关键词： Intelligent reflecting surface   mobile edge computing   multi-cell networks  

摘要： The explosive development of the Internet of Things (IoT) has led to increased interest in mobile edge computing (MEC), which provides computational resources at network edges to accommodate computation-intensive and latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have gained attention as a solution to overcome blockage problems during the offloading uplink transmission in MEC systems. This paper explores IRS-aided multi-cell networks that enable servers to serve neighboring cells and cooperate to handle resource exhaustion. We aim to minimize the joint energy and latency cost by jointly optimizing the computation tasks, edge computing resources, user beamforming, and IRS phase shifts. The problem is decomposed into two subproblems-the MEC subproblem and the IRS communication subproblem-using the block coordinate descent (BCD) technique. The MEC subproblem is reformulated as a nonconvex quadratic constrained problem (QCP), while the IRS communication subproblem is transformed into a weight-sum-rate problem with auxiliary variables. We propose an efficient algorithm to alternately optimize the MEC resources and IRS communication variables. Numerical results show that our algorithm outperforms benchmarks and that multi-cell MEC systems achieve additional performance gains when supported by IRS.

关键词： Computation offloading in MEC   Machine learning   Multi-access edge computing   Mobile edge computing   Computation for 5G/6G communication  

摘要： The advancement of technology towards the realization of the evolving mobile computing paradigm brings a rapid paradigm shift in its usage, especially in the Internet, computation, and communications, that has a profound impact on businesses, services, and users. With the rise in resource -intensive or edge -based mobile applications such as autonomous driving, Amazon Go, virtual and augmented reality, and healthcare -related applications, countless challenges in computation and communication parameters like latency, bandwidth, and energy consumption are evolving. As a result, the multi-access edge computing (MEC) paradigm receives enormous attention where some portions of the user applications are offloaded to powerful machines for their efficient execution to optimize different evaluation metrics or to achieve performance goals. While a few survey works are available in this direction, none of them focuses explicitly on the emerging machine learning (ML) based computation offloading techniques and various associated sub -problems together. This paper aims to provide a detailed but precise overview of the research on using ML techniques for MEC environments. In this survey, we focus on how authors and researchers utilize the ML models in computation offloading problems on MEC architecture. We extend our study by considering several edge architectures, offloading parameters, ML approaches, and problem formulation strategies concerning computation offloading. Additionally, this paper discusses the potential challenges in the direction of computation offloading on MEC architecture.

关键词： Mobile edge computing   Service offloading   Deep reinforcement learning  

摘要： Computing offloading is a potential avenue to reduce transmission delay by moving computing tasks from cloud to edge nodes. Due to the limited computing capacity of edge nodes, performing effective computing offloading strategy is challenging. To simplify the problem, it is often assumed that the timeline is discrete and offloading decisions are only made at the end of each time slot. However, the assumption results in decision waiting time and increases service delay. In this paper, we first formulate a time-continuous computing offloading problem without the assumption of discrete timeline. In order to reduce service delay while guaranteeing user fairness, the optimization objective is constructed as the alpha-fair utility function of average task delay rather than just average task delay. Then, we propose a novel algorithm by adjusting the reward in standard reinforcement learning to solve this problem, and also prove the convergence of our algorithm theoretically. To the best of our knowledge, this paper is the first attempt to study the time-continuous computing offloading problem with fairness. Evaluations show that the proposed algorithm has better performance in terms of service delay and user fairness compared to five baselines.

关键词： Task analysis   Privacy   Servers   Delays   Wireless communication   Costs   Perturbation methods   Differential privacy   location privacy   mobile edge computing   task offloading  

摘要： In mobile edge computing (MEC), users can offload tasks to nearby MEC servers to reduce computation cost. Considering that the size of offloaded tasks could disclose user location information, several location privacy-preserving task offloading mechanisms have been proposed under the single-server scenario. However, to the best of our knowledge, none of them could provide a strict privacy protection guarantee or be applicable to the multi-server scenario where the user's location can be inferred more accurately if servers collude with each other. In this paper, we propose a novel location privacy-aware task offloading framework (LPA-Offload) for both single-server and multi-server scenarios, which provides strict and provable location privacy protection while achieving efficient task offloading. Specifically, we propose a location perturbation mechanism that allows each user to perturb its real location within a rational perturbation region and provides a differential privacy guarantee. To make a satisfactory offloading strategy, we propose a perturbation region determination mechanism and an offloading strategy generation mechanism that adaptively select a proper perturbation region according to the customized privacy factor, and then generate an optimal offloading strategy based on the perturbed location within the decided region. The determination of the perturbation region could achieve personalized privacy requirements while reducing computation cost. LPA-Offload is proved to satisfy (epsilon,delta)-differential privacy, and the experiments demonstrate the effectiveness of our framework.

关键词： Mobile edge computing   Multi-task multi-server   Non-orthogonal multiple access   Processing capability   Resource allocation   Task offloading  

摘要： With the rapid development of technology, the Internet of vehicles (IoV) has become increasingly important. However, as the number of vehicles on highways increases, ensuring reliable communication between them has become a significant challenge. To address this issue, this paper proposes a novel approach that combines Non-Orthogonal Multiple Access (NOMA) with a time-optimized multitask offloading model based on Optimal Stopping Theory (OST) principles. NOMA-OST is a promising technology that can address the high volume of multiple access and the need for reliable communication in IoV. A NOMA-OST-based IoV system is proposed to meet the Vehicle-to-Vehicle (V2V) communication requirements. This approach optimizes joint task offloading and resource allocation for multiple users, tasks, and servers. NOMA enables efficient resource sharing by accommodating multiple devices, whereas OST ensures timely and intelligent task offloading decisions, resulting in improved reliability and efficiency in V2V communication within IoV, making it a highly innovative and technically robust solution. It suggests a low-complexity sub-optimal matching approach for sub-channel allocation to increase the effectiveness of offloading. Simulation results show that NOMA with OST significantly improves the system's energy efficiency (EE) and reduces computation time. The approach also enhances the effectiveness of task offloading and resource allocation, leading to better overall system performance. The performance of NOMA with OST under V2V communication requirements in IoV is significantly improved compared to traditional orthogonal multiaccess methods. Overall, NOMA with OST is a promising technology that can address the high reliability of V2V communication requirements in IoV. It can improve system performance, and energy efficiency and reduce computation time, making it a valuable technology for IoV applications.

关键词： unmanned aerial vehicle   mobile edge computing   UAV-aided edge computing   task offloading   offloading decision   trajectory planning  

摘要： Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

关键词： Digital twin (DT)   mobile edge computing   vehicular ad-hoc networks (VANETs)   blockchain   synchronized privacy-preserving authentication (SPPA)  

摘要： Digital Twin (DT) technology, by performing simulation, analysis, and prediction over the data mapped to digital space, can create a digital replica of the physical object. It can be combined with edge computing or cloud computing to provide broad vehicle-to-everything applications and improve the service quality of vehicular ad-hoc networks (VANETs). In this article, DT technology and mobile edge computing are integrated into VANETs to introduce a framework of mobile digital twin edge network-driven VANETs (MDTEN-Driven VANETs). Moreover, facing the security and privacy challenges in the framework, we propose a synchronized privacy-preserving authentication (SPPA) scheme. In SPPA, we first design a synchronized anonymous certificateless aggregate signature (SA-CLAS) to achieve the authentication with time state synchronization and the privacy preservation of the real identity. Furthermore, to deal with malicious vehicles, we adopt blockchain technology and devise a smart contract algorithm to manage the public information of vehicles. The security analysis demonstrates that SA-CLAS is existentially unforgeable under adaptive chosen message attacks, and SPPA can satisfy the necessary security requirements. The performance evaluation shows the efficiency and practicality of SA-CLAS and SPPA. Besides, the designed smart contract is implemented in an Ethereum test network, which presents an acceptable blockchain consumption.

关键词： Mobile edge computing   5G   Bilateral teleoperation   ROS   Robot control   Edge analytics  

摘要： Human-centric bilateral teleoperation of robots over a network enables a human controller to perform assistive or corrective control to modify the state of a remotely placed robot. However, the timeliness of control depends on dynamics such as communication latency, bandwidth and quality/quantity of feedback information. In various specific use cases, inadequate compliance to communicate expeditious control could result in hazards. In this paper, we demonstrate the architecture of the split-control mechanism to enable edge-based control in bilateral teleoperation. Edge computing allows a robotic setup to benefit from the introduction of intelligent algorithms at the edge. We characterize the notion of feasibility of edge-based analytics by demonstrating two scenarios viz., a command controlled see-saw-like robot (with a low-complexity data/intelligence to capture the delay from cloud to edge);and a haptic glove controlled robotic hand (with an ML-based intelligence algorithm without incorporating the edge-to-cloud delay). We report a practical implementation of a 5G Mobile Edge Computing system that is leveraged to implement the specified split-control mechanism to detect and avoid certain unwanted events arising from the large latency between a robot and a remote human controller. Experimental results show that the proposed edge-based split-control system notably aids the human-centric control mechanism. We, then, introduce the notion of edge-fiability to capture the limits of edge-based prediction and control approach.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real-world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service. © 2024, CC BY.

关键词： Deep reinforcement learning (DRL)   LEO satellite networks   Lyapunov optimization   mobile edge computing   stochastic computation offloading  

摘要： The deployment of mobile edge computing services in LEO satellite networks achieves seamless coverage of computing services. However, the time-varying wireless channel conditions between satellite-terrestrial channels and the random arrival characteristics of ground users' (GUs) tasks bring new challenges for managing the LEO satellite's communication and computing resources. Facing these challenges, a stochastic computation offloading problem of joint optimizing communication and computing resources allocation and computation offloading decisions is formulated for minimizing the long-term average total power cost of the GUs and the LEO satellite, with the constraint of long-term task queue stability. However, the computing resource allocation and the computation offloading decisions are coupled within different slots, thus making it challenging to address this problem. To this end, we first employ the Lyapunov optimization to decouple the long-term stochastic computation offloading problem into the deterministic subproblem in each slot. Then, an online algorithm combining deep reinforcement learning and conventional optimization algorithms is proposed to solve these subproblems. Simulation results show that the proposed algorithm can achieve the superior performance while ensuring the stability of all task queues in LEO satellite networks.