude   task allocation  

摘要： Unmanned aerial vehicles (UAVs) are widely used in wireless communication systems due to their flexible mobility and high maneuverability. The combination of UAVs and mobile edge computing (MEC) is regarded as a promising technology to provide high-quality computing services for latency-sensitive applications. In this paper, a novel UAV-assisted MEC uplink maritime communication system is proposed, where an MEC server is equipped on UAV to provide flexible assistance to maritime user. In particular, the task of user can be divided into two parts: one portion is offloaded to UAV and the remaining portion is offloaded to onshore base station for computing. We formulate an optimization problem to minimize the total system latency by designing the optimal flying altitude of UAV and the optimal task allocation ratio. We derive a semi closed-form expression of the optimal flying altitude of UAV and a closed-form expression of the optimal task allocation ratio. Simulation results demonstrate the precision of the theoretical analyses and show some interesting insights.

关键词： Mobile edge computing   Resource allocation   Artificial potential field   La-tency  

摘要： Mobile edge computing (MEC) is a promising technology for improving la-tency in mainly real-time applications, such as smart airports, smart maintenances, and AR/VR. And it is an effective technology that helps local servers or the cloud to re-lease the network overload at a rush time. However, an alternative network environment and mobility-agnostic trajectory brings new challenges for minimizing communication latency. To solve these challenges, we present a resource allocation scheme based on an artificial potential field (RAAPF), which provides an adaptable resource allocation in the tradeoff between distance and node capacity conditions. Futhermore, the potential energy of RAAPF is transformed into a knapsack problem in the MEC to optimize the latency, and a heuristic algorithm is applied to finding the optimal solution. The simu-lation results show that the latency of our method is reduced by 25% compared to that of mobility-agnostic online resource allocation within a few node numbers;the latency gap between the two methods increases significantly as the number of nodes increases, and our scheme can always achieve a lower latency performance under different scenes.

关键词： Radar   Sensors   Interference   Information rates   Estimation   Signal to noise ratio   Radar cross-sections   Integrated sensing and communication   mobile edge computing   intelligent reflecting surface  

摘要： In this letter, we propose an integrated sensing and communication (ISAC) assisted energy-efficient mobile edge computing (MEC). To address the performance degradation due to interference between the radar sensing and MEC, we leverage advanced intelligent reflecting surface (IRS) to improve both the performance of the radar sensing and MEC. We adopt the radar estimation information rate as a performance measure of the radar sensing and further characterize the mutual influence between computation offloading transmission in MEC and radar sensing by accounting for the assistance of IRS. An optimization problem is formulated to minimize the total energy consumption of all devices meanwhile satisfying both the radar estimation information rate requirement as well as the latency constraint for computation offloading. We propose an efficient algorithm to optimize the computing and communication resources. The simulation results demonstrate the effectiveness of our proposed scheme of IRS assisted ISAC MEC for energy minimization.

关键词： Mobile edge computing  

摘要： In edge computing, users’ service profiles are migrated between edge servers due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE’s boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. We extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures. © 2022, CC BY.

关键词： Mobile edge computing  

摘要： With the advances of the Internet of Things (IoT) and 5G/6G wireless communications, the paradigms of mobile computing have developed dramatically in recent years, from centralized mobile cloud computing to distributed fog computing and mobile edge computing (MEC). MEC pushes compute-intensive assignments to the edge of the network and brings resources as close to the endpoints as possible, addressing the shortcomings of mobile devices with regard to storage space, resource optimisation, computational performance and efficiency. Compared to cloud computing, as the distributed and closer infrastructure, the convergence of MEC with other emerging technologies, including the Metaverse, 6G wireless communications, artificial intelligence (AI), and blockchain, also solves the problems of network resource allocation, more network load as well as latency requirements. Accordingly, this paper investigates the computational paradigms used to meet the stringent requirements of modern applications. The application scenarios of MEC in mobile augmented reality (MAR) are provided. Furthermore, this survey presents the motivation of MEC-based Metaverse and introduces the applications of MEC to the Metaverse. Particular emphasis is given on a set of technical fusions mentioned above, e.g., 6G with MEC paradigm, MEC strengthened by blockchain, etc. © 2022, CC BY.

关键词： Mobile edge computing   Blockchain   Smart contract   Data caching   Data transaction  

摘要： The emergence of mobile edge computing can provide technical support for the development of Internet services and for coping with massive data traffic, thereby reducing network latency and ensuring efficient network operations and service delivery. However, the mobile edge computing environment is prone to data loss and privacy leakage, and data security and reliability cannot be guaranteed. The application of blockchain technology ensures the stability and dependability of data caching and transactions. In order to guarantee the security of data caching in the mobile edge computing environment and minimize the response time of caching servers, this paper proposes a decentralized data caching strategy in the mobile edge computing environment. The strategy uses a greedy algorithm to make the transmission delay of the requested content as small as possible under the multiple constraints of the storage space of each server and whether the content is cached on the server. To address the problem that data trading platforms are unable to afford the computation and storage of massive amounts of data and cannot guarantee that the data will not be leaked, this paper proposes a secure decentralized data transaction program. The scheme promotes the increase of transaction turnover rate and improves the revenue of both participating parties by establishing a buyer-seller matching algorithm. According to the experimental results, the proposed data caching strategy can improve the cache hit rate and reduce the transmission delay;the proposed data transaction solution can increase the revenue of both the data holder and the data buyer. (C) 2022 Published by Elsevier B.V.

关键词： Mobile edge computing  

摘要： In mobile edge computing (MEC) systems with energy harvesting, edge devices are powered by unstable energy harvested from the environment. To prolong the lifetime of edge devices, some computing tasks should be offloaded to MEC servers. However, computing services offered by MEC servers may be very costly. In this work, we aim to minimize total costs caused by computing services and dropping tasks while avoiding the devices running out energy. With the consideration of the unpredictability of the harvestable energy, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make task execution decisions (i.e., local executing, offloading, or dropping tasks) and develop an online algorithm which could help asymptotically obtain the optimal results for the whole system. The algorithm does not require any knowledge of the harvestable energy and the statistics of task arriving processes and can be easily implemented in a distributed manner. Numerical results corroborate that the proposed algorithm can try its best to push battery energy of edge devices to a preset parameter and effectively reduce the service costs and task drops.

关键词： Mobile edge computing  

摘要： 5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services. © 2022, CC BY.

关键词： Mobile edge computing   Wireless power transfer   Deep reinforcement learning   Computation rate maximization  

摘要： Since most Internet of Things (IoT) nodes are powered by small-capacity batteries and equipped with small computing units, it is difficult for them to process computing-intensive and delay-sensitive tasks. To address these two limitations, a feasible approach is to use both wireless power transmission (WPT) and mobile edge computing (MEC) technologies. Wireless edge devices (EDs) have stable energy supply by capturing radio frequency (RF) energy transmitted by the energy source and offloading computation workload to edge computing servers (ECSs) to enhance the computation rate. In this paper, an FDMA-based wireless-powered MEC network is considered, which consists of an ECS, an RF energy source and multiple EDs. The research goal is to maximize the sum computation rate (SCR) by jointly optimizing the WPT duration, energy allocation for local computation, and computation offloading, and bandwidth allocation among EDs. We first formulate this as a non-convex optimization problem that is hard to address. Then, we decompose it into a top-problem for optimizing the WPT duration and a sub-problem for optimizing the energy allocation and bandwidth allocation under a given WPT duration. An online offloading algorithm based on deep reinforcement learning (DRL) is proposed to quickly obtain the near-optimal WPT duration. For the sub-problem, we design an efficient two-stage algorithm to obtain the optimal solution. The simulation results show that the proposed algorithm can obtain the near-optimal solutions with low computation complexity.

关键词： Mobile edge computing   Energy-aware systems   Task scheduling   Heuristic algorithms  

摘要： Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile *** edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission *** nodes are usually distributed in edge environments,enabling crucially efficient task scheduling among those nodes to achieve reduced processing ***,it is imperative to conserve edge server energy,enhancing their *** this end,this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization(EA-DFPSO)that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution *** proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for *** experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.