An efficient data collection using wireless sensor networks and internet of things to monitor the wild animals in the reserved area

This paper investigates the possibility of using the Wireless Sensor Networks (WSNs) with the Internet of Things (IoT) in which the sensor nodes are attached to the collar of the animals to track the movement pattern of wild animals, and identify the territorial behavior, population and hunting. The random movement of animals creates the network issues such as the energy hole, void problem, poor network lifetime, coverage, and link failure due to animal mobility. To overcome these issues, an efficient data collection mechanism called Location based Clustering and Opportunistic Geographic Routing (LCOGR) is introduced. In this work, a Location Point (LP) is applied to select the Cluster Head (CH) that confirms the uniform distribution of CHs and improves energy efficiency. Also a BYPASS beacon based geographic routing is designed to transmit data to the Base Station (BS) which in turn is connected to the cloud sever. LCOGR ensures stable connectivity and complete coverage of the sensing area. The findings of the simulation show that the suggested strategy considerably increases network efficiency compared to the well-known protocols such as CSDGP, VELCT and MBC.