Data mules-oriented particle swarm optimisation-based mobile sink data gathering techniques with analytical data analysis using linear regression

Wireless sensor networks with converge-cast nature poses great challenge on data collection strategies. In order to cut down the issues on constrained resources of wireless nodes, a sink-based (PSOMSDG) particle swarm optimisation-based mobile sink data gathering had been proposed. This PSOMSDG is a rendezvous-based protocol which uses three metrics for data gathering based on the nodes position as; when the nodes are in inertia; when they change to optimistic position (based on current scenario); finally when they change to swarms' optimistic position. These three metrics avoid long detour path by providing global optimal length constrained trajectory. In addition, residual energy consumption of protocol was achieved in a balanced manner. The performance is noticed with increasing data rates and compared with biased sink mobility with adaptive stop times (BSMAST). Then data was obtained with NS2 simulation which was developed into a linear regression model. Finally, the analytical study states that there is a strong relationship between data rate and energy consumption. The analysis of variance (ANOVA)-based analysis shows that there is a strong influence between groups.