Abstract
Renewable solar energy harvesting systems have received considerable attention as a possible substitute for conventional chemical batteries in sensor networks. However, it is difficult to optimize the use of solar energy based only on empirical power acquisition patterns in sensor networks. We apply acquisition patterns from actual solar energy harvesting systems and build a framework to maximize the utilization of solar energy in general sensor networks. To achieve this goal, we develop a cross-layer optimization-based scheduling scheme called binding optimization of duty cycling and networking through energy tracking (BUCKET), which is formulated in four-stages: 1) prediction of energy harvesting and arriving traffic; 2) internode optimization at the transport and network layers; 3) intranode optimization at the medium access control layer; and 4) flow control of generated communication task sets using a token-bucket algorithm. Monitoring of the structural health of bridges is shown to be a potential application of an energy-harvesting sensor network. The example network deploys five sensor types: 1) temperature; 2) strain gauge; 3) accelerometer; 4) pressure; and 5) humidity. In the simulations, the BUCKET algorithm displays performance enhancements of ∼ 12-15% over those of conventional methods in terms of the average service rate.
| Original language | English |
|---|---|
| Article number | 6930731 |
| Pages (from-to) | 1489-1503 |
| Number of pages | 15 |
| Journal | IEEE Sensors Journal |
| Volume | 15 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2015 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Instrumentation
- Electrical and Electronic Engineering
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BUCKET : Scheduling of solar-powered sensor networks via cross-layer optimization. / Lee, Sungjin; Kwon, Beom; Lee, Sanghoon; Bovik, Alan Conrad.
In: IEEE Sensors Journal, Vol. 15, No. 3, 6930731, 01.03.2015, p. 1489-1503.Research output: Contribution to journal › Article
TY - JOUR
T1 - BUCKET
T2 - Scheduling of solar-powered sensor networks via cross-layer optimization
AU - Lee, Sungjin
AU - Kwon, Beom
AU - Lee, Sanghoon
AU - Bovik, Alan Conrad
PY - 2015/3/1
Y1 - 2015/3/1
N2 - Renewable solar energy harvesting systems have received considerable attention as a possible substitute for conventional chemical batteries in sensor networks. However, it is difficult to optimize the use of solar energy based only on empirical power acquisition patterns in sensor networks. We apply acquisition patterns from actual solar energy harvesting systems and build a framework to maximize the utilization of solar energy in general sensor networks. To achieve this goal, we develop a cross-layer optimization-based scheduling scheme called binding optimization of duty cycling and networking through energy tracking (BUCKET), which is formulated in four-stages: 1) prediction of energy harvesting and arriving traffic; 2) internode optimization at the transport and network layers; 3) intranode optimization at the medium access control layer; and 4) flow control of generated communication task sets using a token-bucket algorithm. Monitoring of the structural health of bridges is shown to be a potential application of an energy-harvesting sensor network. The example network deploys five sensor types: 1) temperature; 2) strain gauge; 3) accelerometer; 4) pressure; and 5) humidity. In the simulations, the BUCKET algorithm displays performance enhancements of ∼ 12-15% over those of conventional methods in terms of the average service rate.
AB - Renewable solar energy harvesting systems have received considerable attention as a possible substitute for conventional chemical batteries in sensor networks. However, it is difficult to optimize the use of solar energy based only on empirical power acquisition patterns in sensor networks. We apply acquisition patterns from actual solar energy harvesting systems and build a framework to maximize the utilization of solar energy in general sensor networks. To achieve this goal, we develop a cross-layer optimization-based scheduling scheme called binding optimization of duty cycling and networking through energy tracking (BUCKET), which is formulated in four-stages: 1) prediction of energy harvesting and arriving traffic; 2) internode optimization at the transport and network layers; 3) intranode optimization at the medium access control layer; and 4) flow control of generated communication task sets using a token-bucket algorithm. Monitoring of the structural health of bridges is shown to be a potential application of an energy-harvesting sensor network. The example network deploys five sensor types: 1) temperature; 2) strain gauge; 3) accelerometer; 4) pressure; and 5) humidity. In the simulations, the BUCKET algorithm displays performance enhancements of ∼ 12-15% over those of conventional methods in terms of the average service rate.
UR - http://www.scopus.com/inward/record.url?scp=84919820119&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84919820119&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2014.2363900
DO - 10.1109/JSEN.2014.2363900
M3 - Article
AN - SCOPUS:84919820119
VL - 15
SP - 1489
EP - 1503
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
IS - 3
M1 - 6930731
ER -