An Efficient Node to Node Coverage and Connectivity with RSSI Using Grey-Wolf Prediction Optimization Algorithm in Remote Low Accessibility Area

Wireless Sensor Networks (WSN) which are specifically designed for monitoring disaster applications require precise knowledge of the location of the sensor nodes, since the nodes tend to relocate from their initial deployed position when disaster strikes. In addition, due to sensing, processing and transmission of monitored data, energy of the nodes gets depleted resulting in energy holes and might lead to partition in the network which is undesirable. In this work, Delaunay Triangulation (DT) method is used to determine the Point of Intersection (POI) between the partitioned sensor node groups. Received Signal Strength Indicator (RSSI) and Predicted Received Signal Strength Indicator (PRSSI) techniques are used to find the connectivity strength between the partitioned group nodes and the POI. Grey Wolf Optimization with Weight Prediction Algorithm (GWO-WP) is used to improve RSSI and arrive at a stronger POI. It is also shown that the use of mobile nodes to collect data from multiple POI establishes good connectivity between the partitioned groups.