Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Authors:
Song Lin;Benjamin Arai;Dimitrios Gunopulos;Gautam Das
Affiliations:
Department of Computer Science and Engineering, University of California, Riverside. slin@cs.ucr.edu;Department of Computer Science and Engineering, University of California, Riverside. barai@cs.ucr.edu;Department of Computer Science and Engineering, University of California, Riverside/ Department of Informatics, University of Athens. dg@cs.ucr.edu;Department of Computer Science and Engineering, The University of Texas at Arlington. gdas@cse.uta.edu
Venue:
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Year:
2008

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Abstract

Satisfying energy constraints while meeting performance requirements is a primary concern when a sensor network is being deployed. Many recent proposed techniques offer error bounding solutions for aggregate approximation but cannot guarantee energy spending. Inversely, our goal is to bound the energy consumption while minimizing the approximation error. In this paper, we propose an online algorithm, Region Sampling, for computing approximate aggregates while satisfying a pre-defined energy budget. Our algorithm is distinguished by segmenting a sensor network into partitions of non-overlapping regions and performing sampling and local aggregation for each region. The sampling energy cost rate and sampling statistics are collected and analyzed to predict the optimal sampling plan. Comprehensive experiments on real-world data sets indicate that our approach is at a minimum of 10% more accurate compared with the previously proposed solutions.