Robust computation of aggregates in wireless sensor networks: Distributed randomized algorithms and analyses

Jen-Yeu Chen, Purdue University

Abstract

A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in a hostile environment that is prone to link and node failures. Consequently, the algorithms developed on a sensor network have to be prudent on energy cost, scalable to network size, and robust to frequent topology changes. Among the operations on a sensor network, computing aggregates such as average, minimum, maximum and sum over the data stored in the sensor nodes is not only an important application in itself but also fundamental to various other functions such as system monitoring, data querying, and collaborative information processing. In this work, we present a class of distributed randomized algorithms to efficiently compute aggregates in a sensor network. The proposed algorithms are energy-efficient, scalable, and robust to frequent topology changes. Our analyses and experimental results show that they outperform other representative distributed algorithms for the aggregates computation in wireless sensor networks.

Degree

Ph.D.

Advisors

Pandurangan, Purdue University.

Subject Area

Engineering|Electrical engineering|Computer science

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