Abstract
Green communication in wireless sensor networks (WSNs) has witnessed significant attention due to the growing significance of sensor enabled smart environments. Energy optimization and communication optimization are two major themes of investigation for green communication. Due to the growing sensor density in smart environment, intelligently finding shortest path for green communication has been proven an NP-complete problem. Literature in green communication majorly focuses towards finding centralized optimal path solution. These centralized optimal-path finding solutions were suitable for application specific traditional WSNs environments. The cutting edge sensor enabled smart environments supporting heterogender applications require distributed optimal path finding solutions for green communication. In this context, this paper proposes a genetic algorithm enabled distributed zone approach for green communication. Specifically, instead of searching the optimal path solution in the whole network, the proposed algorithm identifies path in a small search space called distributed forward zone. The concept of forward zone enhances the searching convergence speed and reduces the computation centric communication cost. To encode the distributed routing solutions, variable length chromosomes are considered focusing on the target distributed area. The genetic algorithm enabled distributed zone approach prevents all the possibilities of forming the infeasible chromosomes. Crossover and truncation selection together generate a distributed path finding solution. To validate the experimental results with analytical results, various mathematical models for connectivity probability, expected end-to-end delay, expected energy consumption, and expected computational cost have been derived. The simulation results show that the proposed approach gives the high-quality solutions in comparison to the state-of-the-art techniques including Dijkstra's algorithm, compass routing, most forward within radius, Ahn-Ramakrishna's algorithm and reliable routing with distributed learning automaton (RRDLA). (C) 2019 Elsevier B.V. All rights reserved.