Data Collection and Path Determination Strategies for Mobile Sink in 3D WSNs

Abstract

Wireless sensor networks (WSNs) are composed with a set of sensor nodes, and its primary task is to sense and relay the data to the base station or sink. Sensor nodes are equipped with a limited battery, and more energy is consumed due to the relay of the data. While relaying on more data, nodes near to the base station will die soon and result in energy-hole or hotspot problem. A mobile sink has been introduced to collect the data from the nodes to avoid the hotspot problem. Several data collection algorithms have been proposed for 2D WSNs; however, these algorithms do not meet the requirements of the 3D WSNs due to its variations in topology, connectivity, coverage, scalability, etc. Therefore, the selection of mobile sink visiting points called rendezvous points (RPs), and path determination between then in 3D WSNs is a challenging task. In this paper, we determine the optimal RPs in 3D WSNs using unsupervised learning-based hierarchical agglomerative clustering. Further, we propose an algorithm for path determination between the RPs with minimal computational overhead. The simulation runs show that the proposed method finds the near-optimal set of RPs and path of the mobile sink while enhancing the network lifetime.