Hybrid Evolutionary Algorithm with Energy Efficient Cluster Head to Improve Performance Metrics on the IoT

In recent times, the internet of Things (IoT) is an alternative model that is quickly getting ground in the scenario of current wireless telecommunication. Wireless sensor network (WSN) is a significant part of IoT, and it is primarily accountable for reporting and acquiring information. As coverage area and lifetime of WSN directly define the performance of IoT, how to design a technique for conserving node energy and decreasing node death rate becomes crucial problem. Sensor network clustering is an efficient technique to overcome this problem. It splits nodes into clusters and chooses one to be cluster head (CH). The data communication and transmission within single cluster are accomplished by its CH. This study develops a hybrid evolutionary algorithm-based energy efficient cluster head selection (HEA-EECHS) technique in the IoT environment. The presented HEA-EECHS technique concentrates on the effectual choice of CHs in the IoT environment. To do so, the HEA-EECHS technique derives an improved artificial jellyfish search algorithm (IAJSA) by the incorporation of oppositional based learning (OBL) approach into the traditional AJSA. Along with that, the HEA-EECHS technique designs a fitness function incorporating four parameters namely energy, cluster node density, average neighboring distance, and average distance to BS. The experimental assessment of the HEA-EECHS technique is investigated under several IoT nodes and the final results gives the value of 500 WMNs, the HEA-EECHS method has attained decreased CMO of 0.0015. The simulation output highlighted the improvised efficacy of the HEA-EECHS technique.