ABSTRACT
Internet of Things (IoT) sensor networks comprise a set of sensors and actuators to observe the physical environment. As the sensors are battery powered, the reduction in energy consumption plays a vital role to achieve energy efficiency and maximized network lifetime. To achieve, this chapter introduces a new energy-efficient clustering using metaheuristic quasi-oppositional krill herd (QOKHC) algorithm for IoT sensor networks. The proposed QOKHC algorithm incorporates the concept of quasi-oppositional-based learning in the krill herd algorithm to increase the convergence rate. The presented QOKHC algorithm has the capability of selecting the cluster heads properly and organizes clusters in the network. The efficiency of the QOKHC-based clustering technique has been assessed and the results are examined under diverse measures: residual energy, network lifetime, alive node analysis, and the number of packets transmitted to base station. The obtained simulation outcome stated the superior energy efficiency performance of the QOKHC technique over the compared methods.
