Research On UWSN Node Location Based On Mobile Anchor

In recent years,with the continuous consumption of terrestrial resources,human requirements for the development of marine resources have become stronger.In order to enhance the understanding of the ocean,Underwater Wireless Sensor Network(UWSN)has an irreplaceable role in ocean exploration today.Whether in the military field for target positioning or in marine resource exploration,marine biological research,and so on,for the calibration of monitoring targets or regional locations,underwater positioning technology is required as an effective means to complete.Therefore,estimating the position coordinates of a node has become a research hotspot.In UWSN,network nodes can be divided into anchor nodes with known coordinates and unknown nodes that need to calculate coordinates.Due to the high cost of the anchor node,the use of the mobile anchor node can reduce the number of anchor nodes,improve the coverage of the anchor node to the UWSN,reduce the network cost,and reduce the energy consumption of the positioning process.This thesis focuses on the path planning of mobile anchor nodes and the location of unknown nodes in UWSN,aiming at reducing network energy consumption and improving positioning accuracy.The specific research contents are as follows:Aiming at the problem of path planning for mobile anchor nodes in UWSN,a path planning strategy based on Hopfield neural network is proposed.Firstly,this strategy combines with the underwater acoustic channel propagation loss model to select the nodes with important locations and many neighbor nodes in all nodes as virtual anchor nodes,so that the mobile anchor can broadcast to the surrounding locations of each virtual anchor node,which can completely cover the entire network.In the process,a filtering strategy is proposed to minimize the number of virtual anchor nodes.Then consider the path planning of all virtual anchor nodes as the Traveling Salesman Problem(TSP),and connect these virtual nodes through the Hopfield neural network to minimize the total path length.In order to solve the randomness of Hopfield neural network,and make it be applied to a large number of TSPs.In the end condition part,the idea of crossover operator Position-based Crossover is introduced,and a crossover strategy is proposed to reduce the total path length of the planning.Through simulation experiments,this strategy can solve the path planning problem ofUWSN mobile anchor,and can effectively reduce the total path length.Aiming at the problem of large calculation error in the traditional DV-Hop algorithm in UWSN,a UWSN node localization algorithm based on Artificial Bee Colony algorithm(ABC)is proposed.The algorithm optimizes DV-Hop from two aspects: average hop error correction and positioning calculation optimization.Firstly,the average hop distance is corrected by the error between the estimated distance and the actual distance between the anchor nodes.In the correction process,the influence of the hop count on the error is used to constrain the weight of the error.Secondly,by setting the objective function,the ABC is used to replace the original least squares method to calculate the coordinates of the nodes,and in the ABC,the process of neighborhood searching of employed bee and the method of developing a new honey source of scout bee,the mutation strategy of differential evolution and the Gaussian distribution are respectively introduced to improve the localization accuracy and convergence speed.The simulation results show that the algorithm improves the positioning accuracy than DV-Hop and ADV-Hop that ABC was replaced by the calculated phase under the same network parameters.