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ABSTRACT

In highly power constrained sensor networks, harvesting energy from the environment makes prolonged or even perpetual execution feasible. In such energy harvesting systems, energy sources are characterized as being regenerative. Regenerative energy sources fundamentally change the problem of power scheduling for embedded devices. Instead of the problem being one of maximizing the lifetime of the system given a total amount of energy, as in traditional battery powered devices, the problem becomes one of preventing energy depletion at any given time.

Coupling relatively computationally intensive applications, such as video processing applications, with the constrained FPGAs that are feasible on power constrained embedded systems, makes dynamic reconfiguration essential. It provides the speed comparable to a hardware implementation, but it also allows the dynamic reconfiguration to meet the multiple application needs of the system. Different applications can be loaded on the FPGA, as the system's needs change over time. The problem becomes how to schedule the dynamic reconfiguration to appropriately make use of the regenerative energy source, to ensure the proper availability of energy for the system over time.

In this paper, we present a methodology for carrying out dynamic reconfiguration for regenerative energy sources, based on statistical analysis of tasks and supply energy. The approach is evaluated through extensive simulations. Additionally, we have evaluated our implementation on our regenerative energy, dynamically reconfigurable prototype, known as the MicrelEye. Our approach is shown to miss 57.7% less deadlines on average than the current approach for reconfiguration with regenerative energy sources.

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