Q-Learning-based Edge Node Resource Allocation Algorithm in the Environment of Power Distribution Internet of Things

In the power distribution Internet of Things environment, the execution efficiency of user tasks is low when smart node resources are limited. In order to solve this problem, this paper designs an edge computing network architecture under the power distribution Internet of Things environment. The architecture includes three types of devices: smart nodes, network devices, and edge computing nodes. The execution mode of the computing task of the smart node is modeled, the local computing model and the remote computing model are designed, and the task execution time model and energy consumption model are established respectively, as well as the objective function of the edge node resource allocation problem. The four key elements of state space, action set, reward function and search mechanism for solving the optimal resource allocation problem under Q- Learning theory are designed. A Q-Learning-based edge node resource allocation algorithm is proposed, and it is verified that the algorithm in this paper can improve the execution efficiency of user tasks.