Law, Yee Wei and Chatterjea, S. and Jin, Jiong and Hanselmann, T. and Palaniswami, M.
Energy-Efficient Data Acquisition By Adaptive Sampling for Wireless Sensor Networks.
Technical Report TR-CTIT-08-77,
Centre for Telematics and Information Technology University of Twente, Enschede.
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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:||Internal Report (Technical Report)|
|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|
|Deposited On:||09 February 2009|
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