Data Collection in WSNs using a Probability-Based Rendezvous Points Selection Algorithm

Abstract

Wireless Sensor Networks are becoming more prevalent in various industries, including military operations and distant environmental monitoring. This is important because sensors are getting smarter, smaller, and less expensive. The energy hole problem in the WSN has been a major focus of recent research. The mobile sink is an efficient solution for the energy hole problem in a wireless sensor network. A mobile sink gets data from sensors by moving around the network often to avoid problems with hotspots or energy holes. It gets data from network nodes by traveling regularly and visiting a group of nodes known as rendezvous points (RPs). This research will present a probability-based RP selection (PRPS) technique for data collection in wireless sensor networks. To begin, a directed spanning tree is used to construct a tree that eliminates duplication in the data forwarding path. The proposed method is employed to compute the likelihood of RPs. Finally, using the shortest path technique, a mobile sink is constructed between these locations. The path provided is the best path that connects all of the RPs. The proposed approach improves the previous solutions by choosing the nodes with the most data packets as RPs. As a result, it extends network lifetime by lowering energy consumption and addressing the energy hole problem.