Performance aware tasking for environmentally powered sensor networks
Proceedings of the joint international conference on Measurement and modeling of computer systems
Energy Scavenging for Mobile and Wireless Electronics
IEEE Pervasive Computing
Harvesting aware power management for sensor networks
Proceedings of the 43rd annual Design Automation Conference
Multi-version scheduling in rechargeable energy-aware real-time systems
Journal of Embedded Computing - Real-Time Systems (Euromicro RTS-03)
Active sensing platform for wireless structural health monitoring
Proceedings of the 6th international conference on Information processing in sensor networks
Adaptive power management in energy harvesting systems
Proceedings of the conference on Design, automation and test in Europe
Poster abstract: Energy management in wireless healthcare systems
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
DVFS based task scheduling in a harvesting WSN for structural health monitoring
Proceedings of the Conference on Design, Automation and Test in Europe
Adaptive scheduling of real-time systems cosupplied by renewable and nonrenewable energy sources
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on ESTIMedia'10
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Energy harvesting sensor nodes eliminate the need for post-deployment physical human interaction by using environmental power and wireless communication; however, they must adapt the utility of their tasks to accommodate the energy availability. For example, on sunny days, a solar-powered sensor node can perform highly accurate tasks requiring more extensive computation and communication, but on cloudy days, it must reduce utility due to a decrease in harvested energy. In this paper, we present a controller that uses two algorithms to balance task utility and execution time subject to an energy constraint. One algorithm determines the total execution time of a set of tasks such that desired task utilities are met, while the other solves the converse problem by approximating the maximum task utilities achievable within a global deadline. We apply our methods to a prototype Structural Health Monitoring system, demonstrating the controller's ability to adapt at runtime.