An Integrated Xbee Arduino with Group Search Optimizer Approach for Localization in Wireless Sensor Networks

Abstract

Most of the real life problems are non linear, non differentiable and multi objective with more constraints. Traditional optimization algorithms cannot find solution or is more complex to find an optimal solution and its complexity increases with the scalability problem. This is the motivation of nature and bio-inspired heuristic algorithms which provide better optimal solution for real life problems. This paper gives an approach of one such bio-inspired algorithm called as group search optimizer localization algorithm integrated with Xbee arduino sensor network, which is used to minimize the localization error of wireless sensor networks. The algorithm is developed from the inspiration of animal food and basic need foraging behavior and is a group based localization optimization algorithm. This employs a resource finding producer and scrounger follower model. Real time data collected by Xbee arduino sensor network is used by group search optimizer localization algorithm to self determine the location information of sensor nodes. Localization in wireless sensor networks is needed to improve the performance, reliability and life time of the network.