Research On WSN Location Algorithm Based On Intelligent Search Strategy

As an important part of information perception and collection in Internet of things,wireless sensor network,has greatly influenced and changed our life.For most of applications,data should be collected accurately and location information should be more precise.Therefore,the research of node localization and related applications in wireless sensor networks is very important.In this paper,the existing positioning system and positioning algorithm is analyzed in detail,and the relative positioning technology in the range-free localization algorithm is optimized.And the localization strategy of wireless sensor network node based on swarm intelligence algorithm is proposed.The specific work is as follows:In this paper the node localization problem is transformed into a multidimensionalmathematical constraints optimization problem,and then the problem is solved by the efficient swarm intelligence algorithm.As the traditional least squares method is vulnerable to the nodes ranging error,a node localization algorithm of wireless sensor network is proposed based on the cuckoo search algorithm.In this algorithm,the cuckoo search is used to solve the constraint optimization problem by its strongly global and local search ability.By setting the corresponding constraint fitness function,the hunting zone was reduced during the positioning search.The convergence rate was speeded up.The position of unknown node can be found quickly.The simulation results show that the algorithm can effectively improve the node's sensitivity of the ranging error,and effectively improve the positioning accuracy of the node,hence has better practicability.Aimed at the defects of energy consumption of nodes,in this thesis,a bat search strategy is proposed based on levy flights.The optimization strategy is applied to the optimization process of node localization.The simulation results show that: compared with the bat algorithm and particle swarm optimization algorithm,our algorithm can effectively reduce the computation and the complexity in the process of positioning.Meanwhile it improves the positioning accuracy of the node while effectively saving the average energy of the whole network.