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： 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： 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： Task analysis   Energy harvesting   Delays   Servers   Energy consumption   Computational modeling   Central Processing Unit   Computation offloading   deep reinforcement learning   energy consumption   mobile edge computing   offloading strategy   wireless information and power transmission  

摘要： Computation offloading is an effective method in mobile edge computing (MEC) to relieve user equipment (UE) from the limited computation resource and battery capacity. Meanwhile, simultaneous wireless information and power transmission (SWIPT) can be applied to MEC to extend the operating time of the equipment. However, in multi-user network environment, diverse computation task requirements and changeable network channel states make it challenging to obtain offloading strategy timely and accurately. To address the issue, we propose an intelligent computation offloading scheme (iCOS) based on enhanced priority deep deterministic policy gradient (EPDDPG) algorithm to minimize the energy consumption of all the UEs by jointly optimizing the offloading decision, the central processing unit (CPU) frequency and the power split ratio in a dynamic SWIPT-MEC network. In particular, we improve the traditional fully-connected network structure to obtain both discrete and continuous action outputs, and accelerate neural network parameter updates by using prioritized experience tuples. Furthermore, we use dynamic voltage and frequency scaling (DVFS) technology to dynamically adjust the CPU frequency of local computing, and employ SWIPT technology to balance the charging and communication according to the obtained strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the energy cost of UEs, and complete more computation tasks within the delay limit.

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   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： Link selection   Mobile edge computing   Quality of service   Vehicular networks  

摘要： Due to the roadside unit can not fully achieve seamless coverage and the dynamic change of network topology in vehicular networks, problems such as the interruption of vehicle service, poor stability of communication link, and high network delay will be caused. To solve these problems, this paper proposes a codirectional multilane adaptive message return (CMAMR) strategy based on edge computing. Firstly, we establish a CMAMR communication model. Then, according to the movement trajectory of the receiving node, we propose a single-hop and a multi-hop message return link strategy. The former combines the single-hop link quality and node location information to construct a node selection function to assist the head node in selecting an optimal forwarding node to form a single-hop message return link. The latter assists the RSU to select the optimal multi-hop message return link in the same lane by establishing a link adaptive quality evaluation function, thus improving the user's quality of server. The simulation results show that the link network delay of the CMAMR strategy is 14.6%, 42.86%, and 38.47% lower than those of the random with forwarding progress (RFP), most forward with fixed radius (MFR), and traditional vehicle-to-infrastructure and vehicle-to-vehicle cooperative communication (TVCC) message return strategies, respectively;correspondingly, the link reliability is at least 9.13% higher for the CMAMR strategy.

关键词： Internet of Vehicles (IoV)   mobile edge computing   cooperation   resource management   low latency   energy efficiency  

摘要： To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs' workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can he also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs' workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.

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： Non-orthogonal multiple access   mobile edge computing   scheduling  

摘要： This letter investigates the cooperative computing in a non-orthogonal multiple access (NOMA)-enabled mobile edge computing (MEC) system consisting of one edge server, one user equipment (UE) and M full-duplex helpers. For this system, we design an adaptive cooperative computing strategy that operates in one of two modes: the cooperative NOMA-MEC mode and the non-cooperative orthogonal multiple access (OMA)-MEC mode. For this strategy, we propose a novel two-stage fullduplex helper scheduling scheme to improve the successful completion probability (SCP). Theoretical analysis is carried out to derive a tightly approximate SCP and further demonstrate that the UE achieves a full diversity in high main-to-loop-selfinterference ratio regime. Simulation results verify the accuracy of the theoretical analysis and demonstrate the advantages of the proposed strategy.