Task Allocation Mechanism of Power Internet of Things based on Cooperative Edge Computing

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Wang, Qianjun, Shao, Sujie, Guo, Shaoyong, Qiu, Xuesong, Wang, Zhili
• Format: Article
• Language: English
• Subjects: Algorithms; Business; Computational modeling; Cooperation; Cooperative edge computing; Crossovers; Data processing; Delays; Edge computing; Genetic algorithms; Inspection; Internet of Things; Network latency; Nodes; Particle swarm optimization; Power consumption; Power Internet of Things; Resource management; Task allocation; Task analysis; Task completion delay; Terminals; Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3020233

Edge computing can be widely used in unmanned aerial vehicle (UAV) inspection, field operation control, power consumption information collection and other businesses in the power Internet of Things scene. Edge computing offloads functions such as data processing and applications to network edge nodes near the terminals to provide low-latency services and ensure service quality. However, with the explosive growth of business terminals, the capacity of single edge node is limited and it is difficult to meet all business requirements at the same time. Therefore, this paper proposes a task allocation mechanism based on cooperative edge computing. Firstly, a task allocation model based on cooperation of two edge nodes is established to minimize the average task completion delay while meeting business requirements. Secondly, the Two-edge-node Cooperative-task Allocation based on Improved Particle Swarm Optimization (TCA-IPSO) algorithm is proposed, which applies the crossover and mutation strategy in genetic algorithm to improve the particle swarm optimization algorithm, and solves the problem that the task allocation scheme in cooperation is prone to fall into a local optimum. Finally the simulation results show that the proposed TCA-IPSO algorithm reduces the average task completion delay by 53.8% and 36.0% compared to the benchmark and QoS-based Task Distribution (QBTD) algorithm.