Voronoi trees for hierarchical in-network data and space abstractions in wireless sensor networks

Abstract

We address the problem of spatial queries in Wireless Sensor Networks (WSN) via hybrid overlays, where the data values may correspond to different physical phenomena and sensors may be correlated with spatial constraints. We show how hierarchical data and space abstractions can be used to represent Voronoi Cell based partitions of the sensing field and use Voronoi Trees to efficiently map the hierarchical abstractions for energy-efficient processing. The proposed scheme is simulated on the SidNET, a JiST/SWANS based sensor network simulation platform. The performance results show significant advantages in terms of accurate field representation at different levels of the tree hierarchy with a trade-off in query processing delay.