An Optimized Genetic Algorithm for Cluster Head Election Based on Movable Sinks and Adjustable Sensing Ranges in IoT-Based HWSNs

Internet of Things (IoT)-enabled wireless sensor network (WSN) permits the development of various IoT-based applications, ranging from industry to education and military to agriculture. However, the common IoT devices usually have very limited battery power, which is not frequently rechargeable. Thus, an energy-efficient mechanism is required to operate IoT-enabled WSN. To address the limited power shortcoming of IoT-enabled WSN, we propose an optimized genetic algorithm (GA) for cluster head (CH) election (OptGACHE). The CH election using GA incorporates four different criteria, namely: 1) node density; 2) distance; 3) energy; and 4) heterogeneous node’s capability for the development of fitness function. These criteria help in optimizing intracluster distance, systematic utilization of node’s energy in the cluster, reducing hop count, and promoting selection of highly capable nodes for CHs. The proposed movable sink strategy shortens the length of communication distance between sink and CH and also diminishes the hotspot problem. Furthermore, the incorporated dynamic sensing range adjustment minimizes the overlapping of sensing range of CH along with cutting down transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics, namely, network’s remaining energy, lifetime, stability period, throughput, and the number of clusters per rounds.