Adaptive duty cycling for energy harvesting systems

Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy ava...

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Vydané v:ISLPED '06 : proceedings of the 2006 International Symposium on Low Power Electronics and Design, Tegernsee, Germany, October 4-6, 2006 s. 180 - 185
Hlavní autori: Hsu, Jason, Zahedi, Sadaf, Kansal, Aman, Srivastava, Mani, Raghunathan, Vijay
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 04.10.2006
IEEE
Edícia:ACM Conferences
Predmet:
Algorithms
Availability
Computer systems organization > Architectures > Distributed architectures
Design
Energy consumption
Energy harvesting
Energy management
energy neutral operation
Environmental management
low power design
Predictive models
Prototypes
Runtime environment
Software and its engineering > Software organization and properties > Software system structures > Distributed systems organizing principles
Solar energy
System performance
Upper bound
energy harvesting
energy neutral operation
low power design
ISBN:9781595934628, 1595934626
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Shrnutí:Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment, (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint, and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used.
Bibliografia:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781595934628
1595934626
DOI:10.1145/1165573.1165616