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Journal of Sensors
Volume 2015 (2015), Article ID 506909, 17 pages
http://dx.doi.org/10.1155/2015/506909
Research Article

Minimum Cost Data Aggregation for Wireless Sensor Networks Computing Functions of Sensed Data

1Department of Computer and Communications, Korea University, Seoul 136-701, Republic of Korea
2Department of Digital Contents Convergence, Seoul National University, Seoul 151-742, Republic of Korea
3Department of Computer Engineering, Hongik University, Seoul 121-791, Republic of Korea

Received 11 December 2014; Accepted 12 January 2015

Academic Editor: Yun Liu

Copyright © 2015 Chao Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We consider a problem of minimum cost (energy) data aggregation in wireless sensor networks computing certain functions of sensed data. We use in-network aggregation such that data can be combined at the intermediate nodes en route to the sink. We consider two types of functions: firstly the summation-type which includes sum, mean, and weighted sum, and secondly the extreme-type which includes max and min. However for both types of functions the problem turns out to be NP-hard. We first show that, for sum and mean, there exist algorithms which can approximate the optimal cost by a factor logarithmic in the number of sources. For weighted sum we obtain a similar result for Gaussian sources. Next we reveal that the problem for extreme-type functions is intrinsically different from that for summation-type functions. We then propose a novel algorithm based on the crucial tradeoff in reducing costs between local aggregation of flows and finding a low cost path to the sink: the algorithm is shown to empirically find the best tradeoff point. We argue that the algorithm is applicable to many other similar types of problems. Simulation results show that significant cost savings can be achieved by the proposed algorithm.