Balanced energy allocation scheme for a solar-powered sensor system and its effects on network-wide performance
- Authors
- Noh, Dong Kun; Kang, Kyungtae
- Issue Date
- Sep-2011
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Keywords
- Solar energy; Sensor system; Energy allocation; Sensor network; Network performance
- Citation
- JOURNAL OF COMPUTER AND SYSTEM SCIENCES, v.77, no.5, pp.917 - 932
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF COMPUTER AND SYSTEM SCIENCES
- Volume
- 77
- Number
- 5
- Start Page
- 917
- End Page
- 932
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/37204
- DOI
- 10.1016/j.jcss.2010.08.008
- ISSN
- 0022-0000
- Abstract
- Solar power can extend the lifetime of wireless sensor networks (WSNs), but it is a very variable energy source. In many applications for WSNs, however, it is often preferred to operate at a constant quality level rather than to change application behavior frequently. Therefore, a solar-powered node is required adaptation to a highly varying energy supply. Reconciling a varying supply with a fixed demand requires a good prediction of that supply, so that demand can be regulated accordingly. We describe two energy allocation schemes, based on time-slots, which aim at optimum use of the periodically harvested solar energy, while minimizing the variability in energy allocation. The simpler scheme is designed for resource-constrained sensors; and a more accurate approach is designed for sensors with a larger energy budget. Each of these schemes uses a probabilistic model based on previous observation of harvested solar energy. This model takes account of long-term trends as well as temporary fluctuations of right levels. Finally, this node-level energy optimization naturally leads to the improvement of the network-wide performance such as latency and throughput. The experimental results on our testbeds and simulations show it clearly. (C) 2010 Elsevier Inc. All rights reserved.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF COMPUTING > DEPARTMENT OF ARTIFICIAL INTELLIGENCE > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.