Dynamic differential evolution strategy localization for Wireless Sensor Networks in three-dimensional space

Wireless Sensor Networks (WSNs) have been widely used in many different environments. The positioning accuracy of sensors node are the essential parameters for various applications such as target detection and tracking system, traffic management, intelligent transportation and many other applications. A localization algorithm based on dynamic differential evolution strategy (DDES-L) is proposed. In order to improve the convergence rate and positioning accuracy, the previous position information of sensors have been used as vital anchor nodes. The distance measurement noise is also considered in this work. To illustrate the effectiveness and the efficiency of the proposed new algorithm, numerical simulations and practical experiments have been carried out. This work also compares the proposed localization results with the genetic algorithm localization (GA-L) and least squares method (LS).