Centroid Based Localization Utilizing Artificial Bee Colony Algorithm

Estimation of position of unknown nodes is of immense importance for proper deployment and tracking of sensors. The centroid based localization algorithm (CLA) is widely used for the localization of the sensors but its original and modified versions suffer from large positioning error. Here the localization algorithm is evaluated in terms of localization error utilizing artificial bee colony based (ABC) algorithm. Comparison of outcome is presented through other widely used techniques including swarm based particle swarm optimization (PSO) and evolutionary algorithms based differential evolution (DE) on basic centroid localization algorithm. The results obtained through simulation demonstrate that localization error is minimal in ABC and DE based CLA as compared to basic and PSO based schemes but the computation time is the largest in DE based localization algorithm as compared to others. In comparison to the basic CLA the average localization error is reduced by 95% and computation time is increased by seven fold in ABC based CLA. It may be established that having considered localization error of prime importance, ABC algorithm based CLA is the most suitable strategy for localization amongst all the three algorithms.