Research On Node Coordination Moving Algorithm For Target Tracking In WSN

Target tracking technology based on Wireless Sensor Network(WSN)has always been one of the research hotspots.However,sensor network resources are limited,which limits the accuracy and real-time performance of target tracking.From the perspectives of resource consumption,tracking accuracy,and delay response,this paper studies the coordinated scheduling and tracking algorithms for target tracking nodes.The paper analyzes the background and significance of WSN target tracking and the current research status at home and abroad,explores the framework of sensor scheduling,introduces the knowledge about reinforcement learning,and finally chooses the combination of Deep Deterministic Policy Gradient(DDPG)for reinforcement learning.Scheduling for sensor nodes.The detailed establishment of the model of node coordinated scheduling is explained in detail,and it provides a theoretical basis for subsequent DDPG learning and training.The contributions of this paper are as follows: This paper proposes a target tracking algorithm based on DDPG learning and centralized node coordinated scheduling.This algorithm is a collaborative tracking algorithm for single targets.It is mainly aimed at the optimization of target tracking overall performance.Tracking accuracy.In this study,a task model and an energy consumption model are established.The model is designed as an environment for DDPG training and interaction with the agent.Finally,the state space of the environment,the action space of the agent,and the reward function are designed.The DDPG method is used to iteratively train.The training center is trained as an agent to obtain a feasible motion coordination strategy,thereby ensuring reasonable energy consumption and effective tracking accuracy in wireless sensor networks.In addition,this paper proposes a target tracking algorithm based on DDPG learning and distributed node coordinated scheduling.First,a system for target tracking is established to optimize system response delay and tracking accuracy under the constraints of available energy.A new hierarchical structure is proposed to realize the coupling function of perception and computing.In the case of those sensor nodes with limited resources,Unmanned Aerial Vehicles(UAVs)serve as edge nodes to provide computing services.An intelligent scheduling strategy is used to coordinately track tasks and guarantee low latency and low energy consumption.In order to verify the correctness and robustness of the algorithm proposed in this paper,this paper uses Python language,TensorFlow and Pyglet modules to build a relevant experimental platform.Through a large number of simulation experiments,it is shown that the algorithm of node tracking in WSN proposed in this paper can effectively reduce energy consumption while ensuring tracking accuracy and fast response,and has certain practical value.