Energy-Efficient Path Optimization Using A Genetic Algorithm (GA)

Abstract

Wireless sensor networks (WSNs) have remained a hot field of research for the last three and a half decades. WSNs comprise small-sized sensors having small batteries, so energy depletion is a significant problem, resulting in a short lifetime of the WSNs. Since the WSNs work as application-centric they have the capabilities of data gathering and transmission to the base station. Therefore, a significant amount of sensor networks` energy is spent during data collection and transmission operations if the route is not optimized. In this study, an efficient optimization routing scheme for multiple hops is proposed to enhance the lifetime of the network. In this regard, a meta-heuristic genetic algorithm plays a vital role in finding the optimized route.

The genetic algorithm is modified by tuning its parameters for better optimization results so that the best route can be selected among all possible routes and will save the wireless sensor networks` energy. All information is routed towards the sink via intermediate neighbor nodes, which only relay the data without performing any computation or operation. The genetic algorithm determines the route information to all nodes and sinks. Then, the data takes the most optimal route for data transmission. The simulation results of 10 sensor node deployments indicate that the path efficiency, namely 44.9% and 59.3%, is achieved through optimal paths to the nearest sinks towards sink1 and sink4, respectively. While the 20 sensor nodes deployment resulted in considerable path efficiency to the nearest sinks towards sink1 with 38.8%, sink2 with 41%, sink3 with 52.3%, and sink4 with 52.2%. Thus, this study proved a considerable saving of energy and resulted in substantially efficient optimized paths.