Optimal service level allocation in environmentally powered embedded systems
Proceedings of the 2009 ACM symposium on Applied Computing
Power management in energy harvesting embedded systems with discrete service levels
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
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
Dynamic power management in environmentally powered systems
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
A real-time scheduling framework for embedded systems with environmental energy harvesting
Computers and Electrical Engineering
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
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Renewable energies can enable embedded systems to be functional indefinitely. In particular for small autonomous sensors, energy harvesting techniques have attracted much interest. This paper considers systems which provide services periodically with adjustable quality evaluated in terms of rewards. The reward garnered for one service is monotonically increasing and strictly concave with respect to the energy consumption of the service. There exist two major constraints which arise due to the burstiness of common energy sources: (1) The harvested energy is temporarily low and the service must be lowered or suspended. (2) During bursts, the harvested energy exceeds the battery capacity. To resolve these issues, we propose algorithms to derive optimal solutions which maximize the overall reward. Furthermore, we determine the minimum battery capacity necessary to optimally exploit a given power source. By applying real data recorded for photovoltaic cells as the harvested energy, simulations illuminate the merits of our algorithms.