HOLLOWS: A Power-aware Task Scheduler for Energy Harvesting Sensor Nodes

Energy harvesting sensor nodes (EHSNs) have stringent low-energy consumption requirements, but they need to concurrently execute several types of tasks (processing, sensing, actuation, etc.). Furthermore, no accurate models exist to predict the energy harvesting income in order to adapt at run-time...

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Bibliographic Details
Published in:Journal of intelligent material systems and structures 2010-09, Vol.21 (13), p.1317-1335
Main Authors: Piorno, Joaquín Recas, Bergonzini, Carlo, Atienza, David, Rosing, Tajana Simunic
Format: Article
Language:English
Subjects:
Accuracy
Applied sciences
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Dynamical systems
Dynamics
Energy use
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
General equipment and techniques
Harvesting
Income
Instruments, apparatus, components and techniques common to several branches of physics and astronomy
Mathematical models
Measurement and testing methods
Physics
Sensors
Software
Solid mechanics
Structural and continuum mechanics
Tasks
Transducers
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Summary:Energy harvesting sensor nodes (EHSNs) have stringent low-energy consumption requirements, but they need to concurrently execute several types of tasks (processing, sensing, actuation, etc.). Furthermore, no accurate models exist to predict the energy harvesting income in order to adapt at run-time the executing set of prioritized tasks. In this article, we propose a novel power-aware task scheduler for EHSNs, namely, HOLLOWS: Head-of-Line Low-Overhead Wide-priority Service. HOLLOWS uses an energy-constrained prioritized queue model to describe the residence time of tasks entering the system and dynamically selects the set of tasks to execute, according to system accuracy requirements and expected energy. Moreover, HOLLOWS includes a new energy harvesting prediction algorithm, that is, weather-conditioned moving average (WCMA), which we have developed to estimate the solar panel energy income. We have tested HOLLOWS using the real-life working conditions of Shimmer, a sensor node for structural health monitoring. Our results indicate that HOLLOWS accurately predicts the energy available in Shimmer to guarantee a certain damage monitoring quality for long-term autonomous scenarios. Also, HOLLOWS is able to adjust the use of the incoming energy harvesting to achieve high accuracy for rapid event damage assessment (after earthquakes, fires, etc.).
ISSN:1045-389X
1530-8138
DOI:10.1177/1045389X10377033