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16581 Energy-efficient data acquisition by adaptive sampling for wireless sensor networks
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Law, Yee Wei and Chatterjea, S. and Jin, Jiong and Hanselmann, T. and Palaniswami, M. (2009) Energy-efficient data acquisition by adaptive sampling for wireless sensor networks. In: Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly, 21-24 Jun 2009, Leipzig, Germany. pp. 1146-1151. ACM. ISBN 978-1-60558-569-7

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Official URL: http://doi.acm.org/10.1145/1582379.1582631

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Abstract

Wireless sensor networks (WSNs) are well suited for environment monitoring. However, some highly specialized sensors (e.g. hydrological sensors) have high power demand, and without due care, they can exhaust the battery supply quickly. Taking measurements with this kind of sensors can also overwhelm the communication resources by far. One way to reduce the power drawn by these high-demand sensors is adaptive sampling, i.e., to skip sampling when data loss is estimated to be low. Here, we present an adaptive sampling algorithm based on the Box-Jenkins approach in time series analysis. To measure the performance of our algorithms, we use the ratio of the reduction factor to root mean square error (RMSE). The rationale of the metric is that the best algorithm is the algorithm that gives the most reduction in the amount of sampling and yet the the smallest RMSE. For the datasets used in our simulations, our algorithm is capable of reducing the amount of sampling by 24% to 49%. For seven out of eight datasets, our algorithm performs better than the best in the literature so far in terms of the reduction/RMSE ratio.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:SENSEI: Integrating the PhySical with the Digital World of the Network of the Future
Uncontrolled Keywords:Adaptive sampling, Box-Jenkins, ARIMA, wireless sensor network
ID Code:16581
Status:Published
Deposited On:01 December 2009
Refereed:Yes
International:Yes
More Information:statisticsmetis

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