An Energy-Efficient Modified Metaheuristic Inspired Algorithm for Disaster Management System Using WSNs

In recent trends, Internet of Things (IoT) enabled Wireless Sensor Network (WSN) is widely used for various applications like emergency medical services, fire detection, flood control, etc. for disaster management in the smart city. Commonly, sensor devices are equipped with limited battery but they consumed a lot of battery in the communication for such scenarios. Therefore, practical comprehension of such networks is the challenging task as data collection consuming tremendous energy due to the shortcomings of the existing methods i.e., cluster overlapping, elect lower energy nodes as cluster-head (CH), formed highly dense clusters and hotspot problem in the network, and large communication distance. To overcome the above-discussed issues, an integrated modified Genetic Algorithm (GA) for CH election in WSNs is proposed to maximizing the network lifetime referred to as ModifyGA. The energy-efficiency of ModifyGA is increased by incorporating dynamic sensing range and criteria's used for developing fitness function. The ModifyGA satisfies various constraints for optimizing intra-cluster distance, systematic utilization of node's energy in the cluster, reducing hop-count and promoting selection of highly capable nodes for CHs. The simulation analysis of ModifyGA is carried-out to operate with a single static sink, multiple static and movable sinks to have an impartial comparative analysis.