An Improved Method for Distributed Localization in WSNs Based on Fruit Fly Optimization Algorithm

The use of a localization system is necessary in Wireless Sensor Networks (WSN) either for communication protocols (geographic routing) or for various applications (person tracking, battlefield tele-monitoring, enemy detection, etc.). The objective of a localization method in such environment is to find the locality (or position) of all sensors deployed randomly in a multidimensional field in order to accomplish a specific task. We specify in this paper an improved localization method in WSNs called FOA-L (Fruit Fly Optimization Algorithm for node's Localization). The proposed method applies the Fruit fly Optimization Algorithm (FOA) to minimize the error between estimated and real locations of the unknown sensors. In the proposed localization scheme, we initialize a group of flies in the search area and they are given a random value of direction and distance. Then, we find out the flies with the highest smell value using fitness in order to estimate the location of the target node. Simulation results concerning performance evaluation show that the proposed technique FOA-L has the better localization accuracy than the well-known localization algorithms such as Particle Swarm Optimization and Chicken Swarm Optimization as well as a faster computation time, which contributes in reducing the cost of the sensor's localization.