A novel method for optimizing energy consumption in wireless sensor network using genetic algorithm

Abstract

Advancements in information technology and wireless sensors networks (WSN) create new possibilities in a variety of industries, including environmental testing, healthcare, and industrial control. The sensor has become more intelligent, has begun to go wireless, and has become smaller. WSN is used in various industries thanks to its benefits such low cost, simple installation, consistent transmission, and excellent anti-interference ability. Although wireless network sensors have numerous benefits, they also have certain drawbacks. The main obstacle limiting the lifespan of wireless sensor networks is energy, and it might be difficult or even impossible to charge or replace the power sources on wireless network nodes. The battery puts limits on the network nodes responsible for data processing and information transfer. The key to network optimization is thus to increase the network's overall life. WSN have a wide range of recognized uses, and this diversity necessitates improvements to the existing protocols and their particular properties. The lifespan of the network and energy usage for routing are two important criteria that are important in every application. In order to create the perfect set of parameters for routing and application-based WSNs, the study discussed here improved network coverage, clustering, node placement, and data aggregation. A unique fitness function was created, enhanced, and customised for usage in all stages of WSN operation based on the outcomes of evolutionary algorithms and NS simulations. The results of the comparison indicated that, while to varied degrees, these logarithms and methods might reduce that energy. It has been determined that the suggested methodology could lead to a 50% reduction in energy consumption.