Research On Node Status Detection Method Of Wireless Sensor Network Based On Reinforcement Learning

At present,the wireless sensor network has become the technical commanding heights of the fourth industrial revolution,defined as a wireless communication system composed of a large number of sensor nodes in its own way.This kind of network can use sensor nodes to monitor relevant physical or environmental information in various geographical areas,which will bring about tremendous changes to human production and lifestyles.However,its operation distribution is poor and dynamic in a wide-area environment,and there are various outstanding problems,such as intermittent links,low network efficiency,etc.,making the application environment and related conditions of WSN subject to many restrictions,so that the sensor nodes may suffer from the network There are many internal and external security threats,and after this problem occurs,the node is not easy to be evaluated and the status of the node is recognized,which causes the node to malfunction and cause failure,unable to perform correct behavior,and ultimately cause WSN to fail to operate normally.Therefore,how to ensure the normal operation of WSN and the safety of internal nodes,research on WSN node detection methods has become a hot topic.This article focuses on researching in an unknown natural field,placing several sensor nodes and networking to form a wireless sensor network,and performing the task of detecting the state of sensor nodes during the network operation.In this process,the characteristics of the WSN network and the system and node structure were fully considered,and the related node detection technology was analyzed.Finally,the wireless sensor network and the related theories of reinforcement learning were combined to design a node state detection method.To maintain network security and improve network operation efficiency.From the perspective of improving the detection rate of sensor nodes and reducing the false alarm rate,this method maps the detection of wireless sensor network nodes to a reinforcement learning optimization problem,constructs a dynamic programming model for node state detection tasks and solves it.Firstly,a Markov Decision Processmodel is established for the sensor nodes in the network.Due to the large scale of the nodes,it is easy to cause dimensional disasters when solving.The hierarchical reinforcement learning Option method is introduced to simplify hierarchically,making the entire node The state detection task is decomposed into several subtasks,and the node interacts with the environment and the network and performs related actions to obtain rewards;then,the traditional Q-Learning algorithm is optimized by using simulated annealing technology and increasing the learning process;after that,the optimization algorithm is used to iterate each In the layer subtask,the Q value function of the node until it converges,and the optimal detection strategy of the node MDP model is solved to determine the normal and abnormal state of each node in the network,so as to achieve the purpose of detecting the node.Finally,the WSN node status detection method based on Markov decision process is applied to the smart campus environment monitoring system to complete the detection of sensor node status.Take the campus infrastructure as the system application occasion,and use some campus facilities such as office buildings,canteens and libraries as the campus environment monitoring objects,deploy sensor nodes in these campus facilities to perceive environmental information,and upload it to the computer and back-end database.Process and analyze environmental data and node status to achieve the purpose of intelligent monitoring of the campus environment.