Abstract

Wireless sensor networks can be regarded as sensor database systems, which permit users to query sensor data of interest. Among various spatial database queries, we focus the area-wise aggregate queries in the region where the sensor values are above a predefined threshold, which are summarized as above-threshold queries. In this paper, we propose a novel Bilinear Interpolation-Based (BIB) algorithm, which utilizes the bilinear interpolation to estimate the environmental variables inside a grid with the known sensor values at the vertexes, to support the above-threshold queries for regularly-deployed sensor networks and provide the closed-form solution of the above-threshold ratio. We designate experiments with both the artificially-constructed environment data and the real temperature data. Experiment results manifest that the proposed BIB algorithm shows a good performance in estimating the above-threshold ratios to support the above-threshold queries in an accurate and efficient manner.

Highlights

  • Wireless sensor networks are sharply emerging as one of the most significant technologies to bridge the gap between the physical world and the digital world [1,2,3]

  • We propose a novel Bilinear Interpolation-Based (BIB) algorithm to support the above-threshold queries for regularly-deployed sensor networks

  • We utilize the bilinear interpolation to estimate the environmental variables inside a square with the known sensor values from sensor nodes located at its four vertexes

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Summary

Introduction

Wireless sensor networks are sharply emerging as one of the most significant technologies to bridge the gap between the physical world and the digital world [1,2,3]. In order to sense spatial natural phenomena in the physical world remotely, wireless sensor networks usually consist of many smart energy-efficient devices, namely sensor nodes that are equipped with inexpensive embedded sensors, processors, memories, radio communication modules, and power supplies to gather environmental conditions, ranging from temperature, precipitation, smoke, soil humidity to atmospheric pressure, to name a few. Wireless sensor networks provide us prolific spatiotemporal environment data and will play important roles in many applications, including environmental monitoring, target tracking, precision agriculture, and so forth [4,5,6,7,8,9]. Various methods and techniques of data storage and data query have been proposed, which permit users to manage sensor data of interest [11,12]

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