Comparative analysis of PSO-SGO algorithms for localization in wireless sensor networks

Abstract

The Wireless sensor networks (WSN) combine autonomous wireless electronic devices which have abilities like sensing, processing, and communication. It is a self-organizing network constructed with immense number of sensors. Localization is about detecting a node at particular geographical position usually titled as range. Nodes in WSN can be installed uniformly, with formation of grid or randomly. When nodes are installed randomly it is important to determine the exact location of the node. But this approach is expensive and not always feasible using geographical positioning system (GPS). It will not provide definite location results in indoor surroundings. The challenging task of WSN includes improving accuracy in approximating position of a sensor node based on anchor nodes. They are incorporated in a network, such that their coordinates play an essential role in location estimation. A well-organized localization algorithm is capable of determining the accurate coordinates for position of nodes by making reference from sensor nodes. Optimization algorithms like Particle swarm optimization (PSO) and Social group optimization (SGO) are implemented with the fitness equation and the performance of both the algorithms are compared. This paper projects a fitness equation such that the results of PSO and SGO are validated by comparing error accumulation factor in both the algorithms.