A Green Data Collection & Transmission Method for IoT-Based WSN in Disaster Management

Abstract

Being as an integral part of data exchange in disaster management, Internet of Thing (IoT) is an essential component to update the disaster management information to all the connected nodes. In such scenarios, the nodes are deployed with a limited battery, batteries of these nodes are non-rechargeable and such networks consume huge energy in data exchange. Therefore, the practical implementation of such type of networks is a difficult task as routing avoids the paths and consume a tremendous amount of energy during collection/transmission of data. This paper proposes an optimized Genetic Algorithm (GA) based green data collection/transmission method for IoT based WSN in disaster management by satisfying multiple constraints i.e., optimizing intra-cluster distance, systematic utilization of node’s energy in the cluster and reducing hop count. The proposed direct data collection/transmission and movable sink strategies shorten the communication distance between the sink and cluster head (CH) which diminishes the hotspot problem. The direct data collection helps in transmitting data directly to the sink when the sinks are nearer to the sensor nodes with respect to CH. Further, the incorporated dynamic sensing range minimizes overlapping of sensing range of CH along with a significant decrement in the 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, and throughput per rounds.