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Real-time scheduling for energy harvesting sensor nodes

Moser, Clemens ; Brunelli, Davide ; Thiele, Lothar ; Benini, Luca

In: Real-Time Systems, 2007, vol. 37, no. 3, p. 233-260

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    Titre
    • Real-time scheduling for energy harvesting sensor nodes
    Auteur
    • Moser, Clemens. Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
    • Brunelli, Davide. Department of Electronics, Computer Science and Systems, University of Bologna, Bologna, Italy
    • Thiele, Lothar. Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
    • Benini, Luca. Department of Electronics, Computer Science and Systems, University of Bologna, Bologna, Italy
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Real-Time Systems, 2007, vol. 37, no. 3, p. 233-260. Springer US; http://www.springer-ny.com
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:315532
    Summary
    Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if real-time responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First scheduling