Introduction to algorithms
Algorithms for Scheduling Imprecise Computations
Computer - Special issue on real-time systems
Algorithms for scheduling imprecise computations with timing constraints
SIAM Journal on Computing
IEEE Transactions on Computers
Maximizing the System Value while Satisfying Time and Energy Constraints
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Improving trace cache hit rates using the sliding window fill mechanism and fill select table
MSP '04 Proceedings of the 2004 workshop on Memory system performance
Voltage Scaling Scheduling for Periodic Real-Time Tasks in Reward Maximization
RTSS '05 Proceedings of the 26th IEEE International Real-Time Systems Symposium
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Design considerations for solar energy harvesting wireless embedded systems
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Perpetual environmentally powered sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Everlast: long-life, supercapacitor-operated wireless sensor node
Proceedings of the 2006 international symposium on Low power electronics and design
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Real-time scheduling for energy harvesting sensor nodes
Real-Time Systems
Robust and low complexity rate control for solar powered sensors
Proceedings of the conference on Design, automation and test in Europe
Energy aware dynamic voltage and frequency selection for real-time systems with energy harvesting
Proceedings of the conference on Design, automation and test in Europe
Reward Maximization for Embedded Systems with Renewable Energies
RTCSA '08 Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
An energy management framework for energy harvesting embedded systems
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
DVFS based task scheduling in a harvesting WSN for structural health monitoring
Proceedings of the Conference on Design, Automation and Test in Europe
Dynamic power management in environmentally powered systems
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Micro-scale energy harvesting: a system design perspective
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
Maximum utility rate allocation for energy harvesting wireless sensor networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Harvesting-aware power management for real-time systems with renewable energy
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Personal and Ubiquitous Computing
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Power management has been a critical issue in the design of embedded systems due to the limited power supply. To prolong the lifetime, energy minimization has been studied under performance constraints in the past decade. The emerging embedded systems with the capability to harvest energy from the environment have recently triggered the revision of power management to improve the quality of service dynamically. As the available power/energy of an electronic device changes over time and is limited by many environmental factors, the system has to decide when to change to which service level to provide better quality of service without wasting the harvested energy. In this paper, we explore how to maximize the quality of service, in terms of system rewards, of a periodic application with discrete levels in an energy harvesting system. To decide service levels in a time horizon, this paper presents algorithms to derive optimal solutions if the future harvested energy is known. In addition, we present efficient algorithms to derive near-optimal solutions approximately. Our work is supported by simulation results which are based on long-term measurements of the power generated by real solar cells.