Wireless Sensor Networks is a new ad-hoc network technology combining technologies sensor, wireless communication and embedded system. It can be widely applied in military surveillance, environment monitoring, healthy caring, smart space and other commercial applications, and is becoming a hot topic in wireless communication researches. It requires that we provide the sensing data with certain location of nodes. Given to the constraint of cost, volume and power of nodes, the present matured Global Positioning System(GPS)is not appropriate for localization of nodes in WSN. So, it has world-wide application background and vital value to research the appropriate localization algorithms in WSN.This paper first analyzes the basic concepts of wireless senseor networks and existing localization algorithm, and presents some localization algorithms of WSNs based on intelligent optimization algorithms. Sencondly, on the basis of the research on node localization algorithms of particle swarm optimization, a new localization algorithm was proposesed based on particle swarm optimization with penalty function. The algorithm use penalty function to accelerate the speed of convergence and improve the localization accuracy. The algorithm introduces a distance error correction coefficientμ,and use it to constrain the measured distance,narrowing the scope of the unknown node's feasible region, and then use particle swarm optimization to find the optimal location of the unknown node in the limited range.In the simulation platform MATLAB, the paper uses RSSI to obtain distance information between sensor nodes, and anslyses PSOPF localization algorithm. First of all, through the simulation experiments, we select the appropriate parameters of the algorithm, for example penalty factor M and inertia weight w . Secondly, tha paper anslyse this algotithm in the ranging error and the number of anchor node, and compare PSO localization algorithm. The experimental results show that PSOPF localization algorithm has a higher positioning accuracy and fast convergence. The algotithm achieve the required positiong accuracy in less iterations, and reduce the capacity consumption of the node. |