An Enhanced Genetic Algorithm (EGA)-based Multi-Hop Path for Energy Efficient in Wireless Sensor Network (WSN)

Sensor Networks (WSNs) encounter a number of issues in terms of performance. In WSN, the majority of the energy is utilized to transfer data from sensor nodes to a central station or hub (BS). There have therefore been many different types of routing protocols devised to help with the distribution of data in WSNs. Large-scale networks have been designed with minimal power consumption and multipurpose processing due to recent improvements in wireless communication and networking technology. For the time being, sensor energy remains a restricted resource for constructing routing protocols between sensor nodes and the base station, despite advances in energy collection technologies. For wireless sensor networks with far-flung cluster heads and base stations, direct transmission is a critical component since it impacts the network's efficiency in terms of power consumption and lifespan. A new approach for identifying an effective multi-hop routing between a source (CH) and a destination (BS) is investigated in this study in order to decrease power consumption and hence increase the life of a network (OMPFM). The suggested technique utilizes a genetic algorithm and a novel fitness metric to discover the best route. For selecting CHs and enhancing the speed and quality of created chromosomes, they suggest two pre-processes, which they call CH-selection and Chromosome Quality Improvement (CHI). The proposed method is evaluated and compared to others of its kind using MATLAB simulator. It has been found that using the proposed method outperforms other methods such as LEACH, GCA, EAERP, GAECH, and HiTSeC in terms of the first node die metric by 35%, 34%, 26%, 19% and 50%, respectively. It also outperforms other methods by 100% in terms of the last node die metric.