Based on genetic algorithm for wireless sensor network node self-localization

Wireless sensor network node positioning technology is one of the key technologies. Due to self-localization of sensor nodes in the process of positioning accuracy is not high, In this paper, the genetic algorithm approach to take, through the evolution of control, making the location of the nodes for continuous progress toward the optimal solution, in order to achieve continuous process of node positioning optimization. Simulation results show that the evolution of the genetic algorithm control, can reduce errors, improve positioning accuracy. Positioning accuracy of wireless sensor network node is related to the collected data validity. [10] propose a weighted least-squares principle with the idea of positioning algorithm, but the algorithm needs the ranging error of the mean square value; Bulusu N propose a HEAP density adaptive algorithm, in order to improve the positioning accuracy of nodes by adding new beacon nodes in low density of nodes regions; [3] mentioned several positioning of the distance-independent mechanisms were compared to the size of the beacon node density affects the level of positioning accuracy. Applications vary widely, there is no universal algorithm for positioning to address different applications, considering the node size, cost and system requirements for positioning accuracy to select the most suitable location algorithm. In this paper, node self-localization from the discussion, the use of genetic algorithm-node self-localization algorithm optimization process to find the most suitable location accuracy requirements as the coordinates of the unknown node to achieve optimal control.