Research On Hierarchical Prediction Localization Technology Of Underwater Wireless Sensor Networks

With the development of the country's modern marine economy,marine development and utilization occupy a key strategic position in the development of the country.With convenient deployment,high flexibility and strong self-organizing ability,underwater sensor networks can be widely used in data collection in the ocean.The location information of the node is a prerequisite for ensuring that the data information collected by the node has practical significance,so that the monitoring of the target area can be realized.Since the performance of sensor nodes is limited by the marine environment,it is difficult for underwater nodes to communicate directly with the satellites for real-time positioning.Meanwhile,nodes deployed in the ocean have limited energy and are difficult to replace batteries or replenish energy,so it is necessary to reduce the communication cost of the network.When monitoring a large-scale target area,the localization coverage of the node should be improved.Aiming at the difficulty of frequent changes of node location and communication cost of underwater wireless sensor networks,this paper realizes hierarchical prediction and localization based on the motion model of nodes,and combines particle swarm optimization to further improve the localization accuracy of nodes.According to the spatial correlation of the underwater node motion,the unknown node establishes its own motion model based on the information of the known node to complete the localization,the energy consumption of the network communication is reduced.The main work of this paper is as follows:(1)The nodes in the underwater sensor networks are divided into anchor nodes and common nodes for hierarchical localization.The motion equation is established according to the motion characteristics of the nodes moving with the tidal current.Considering the influence of noise interference on the localization accuracy of the nodes,the AR model and the Kalman filtering algorithm is combined to optimize the speed of the anchor node,and then realize the prediction localization of the anchor node according to the motion equation.In order to reduce the accumulation error,an optimization model is established for the location calculation method of the anchor node.The particle swarm optimization algorithm with decreasing inertia weight based on Gaussian function is used to optimize the location information and velocity information of the anchor node to improve the localization accuracy.(2)Considering the finite energy and calculation ability of the common node,the ordinary node establishes a motion model based on the information transmitted by the anchor node and combines the historical location information to achieve the localization.In order to improve the localization coverage,the confidence of the node is introduced to select the located normal node with higher precision as the reference node to assist the unknown node positioning.At the same time,the reference node list update mechanism of the common node is designed to update the information of the reference node in time,thereby improving the localization accuracy of the common node.(3)For Hierarchical prediction localization method based on AR model and Kalman algorithm(HPLM-AK)and particle swarm optimization anchor node information algorithm based on HPLM-AK(HPLM-AK-PSO),compared with the simulation of SLMP,the results show that the HPLM-AK is superior to the SLMP algorithm in terms of average localization coverage,average localization error and average communication cost,moreover,the average localization error of the node is further reduced in HPLM-AK-PSO.