Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-10 Cooperative journals: 《计算机应用研究》
Abstract： 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.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-04-07 Cooperative journals: 《计算机应用研究》
Abstract： 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.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： To solve the problem of high service response delay caused by explosive growth of data traffic in the Internet of vehicles, this paper proposed an Ant Colony Simulated Annealing algorithm cache strategy based on Mobile edge computing(ACSAM) . Firstly, under the 5G based " vehicle-edge-cloud " collaborative system architecture, established a communication computing model to minimize the content download delay; secondly, used the Ant Colony Optimization to construct a local optimal solution to minimize the content download delay; finally, used the search ability of the Simulated Annealing algorithm to disturb the local optimal solution and accept the new solution with a certain probability, thereby obtained the global optimal solution and improved cache hit rate. Simulation results show that under the vehicle-edge-cloud collaborative architecture, the ACSAM cache strategy can significantly reduce transmission redundancy and reduce download latency.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-05-10 Cooperative journals: 《计算机应用研究》
Abstract： With the rapid development of mobile Internet services, mobile applications such as augmented reality, virtual reality, and ultra clear video have become popular and IoT applications are emerging. The limited computing power and the lack of endurance of smart terminal devices are becoming more and more inadequate for these applications. Aiming at this situation, this paper proposes a computational offload strategy based on improved auction algorithm under the premise of combining intelligent device performance and server resources in the scenario of multi-user and multi-MEC server, the strategy consists of two important phases: the unloading decision-making phase. By considering the calculation task size, computing requirements and server computing power, network bandwidth and other factors, this paper proposes the basis for the uninstallation decision; In the task scheduling phase, by considering the time requirements of the computing task and the computing performance of the MEC server, this paper proposes a task scheduling model based on improved auction algorithm. The experiment proves that the computational offloading strategy proposed in this paper can effectively reduce the service delay, reduce the energy consumption of smart devices, and improve user satisfaction.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-05-10 Cooperative journals: 《计算机应用研究》
Abstract： Mobile edge computing technology combines IT service environments and cloud computing technologies at the edge of the network to improve the computing and storage capabilities of edge networks while reducing network operations and service delivery delays. Vehicular networks integrating MEC, could meet the strict requirements of vehicle service delay and communication reliability, as well as improve the quality of service for vehicle users. This paper studied and analyzed MEC applications in vehicular networks. Firstly, it provided a brief on the basic concept and architecture of MEC, and its typical application scenarios. Thereafter, it described a comprehensive survey of relevant research in the area of MEC-based vehicular networks along with a focus on the current SDN-related works on MEC-based vehicular networks, as well as several use cases. Finally, this paper discussed the main problems and challenges to deploy MEC in vehicular networks; and analyzed the future research directions on these areas.
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