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Author Moser, Clemens ♦ Chen, Jian-Jia ♦ Thiele, Lothar
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Power management ♦ Embedded systems ♦ Energy harvesting ♦ Model predictive control ♦ Real-time scheduling ♦ Reward maximization
Abstract Energy harvesting (also known as energy scavenging) is the process of generating electrical energy from environmental energy sources. There exists a variety of different energy sources such as solar energy, kinetic energy, or thermal energy. In recent years, this term has been frequently applied in the context of small autonomous devices such as wireless sensor nodes. In this article, a framework for energy management in energy harvesting embedded systems is presented. As a possible scenario, we focus on wireless sensor nodes that are powered by solar cells. We demonstrate that classical power management solutions have to be reconceived and/or new problems arise if perpetual operation of the system is required. In particular, we provide a set of algorithms and methods for various application scenarios, including real-time scheduling, application rate control, as well as reward maximization. The goal is to optimize the performance of the application subject to given energy constraints. Our methods optimize the system performance which, for example, allows the usage of smaller solar cells and smaller batteries. Furthermore, we show how to dimension important system parameters like the minimum battery capacity or a sufficient prediction horizon. Our theoretical results are supported by simulations using long-term measurements of solar energy in an outdoor environment. In contrast to previous works, we present a formal framework which is able to capture the performance, the parameters, and the energy model of various energy harvesting systems. We combine different viewpoints, include corresponding simulation results, and provide a thorough discussion of implementation aspects.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-06-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 6
Issue Number 2
Page Count 21
Starting Page 1
Ending Page 21


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Source: ACM Digital Library