A Data Aggregation Mechanism based on Spatial Correlation Chain-clustering for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are an emerging technology in the mobile ad hoc network as used for many different applications. It has several features, such as limited resources and features, smaller packet size, and dynamic multi-hop transmission. During the worldwide COVID-19 pandemic, WSNs have been used to measure social distancing/contact tracking among people. WSNs are also used to monitor the environment as a part of military applications. However, the major challenge that WSN protocols face lies in the limited battery energy for sensor networks. To effectively transfer data onto a base station (or “sink”) and achieve the efficiency of node energy saving, we group neighboring nodes into the same clusters with a K-means++ clustering and reduce the additional data sent to the sink. Therefore, this study proposes an energy-efficient data aggregation mechanism (EDAM) with a spatial correlation among sensor nodes to avoid high correlation among data and reduce node redundancy data transmission. The EDAM depends on the threshold of the first-order radio model's distance to divide the whole WSN area into several clusters. All cluster heads (CHs) construct a data relay transmission link in a WSN. The EDAM is based on the center of the whole sensing area and divides the WSN area into four quadrants. All data is transferred from all sensors to a CH in a quadrant. Then, the CH in each quadrant forwards the data it receives to its next quadrant. The EDAM adopts a quadrant rotation mechanism to aggregate the data in the four quadrants to the base station. The simulation results show that the EDAM reduces redundant data transmissions, averages the power consumption of nodes in the cluster, and obtains a better overall network lifetime than the LEACH, LEACH-C, and DEEC algorithms.