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Energy-aware sparse approximation technique (east) for rechargeable wireless sensor networks (2010)
| Venue: | Wireless Sensor Networks |
| Citations: | 11 - 5 self |
BibTeX
@INPROCEEDINGS{Rana10energy-awaresparse,
author = {Rajib Rana and Wen Hu and Chun Tung Chou},
title = {Energy-aware sparse approximation technique (east) for rechargeable wireless sensor networks},
booktitle = {Wireless Sensor Networks},
year = {2010},
pages = {306--321}
}
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Abstract
Abstract. Due to non-homogeneous spread of sunlight, sensing nodes typically have non-uniform energy profiles in rechargeable Wireless Sen-sor Networks (WSNs). An energy-aware work load distribution is there-fore necessary for good data accuracy while ensuring an energy-neutral operation. Recently proposed signal approximation strategies, in form of Compressive Sensing, assume uniform sampling and thus cannot be de-ployed to facilitate energy neutral operation in rechargeable WSNs. We propose a sparse approximation driven sensing technique (EAST) that adapts sensor node sampling workload according to solar energy avail-ability. To the best of our knowledge, we are the first to propose sparse approximation for modeling energy-aware work load distribution in order to improve signal approximation from rechargeable WSNs. Experimental result, by using data from an outdoor WSN deployment, suggests that EAST significantly improves the approximation accuracy while support-ing approximately 50 % higher sensor on-time compared to an approach that assumes uniform energy profile of the nodes. 1
Keyphrases
energy-aware sparse approximation technique rechargeable wireless sensor network energy-aware work load distribution sparse approximation rechargeable wsns rechargeable wireless sen-sor network non-uniform energy profile non-homogeneous spread signal approximation energy-neutral operation signal approximation strategy energy neutral operation uniform sampling good data accuracy solar energy avail-ability outdoor wsn deployment compressive sensing uniform energy profile experimental result approximation accuracy
