An Energy-Aware Spatial Index Tree for Multi-Region Attribute Query Aggregation Processing in Wireless Sensor Networks

One of the fundamental tasks for spatial index trees constructed in wireless sensor networks is to determine the sensors, which can participate in the region query accurately and quickly. Most of the existing works focus on constructing the spatial index trees for single attribute sensors having the same sensing capability. The key principle underlying the design of these works is the exploitation of parent–child node relation in the network structure, such as the routing tree in which message broadcasting for the parent node selection will consume more energy. However, due to the existence of multi-attribute sensors having different sensing capabilities in skewness distribution, it is more practical to obtain an energy-efficient spatial index tree to query the multi-attribute sensors in a realistic skewness distribution. Specifically, in this paper, we propose a novel energy-efficient heuristic density-based clustering model to build such a multi-attribute spatial index tree. In addition, multiregion attribute aggregation queries are carried out in our proposed index tree, which mainly focus on the recombination of query regions and query attributes. Finally, through an extensive performance evaluation study, we show that the proposed algorithms outperform the existing state-of-the-art approaches significantly in terms of energy consumption, query time, and network lifetime.