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At present, the mainstream solution of edge computing offloading is to model it as a multi-objective optimization problem to minimize energy consumption and delay. Different from the existing research, this paper mainly considered that tasks in different unloading areas had certain similarity in edge computing, which could be used to accelerate the convergence speed and solution effect of the algorithm. Based on this, this paper proposed an evolutionary multi-task multi-objective optimization algorithm to solve task offloading problems in different regions based on evolutionary multi-task optimization. This algorithm considered multiple independent regions to be optimized. It modeled the task offloading system model for each region as a multi-objective optimization problem, dynamically adjusting the communication level of the population by learning the user distribution in different regions and the similarity of the tasks to be processed, accelerating convergence speed, and achieving optimization for two different regions through one evolution. The experimental results show that the proposed algorithm has achieved good results in convergence speed and the uniformity of the optimal solution distribution, and can obtain the unloading deployment optimization scheme under edge computing. [ABSTRACT FROM AUTHOR]

With the explosive growth of mobile data traffic in wireless networks, cache-enabled unmanned aerial vehicles(UAVs) are being used in mobile computing to serve as edge servers to provide on-demand services to users in the network. To bring better quality of experience to users while satisfying other resource constraints, it jointly optimized UAVs deployment, cache placement and user association to minimize content access latency for all users and provided users with content caching services of varying quality. This paper proposed a joint optimization algorithm based on alternating optimization for the scenario where multiple UAVs and ground base stations collaborate to provide caching services. The algorithm achieved a sub-optimal solution with a convergence guarantee by iteratively solving the three sub-problems obtained from the decomposition of the objective problem. Firstly, a successive convex approximation-based algorithm solved the UAV deployment sub-problem. Secondly, it used a greedy-based algorithm to solve the content caching sub-problem. Then it used a successive convex approximation algorithm based on the penalty function to solve the user access sub-problem. Finally, it repeated the above procedure to obtain a sub-optimal solution to the target problem. Several simulation experiments verified the effectiveness and feasibility of the proposed algorithm. The simulation results show that the proposed joint optimization algorithm performs better regarding average content access latency and cache hit ratio than the benchmark algorithm. [ABSTRACT FROM AUTHOR]

In mobile edge computing research, the edge server can effectively save computing resources by caching task data. But how to allocate cache resources to solve the competitive relationship between edge servers, as well as energy consumption and efficiency issues, and achieve optimal system performance is an NP difficult problem. Therefore, this paper proposes an online potential game resource allocation strategy OPSCO (online potential game strategy based on cache optimization) based on cache optimization, using a new cache replacement strategy CASCU (cache allocation strategy based on cache utility) to maximize the utility of the cache. By optimizing the efficiency indicator function of edge servers, combining factors such as cache replacement cost with Lyapunov optimization, potential game, and EWA (exponential weighting algorithm) algorithm, the competitive relationship of edge servers is modeled, and potential game related proofs and analyses are conducted. The simulation results show that compared with other resource allocation strategies, OPSCO can significantly improve the task completion rate and cache utility, reduce equipment energy consumption and time overhead, and solve the resource allocation and data cache problems in mobile edge computing online cache scenarios. [ABSTRACT FROM AUTHOR]

In a multiple-services mobile edge computing network environment, the requirements and popularity of services, the computing requirement and the amount of data transferred from users to edge servers are dynamic with time. How to adaptively adjust the caching subset of total service types in the resource-limited edge server and determine the task offloading destination and resource allocation decisions to obtain the optimum overall system performance is a challenge problem. To solve this challenge, this paper firstly converted it into a Markov decision process, then proposed a soft actor-critic (SAC) -based deep reinforcement learning algorithm to jointly determine not only the discrete decisions of service caching and task offloading but also the continuous allocation of bandwidth and computing resource. The algorithm employed an effective trick of converting multiple continuous action outputs into discrete action choices to deal with the key design challenge that arised from continuous-discrete hybrid action space while improving the accuracy of algorithm decisions. Additionally, the algorithm integrated an efficient reward function that added auxiliary reward terms to improve resource utilization. Extensive numerical results show that compared with other baseline algorithms, the proposed algorithm can effectively reduce the long-term average completion delay of tasks while accessing excellent performance in terms of stability. [ABSTRACT FROM AUTHOR]

Aiming at computer resources was limited and the total overhead of the system processing tasks was too high, this paper proposed an offloading strategy based on dynamic perception-hybrid artificial fish swarm（DP-HAFS） algorithm. First, it constructed a "local-edge-cloud" three-layer network architecture and adopted a fine-grained partial offloading mode; then, for the scenario where a terminal device covered by multiple base stations, this paper proposed a base station selection strategy to select the optimal base station; finally, it used the DP-HAFS algorithm to dynamically modify the unloading decision and got the best uninstall strategy. The simulation results show that the proposed offloading strategy can effectively reduce the total system overhead in a scenario with a large number of tasks. [ABSTRACT FROM AUTHOR]

In order to meet the challenging requirements of next generation networks in terms of capacity, deployment cost and coverage, Mobile Edge Computing (MEC) is usually necessary to complete the latency-sensitive and computation-intensive task with the assistance of relay nodes. This paper first introduces the basic architecture of relay-assisted MEC system, and then summarizes the up-to-date research methods of relay-assisted MEC system from three aspects: task offloading, resource allocation and relay selection. Furthermore, the possible problems and challenges of the existing methods are discussed and analyzed, and some feasible solutions are put forward to provide reference for follow-up research and development. [ABSTRACT FROM AUTHOR]

This paper proposed a mobile edge computing offloading strategy based on multi-hop transmission of network connected vehicles. Through the prediction of future vehicle trajectory, it found effectively the best real-time multi-hop transmission path of vehicle network, so as to ensure that the computing task could be successfully unloaded to MEC server within the time delay requirement. The simulation results show that compared with the traditional mobile offloading strategy, the average task delay is lower, the task success rate is higher, and all aspects of performance are better than the traditional edge computing offloading strategy. Among them, the task unloading success rate increases by 10. 06% on average, and the task delay decreases by 8. 62% on average. [ABSTRACT FROM AUTHOR]

In the Mobile Edge Computing (MEC) environment, many services distribute on edge servers. Accurate clustering of a large number of edge services in MEC environment is important. Therefore, this paper proposed a multidimensional attribute-aware edge service secondary clustering method in MEC environment. Firstly, analyzing and establishing the secondary clustering index model of edge service in MEC environment. After that, a Heuristic Segmented for MST Clustering based on Service Density (DMSC) algorithm is proposed, and clustering edge services based on first-level index. Finally, the γ in the Density Peak Clustering algorithm is introduced into the Hierarchical clustering, and a Hierarchical Clustering based on Density Peak (HCDP) algorithm is proposed. Experiments show that the DMSC algorithm and the HCDP algorithm improve the accuracy of clustering, reduce the average number of iterations of the algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problem of task offloading with dependency in mobile edge computing, this paper proposed a deep reinforcement learning based offloading scheduling method to minimize the execution time of mobile applications. This method described the process of task scheduling as a Markov decision process. It adopted a sequence to sequence deep neural network to represent the scheduling policy, and then trained the deep neural network with the proximal policy optimization method. Simulation results show that the proposed method has good convergence ability and outperforms six baseline algorithms in different environments, demonstrating the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid growth of storage and transmission capabilities smart devices, streaming users can share video services through device-to-device (D2D) communications to achieve video content cooperative delivery in mobile edge computing. However, existing architectures lack suitable incentive mechanisms to solve the problem of economic loss and local data leakage of relay transmission nodes. Aiming at the problems existing in video content cooperative delivery system, this paper pro-posed a joint security and benefit incentive mechanism for multi-bitrate encoding video. And it gave a proof to show that the method satisfied differential privacy, approximate honesty and individual rational. Finally, experimental results show that the mechanism cannot only protect the individual interests of users, but also improve the overall benefits of video services. [ABSTRACT FROM AUTHOR]

Due to the characteristics of high-speed mobility and limited resources of the vehicle itself, the vehicle networks scenarios using traditional communication and computing methods cannot meet the increasing data computing needs and the quality of experience(QoE) required by users. New vehicle networks scenarios built with 5 G and mobile edge computing(MEC) technologies can meet the above requirements, but due to changes in network structure, it is necessary to design vehicle task communication and computing strategies suitable for the new scenarios. This paper studies the dynamic offloading of mobile vehicle tasks in the 5 G vehicle networks scenario, and proposes a corresponding dynamic task allocation strategy and a low-latency algorithm for offloading scheduling. The vehicle will offload the unfinished computing tasks to the corresponding MEC server or vehicle according to the proposed strategy and algorithm, And the calculation result will be directly returned to the vehicle owner through the joint communication between the edge servers or directly from the nearby idle vehicle selected to accept the offloading task. Simulation results show that the proposed strategy and algorithm have good performance in optimizing offload latency and improve the quality of user experience. [ABSTRACT FROM AUTHOR]

Most of the existing identity authentication schemes can’t be applied to MEC（mobile edge computing） environment with frequent interactions in V2G（vehicle to grid） networks due to imperfect mechanism, large delay and high cost of authentication. To address the above issues, this paper proposed a lightweight hierarchical identity authentication scheme based on elliptic curve cryptography and hash function. The scheme introduced semi-trusted entities to realize two-stage mutual authentication among end, edge and cloud, and designed an evaluation mechanism of utility to reduce the rounds of authentication. The formal security analysis proves that the proposed scheme has high security under the random oracle model, and can defend against various attacks including the information leakage attack. Moreover, the performance evaluation indicates that it reduces the amount of transmitted messages by 16.25%,the computing cost by 19.4%,the communication overhead by 25.38%,and is highly suitable for MEC with multi-entity participation. [ABSTRACT FROM AUTHOR]

Due to its limited battery life and computing power, driverless cars are difficult to meet the processing needs of some delay-sensitive tasks or intensive tasks while ensuring battery life. To solve this problem, in the context of Mobile Edge Computing (MEC), this paper proposed an driverless task offloading policy based on deep Q-network (DQN). First, this paper defined a "vehicle-edge-cloud" cooperative task offloading model based on task priority, which needs to jointly optimize the computing power of the vehicle and the task offloading policy to obtain the minimum delay and energy consumption of the system. Since the problem is a mixed-integer nonlinear programming problem and is NP-hard, this paper solved it in two steps—the first step obtained the analytical solution for the optimal computation power of vehicle through mathematical derivation, and then, under the fixed numerical value condition, the DQN algorithm obtained the optimal offloading strategy of the task. Finally, this paper established a simulation model by integrating tools such as SUMO, Pytorch and Python. This paper compared the DQN algorithm and the other three algorithms under different task loads, MEC server computation powers and energy consumption weight co-efficients. The experimental results verify the feasibility and superiority of the proposed policy. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a method to solve the computing-intensive task requirements of robots. Traditional algorithms are based on intelligent algorithms or convex optimization methods, and the iteration time is long. Deep reinforcement learning can be solved in a single forward pass, but only for a fixed number of robots. Through the analysis and research of deep reinforcement learning, this paper performed input regularization before the input layer in the deep reinforcement learning neural network, and added a convolution layer after the output layer so that the network could adaptively meet the unloading requirements of the number of dynamic mobile robots. Finally, it carried out the simulation experiments to verify the effectiveness and feasibility of proposed algorithm, compared with the adaptive gen etic algorithm and reinforcement learning . [ABSTRACT FROM AUTHOR]

To address the issue of resource wastage and increasing latencies in the placement process of edge servers, this paper analyzed and modeled the user density and average access time of edge server placement schemes as multi-objective optimization problems. It designed an edge server placement scheme based on user density and average access time, and proposed a multi-objective sea-horse genetic algorithm (MOSGA) to solve this problem. The MOSGA algorithm firstly used the idea of multi-objective optimization algorithm to improve the SHO algorithm, so that the SHO algorithm could be applied to multi-objective optimization problems. It used the genetic algorithm to improve the propagation operation of the SHO algorithm, so that the MOSGA could better jump out of the local optimal solution and accelerate the solution of the problem. The proposed algorithm was verified on the data set of Shanghai Telecom, and the simulation experiment results show that MOSGA is obviously better than RA, K-means, NSGA and LMM, which not only effectively solves the problem of server resource waste, but also greatly reduces the time of terminal equipment to access the server. [ABSTRACT FROM AUTHOR]

In the scenario that a dedicated energy transmitter serves multiple wireless-powered users, aiming to minimize the task execution latency, this paper proposed a new multi-user cooperative computation scheme and formulated the problems to optimize user matching and resource allocation. For the one-to-one matching case between users, this paper derived a high-performance solution based on the alternating optimization and Hungarian algorithms and a low-complexity solution based on the reformulation-linearization technique. For the one-to-many matching case, this paper derived an improved greedy algorithm. Experimental results show that in the case of one-to-one matching, the proposed scheme can reduce the task processing delay by up to 12. 6% compared with the comparison schemes, for the one-to-many matching case, the proposed scheme saves 5% of the task processing delay. The proposed scheme can meet the task execution latency requirements of the users. [ABSTRACT FROM AUTHOR]

Considering federated learning under mobile edge computing, where a global server connects a large number of mobile devices through the network to jointly train a deep neural network model. The data distribution shift caused by global class imbalance and device local class imbalance leads to the performance degradation of the standard federated averaging ( FedAvg) algorithm. This paper proposed a device selection algorithm based on the combinatorial multi-armed bandit ( CMAB) as an online learning algorithm framework, and designed a class estimation scheme to form a nonlinear reward function. Based on class estimation scheme and CMAB, in each round of communication, the global server selected the device subset with class imbalance that could be best complementary with test performance deviation across classes of global model from last communication round. Therefore, the current aggregated global model could achieve more balance and better test performance per class, and also faster convergence and more stable training dynamics. Extensive numerical results demonstrate the influence of different parameters on FedAvg under class imbalance, and verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

At present, most existing works focus on offloading tasks to MEC servers, in view of the total process time of the computation tasks. However, the on board unit (OBU) still has its computing capacity. To solve these problems, this research aimed to optimize the time and energy consumption of tasks and proposed a collaborative multi-task computing offloading model, where OBU in the task-solving vehicle and neighbor vehicles compute in collaboration with the MEC servers. This model also considered the energy and time consumption as well as priority among different tasks. It introduced the idea of simulated annealing and the constriction coefficient to improve the particle swarm optimization algorithm and got solution. Experimental results show that compared with other offloading strategies, the proposed task offloading strategy has obvious effects on the total cost optimization, the solutions quality of TPSO is 53. 8% of PSO algorithm, 27. 1 % of the LOCAL-MEC strategy and 78% of the DCOS, and it can adapt to various application scenarios. [ABSTRACT FROM AUTHOR]