Abstract

The skyline query processing technique plays an increasingly important role for multicriteria decision making applications in wireless sensor networks. The technique of saving energy to prolong the lifetime of sensor nodes is one of the dominating challenges to resource-constrained wireless sensor networks. In this paper, we propose an energy-efficient skyline query processing algorithm, called the histogram filter based algorithm (HFA), to efficiently retrieve skyline results from a sensor network. First, we use historical data at the base station to construct histograms for further estimating the probability density distributions of the sensor data. Second, the dominance probability of each tuple is computed based on the histograms, and the optimal tuple which has the largest possibility of dominance/filtering capability is obtained using in-network aggregation approach. After that, the base station broadcasts the optimized tuple as the global filter to each sensor node. Then, the tuples which do not satisfy the skyline query semantics are discarded to avoid unnecessary data transmissions. An extensive experimental study demonstrates that the proposed HFA algorithm performs more efficiently than existing algorithms on reducing data transmissions during skyline query processing, which saves the energy and prolongs the lifetime of wireless sensor networks.

Highlights

  • Rapid advances of embedded systems, sensing, and wireless communication technologies have fostered the developments of wireless sensor networks

  • Suppose all tuples are in the sliding window, and we will use the filter-based approach (FA) algorithm and minscore filter tuple (MFT)-applied aggregation approach (MFTA) algorithm to compute the skyline of given tuples

  • The reason is that the increase of dimension adds the probability of two tuples that are not dominated by each other in anticorrelated distribution data, which makes the size of the skyline set larger

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Summary

Introduction

Rapid advances of embedded systems, sensing, and wireless communication technologies have fostered the developments of wireless sensor networks. The sensor nodes perceive, gather, and process information of monitoring area, such as light, temperature, and humidity, and transmit the information to remote users via wireless communication. Wireless sensor networks have been widely applied to many fields, such as defense military, environmental monitoring, and traffic management. The sensor nodes are generally battery powered and have limited energy. Sensor nodes are often deployed in harsh environments, which makes changing batteries unpractical. Applications over wireless sensor networks need an energy-efficient method to process data gathered by sensor nodes [1]

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