Implement Hybrid Algorithm to decrease localization error in Wireless Sensor Network
Abstract
The enormous technical development has resulted in the ubiquitous adoption wireless sensor networks (WSN) in many spheres of life, posing great obstacles, the most essential of which is location determining. There are three most well-known methods to handle these difficulties: Based on the K-means model (an algorithm for grouping data) the first method divided a data set. The second method is the PSO algorithm, which makes use of a group of elements known as a "swarm" randomly dispersed in a constrained area to arrive to the ideal answer. The third method is the genetic algorithm, which uses Darwinian perspective imitation of the work of nature to attain optimum. In order to decrease the localization error in this paper, a hybrid method was applied leveraging the advantages of the genetic algorithm and the swarm intelligence algorithm. Actually, this method was evaluated individually against the k-means method, the intelligent swarm algorithm, and the genetic algorithm. The novel method greatly lowered the localization error in wireless networks and obtained an average error of 28.56 m, the lowest among the three compared techniques. The performance of the suggested method was assessed by means of simulations adjusting numerous PSO and GA parameters. While the results of GA and PSO converge and one may move over the other, the experimental results revealed that the suggested algorithm is always the best and k-means is the lowest.
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