关键词： Mobile edge computing   edge computing   deadline-aware task offloading   pricing   offloading  

摘要： In this paper, we study the deadline-aware task data offloading in edge-cloud computing systems. The hard-deadline tasks strictly demand to be processed within their delay deadline, whereas the deadline can be relaxed for the soft-deadline tasks. Generally, edge computing aims to shorten the transmission delay between the remote cloud and the end-user, however, at the cost of limited computing capability. Therefore, it is challenging to decide where to offload the hard- and soft-deadline tasks based on the average delay and the service price set by the edge and cloud servers. Both edge and cloud servers aim to maximize their revenue by selling the computational resources at the optimal price. Interestingly, a Wardrop equilibrium is reached, considering that each task is considered independently to be offloaded to a suitable location. The numerical results demonstrate that the proposed price- and deadline-sensitive task offloading policy reaches the equilibrium and finds the optimal location for processing while maximizing the revenue of both edge and cloud servers.

关键词： Mobile edge computing   Identity authentication   Key agreement   Symmetric encryption   SVO logic   User anonymity  

摘要： Mobile Edge Computing (MEC), which is a product of 5G technology development, is actually a kind of edge computing form. It takes the base station as the edge server and sinks the cloud computing services to the network edge. To better protect the users and the base stations, a lightweight authentication and key agreement scheme, which is applicable to the MEC field, is proposed in this paper. It achieves secure and efficient key agreement under the premise that the users and the edge servers are completely anonymous and untraceable. For this scheme, instead of the complicated elliptic curve computation and the bilinear pairing computation, it flexibly adopts a number of temporarily generated symmetric keys to realize the identity authentication of the three parties while guaranteeing both the security and efficiency. Compared with other schemes, it is featured in low performance requirements and can better adapt to MEC environment.

关键词： Computation offloading   Mobile edge computing   Markov chain   User mobility   Task randomness  

摘要： Recent years have witnessed substantial research efforts on computation offloading for mobile edge computing (MEC) systems. User mobility is an intrinsic trait of many MEC applications, which has posed significant challenges for realizing reliable computing. However, existing works studying this problem mainly focus on the movements of users while another high-dynamic behavior due to the randomness of computation task is largely ignored. To fill this gap, in this paper, we formulate the computation offloading decision problem in MEC system as a combinatorial optimization problem, and then we use Log-Sum-Exp function to approximate the optimal objective. Thereafter, we construct a Markov chain with steady-state distribution specifying to our problem in a distributed manner, such that the user mobility problem is transformed into the state transition problem. Moreover, this Markov chain is further extended to consider a dynamic scenario where the number of active users in the MEC system changes due to the random arrivals of new computation task or completions of old tasks. Numerical results show that our proposed computation offloading distributed algorithm can converge very fast to the optimal solution, and has a provable performance with a guaranteed loss bound.

关键词： Energy efficient   intelligent reflecting surface   mobile edge computing   non-orthogonal multiple-access  

摘要： This letter studies a non-orthogonal multiple-access (NOMA)-based mobile edge computing (MEC) system. In order to enhance communication coverage and energy efficiency, intelligent reflecting surface (IRS) is proposed, which is deployed to assist computation offloading from end users to an access point (AP) equipped with an edge server. We aim to minimize the sum energy consumption by jointly optimizing the transmit powers, transmission time, offloading partitions and IRS phase shift. To address the non-convex formulated problem, we first derive the optimal solution of transmit power in closed-form, and then decouple the reformulated problem into two subproblems and employ alternating optimization (AO) technique to solve it. Numerical results validate that the proposed IRS assisted NOMA-based MEC scheme can yield superior performance in comparison with other benchmark schemes.

关键词： smart city   mobile edge computing   task offloading   resource allocation   DDPG   federated learning  

摘要： Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices.

关键词： Task analysis   Sensors   Servers   Optimization   Computational modeling   Resource management   Energy consumption   Computation offloading   cooperative vehicle infrastructure system   deep reinforcement learning   joint optimization   mobile edge computing  

摘要： In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.

关键词： Mobile edge computing   Computing offloading   Genetic algorithm   Ant colony algorithm  

摘要： As the key technology of edge computing, computing offloading has attracted the attention of many scholars in recent years. Many people use heuristic algorithm as the basic algorithm to study the algorithm of computing offloading, but a single heuristic algorithm has some defects, such as some will fall into local optimal, and some will converge prematurely. In order to make up for the defects of single heuristic algorithm applied to the calculation offloading and improve the efficiency of the algorithm, this paper combines genetic algorithm with ant colony algorithm, and designs the calculation offloading strategy of gene-ant colony fusion algorithm. Firstly, a group of solutions are obtained through the selection, crossover, mutation and other operations of genetic algorithm, and the solution is improved as the initial solution of ant colony algorithm. The fusion algorithm makes full use of the feedback value of genetic algorithm and the high efficiency of ant colony algorithm to overcome the shortcomings of the two algorithms. The feasibility of the algorithm is verified by several groups of experiments. The simulation results show that compared with GA, ACA and PSO, the number of iterations is reduced by 17.96%, 24.43% and 36.25% respectively. When the base station remains unchanged, the G-ACA has the lowest objective function value. Compared with GA, ACA and PSO algorithm, the objective function value is reduced by 36.68%, 16.15% and 11.35% respectively. That is, the fused algorithm is better than the non fused GA,ACA and PSO in time delay, energy consumption and objective function value.

关键词： Computation offloading   game theory   mobile edge computing   multi-channel communications   Nash equilibrium  

摘要： Mobile edge computing is emerging to provide cloud-computing capabilities to mobile users, so that they can offload computation intensive tasks to close proximity for execution. However, most existing works imply that a transmission-finished task still occupies the channel until all users on the same channel finish the transmission, leading to severe channel resource waste. To solve this problem, we propose an efficient computation offloading mechanism which releases the channel resources of transmission-finished tasks for transmission-unfinished tasks, and aims to minimize the response time and energy consumption for each user. Specifically, we formulate the computation offloading problem as a game, analyze its structural properties and show how it possesses a Nash equilibrium and admits the finite improvement property, in the cases of elastic cloud and non-elastic cloud respectively. We then propose a Distributed Multi-channel Computation Offloading (DMCO) algorithm, which can converge to a Nash equilibrium, and find the upper bound of the convergence time. We further evaluate the performance of DMCO using the price of anarchy. Numerical results show that DMCO scales well with the number of users, and outperforms existing works, for example, benefits 13.3 percent more users and reduces cost by 23.7 percent than CO, one of the best existing works.

关键词： 6G Network   Actor-Critical learning   Digital Twin Edge Network   Mobile Edge Computing  

摘要： Sixth Generation (6G) network is meant to allow wireless networking and computing by digitalizing and sharing everything, by providing a computer image of the actual network world. Mobile edge computation as one of the key factors in allowing mobile downloads faces unparalleled obstacles because the 6G network environment is incredibly dynamic and unforeseeable. In the latest literature on mobile edge computing, the implications of user mobility and the volatile mobile edge computing world are still ignored. In this paper, we propose a new methodology for the Digital Twin mirror that offers training data to offload decisions for digital edge servers to evaluate the edge servers' status and the Digital Twin for the whole edge computing environment. In the wireless twin edge networks, the proposed system is to reduce the download delay in the face of the cumulative expense of relocation from the accessed service Mobility for consumers. The Lyapunov approach's Optimization is used to simplify the cost constraint of Long-term transformation to an intra-functional enhancement challenge, which is then resolved by profoundly enhanced Actor-Critic (AC) learning. Replications demonstrate that, as opposed to benchmark systems, our proposed arrangements effectively decrease the average offload delay, discharge failure rate and operation migration rate and save device costs with Digital Twin help. © 2022 University of Bahrain. All rights reserved.

关键词： Radar   Sensors   Robot sensing systems   Precoding   Radar signal processing   Communication symbols   Resource management   Dual function radar and communication   mobile edge computing   MIMO precoding   radar sensing  

摘要： In this paper, an integrated communication, radar sensing, and mobile-edge computing (CRMEC) architecture is developed, where user terminals (UTs) perform radar sensing and computation offloading simultaneously at the same spectrum by using multiple-input and multiple-output (MIMO) arrays and dual-function radar-communication techniques. We formulate a multi-objective optimization problem to jointly consider the performance of multi-UT MIMO radar beampattern design and computation offloading energy consumption while jointly optimizing individual transmit precoding for radar and communication and computation resource allocation. To address the optimization problem, we first decompose the it into three subproblems and adopt an iterative optimization algorithm. Specifically, quadratic transform based fractional programming methods are used to minimize the offloading energy consumption. The design objective of MIMO radar beampattern is handled by the first-order Taylor expansion. Transmit precoding is designed to optimize radar sensing and computation task offloading. The local and edge computation resource allocation are obtained in closed-form. Numerical results verify the effectiveness of the proposed algorithms. The proposed CRMEC architecture can generate the desired multi-UT MIMO radar beampattern and perform computation offloading simultaneously.