Optimal sampling strategy enabling energy-neutral operations at rechargeable wireless sensor networks

Article

Rana, Rajib, Hu, Wen and Chou, Chun Tung. 2015. "Optimal sampling strategy enabling energy-neutral operations at rechargeable wireless sensor networks." IEEE Sensors Journal. 15 (1), pp. 201-208. https://doi.org/10.1109/JSEN.2014.2337334
Article Title

Optimal sampling strategy enabling energy-neutral operations at rechargeable wireless sensor networks

ERA Journal ID4437
Article CategoryArticle
AuthorsRana, Rajib (Author), Hu, Wen (Author) and Chou, Chun Tung (Author)
Journal TitleIEEE Sensors Journal
Journal Citation15 (1), pp. 201-208
Number of Pages8
Year2015
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1530-437X
1558-1748
Digital Object Identifier (DOI)https://doi.org/10.1109/JSEN.2014.2337334
Web Address (URL)http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6851118
Abstract

Due to nonhomogeneous spread of sunlight, sensing nodes possess nonuniform energy budget in rechargeable wireless
sensor networks. An energy-aware workload distribution strategy is therefore necessary to achieve good data accuracy subject to energy-neutral operation. Our previously proposed energy aware sparse approximation technique (EAST) can approximate a signal, by adapting sensor node sampling workload according to solar energy availability. However, the major shortcoming of EAST is that it does not guarantee an optimal sensing strategy.
In other words, EAST offers energy neutral operation, however it does not offer the best utilization of sensor node energy, which compromises the reconstruction accuracy. In order to overcome this shortcoming, we propose EAST+, which maximizes the reconstruction accuracy subject to energy neutral operations. We also propose a distributed algorithm for EAST+, which offers accurate signal reconstruction with limited node-to-base communications.

Keywordsrechargeable wireless sensor networks; sparse approximation; energy-aware sensing; energy-neutral operations
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Byline AffiliationsInstitute for Resilient Regions
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
University of New South Wales
Institution of OriginUniversity of Southern Queensland
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