Genetic Algorithm based Approach to Determine Optimal Collection Points for Big Data Gathering in Distributed Sensor Networks

Abstract

In recent of data, distributed sensor networks have become one of the primary source of generating big data. Therefore energy- efficient data gathering in densely distributed sensor networks is a demanding area of research. Among the various techniques of data acquisition, the mobile sink approach is highly suitable in densely distributed sensor networks. However, optimizing the trajectory of mobile sink is a crucial challenge to be addressed by researcher. The clustering-based Expectation Minimization technique proposed by Takaish et al. is an efficient approach to minimize the energy consumption of sensors while maintaining the node coverage. However, clustering of nodes may not ensure an optimal trajectory of mobile sink node. In this paper, we use genetic algorithm based approach to optimally select the data gathering points that minimize the distance of mobile sink trajectory to improve data collection time. The experimental results depict that the proposed technique is able to achieve optimal trajectory for mobile sink compared to Expectation Minimization technique.