Research On Target Localization And Node Localization Algorithms Based On Wireless Sensor Networks

Wireless sensor networks are composed of numerous cooperative sensor nodes,which show significant potential for a wide range of applications including medical health,environmental monitoring,and target tracking.Location information is crucial in many applications,so how to obtain localization algorithms with high localization accuracy and low complexity has been extensively studied.In this paper,target localization and node localization algorithms in wireless sensor networks are explored and studied in order to further improve the localization accuracy.The main contents include:(1)The existing asynchronous time of arrival(TOA)based target localization algorithms usually use the relaxation technique of convex optimization to transform the nonconvex localization problem into a convex one,so as to obtain an approximate optimal solution for the target location.Unlike the existing algorithms,this paper proposes an asynchronous TOA target localization algorithm based on differential evolution algorithm,which does not require relaxation of a nonconvex problem into a convex one.The signal transmission instant is eliminated from the objective function by expressing it as a function of the target position in least squares criterion.Finally,the differential evolution algorithm with opposition-based learning and adaptive redirection is used to solve the coordinates of the target node.Simulation results demonstrate that,compared with other algorithms,the proposed algorithm has higher localization accuracy and lower computational complexity in four different localization scenarios,showing strong robustness.(2)Based on the above algorithm,a hybrid RSS-TOA target localization algorithm based on differential evolution algorithm with unknown transmission parameters is proposed.The proposed algorithm addresses the problem of target localization using received signal strength(RSS)and TOA measurements when transmit power,path loss exponent and signal transmission instant are unknown.The differential evolution algorithm with opposition-based learning and adaptive redirection is used to simultaneously estimate transmit power,path loss exponent,and target location.Simulation results confirm the effectiveness of the proposed hybrid algorithm when the transmission parameters are unknown,and validate that it performs better than its counterpart utilizing only RSS and TOA measurements.(3)In order to improve the localization accuracy of distance vector-hop(DV-Hop)algorithm,an improved DV-Hop node localization algorithm based on marine predators algorithm is proposed.During the distance estimation phase,the algorithm adopts the weighted minimum mean square error criterion to correct the average hop distance of the anchor nodes,and then the average hop distance used by unknown nodes for estimating the distance is modified through averaging the received average hop distance from anchor nodes.In the node location estimation stage,the marine predators algorithm is employed to solve the coordinates of the unknown nodes.Simulation results show that the proposed algorithm is superior to the basic DV-Hop algorithm and other improved algorithms in terms of localization accuracy under the square random network topology.