A GA-Based Sustainable and Secure Green Data Communication Method Using IoT-Enabled WSN in Healthcare

This article proposes an optimized genetic algorithm (GA)-based sustainable and secure green data collection/transmission method for IoT-enabled WSN in healthcare by optimizing intracluster distance, systematic utilization of node's energy, and reducing hop count. For secure transmission of data, the communication data is encrypted using stream cipher and a pseudo-randomly generated security key. Additionally, the proposed movable sink and data collection/transmission strategies shorten communication distance between 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 with a significant decrement in the transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics, such as remaining energy, lifetime, stability period, throughput, and the number of clusters per rounds.

Automated summary

This article proposes an optimized genetic algorithm-based method for sustainable and secure green data collection/transmission in IoT-enabled WSN in healthcare. The method optimizes intracluster distance, node's energy utilization, and reduces hop count. The data is encrypted using stream cipher and a pseudo-randomly generated security key for secure transmission. The proposed movable sink and data collection/transmission strategies shorten communication distance and diminish the hotspot problem. The incorporation of dynamic sensing range minimizes energy consumption. Simulation results show the superiority of the proposed protocol over existing protocols on various performance metrics.