Energy Efficient Spatial Query Processing in Wireless Sensor Networks

TL;DR: This work proposes semi-distributed spatial query indexing structure that disseminates a query into the network and retrieves data energy efficiently using a localized tree building algorithm, and proposes a sectioned tree index, which divides the network area into several squares and each square has a local index subtree organized within that square.

Abstract: Because a sensor network depends on limited battery power, energy saving is important to increase the sensor network lifespan. We propose semi-distributed spatial query indexing structure that disseminates a query into the network and retrieves data energy efficiently using a localized tree building algorithm. We also propose a sectioned tree index, which divides the network area into several squares and each square has a local index subtree organized within that square. Local trees are interconnected to form one big tree in the network. Local trees are also built based on any algorithm that is energy consumption aware at each sub-root node in a locally centralized way. We use an existing two dimensional indexing technique for energy efficient query dissemination. We show that our proposed scheme is energy efficient for query and data processing heuristically. Our proposed scheme, sectioned tree, is finally simulated in sparse and dense networks to show the energy saving for query and data processing in the sensor network.