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Issue No. 09 - September (2006 vol. 17)
ISSN: 1045-9219
pp: 987-1000
Jen-Yeu Chen , School of Electrical and Computer Engineering, Purdue University, Box 165, Electrical Engineering Building, West Lafayette, IN 47907
G. Pandurangan , Department of Computer Science, Purdue University, 205 N. University Street, West Lafayette, IN 47907- 2066
Dongyan Xu , Department of Computer Science, Purdue University, 205 N. University Street, West Lafayette, IN 47907- 2066
A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in hostile environments that are prone to link and node failures. Computing aggregates such as average, minimum, maximum and sum is fundamental to various primitive functions of a sensor network, such as system monitoring, data querying, and collaborative information processing. In this paper, we present and analyze a suite of randomized distributed algorithms to efficiently and robustly compute aggregates. Our distributed random grouping (DRG) algorithm is simple and natural and uses probabilistic grouping to progressively converge to the aggregate value. DRG is local and randomized and is naturally robust against dynamic topology changes from link/node failures. Although our algorithm is natural and simple, it is nontrivial to show that it converges to the correct aggregate value and to bound the time needed for convergence. Our analysis uses the eigenstructure of the underlying graph in a novel way to show convergence and to bound the running time of our algorithms. We also present simulation results of our algorithm and compare its performance to various other known distributed algorithms. Simulations show that DRG needs far fewer transmissions than other distributed localized schemes
distributed algorithms, graph theory, probability, randomised algorithms, telecommunication links, telecommunication network topology, wireless sensor networks

Jen-Yeu Chen, G. Pandurangan and Dongyan Xu, "Robust Computation of Aggregates in Wireless Sensor Networks: Distributed Randomized Algorithms and Analysis," in IEEE Transactions on Parallel & Distributed Systems, vol. 17, no. 9, pp. 987-1000, 2008.
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