A key challenge to the future of energy-harvesting systems is the discontinuous power supply that is often generated. We propose a new approach, Hibernus, which enables computation to be sustained during intermittent supply. The approach has a low energy and time overhead which is achieved by reactively hibernating: saving system state only once, when power is about to be lost, and then sleeping until the supply recovers. We validate the approach experimentally on a processor with FRAM nonvolatile memory, allowing it to reactively hibernate using only energy stored in its decoupling capacitance. When compared to a recently proposed technique, the approach reduces processor time and energy overheads by 76%-100% and 49%-79% respectively.

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass, and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This paper proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over state-of-the-art approaches.

Supercapacitors are often used in energy harvesting wireless sensor nodes (EH-WSNs) to store harvested energy. Until now, research into the use of supercapacitors in EH-WSNs has considered them to be ideal or oversimplified, with non-ideal behavior attributed to substantial leakage currents. In this brief, we show that observations previously attributed to leakage are predominantly due to redistribution of charge inside the supercapacitor. We confirm this hypothesis through the development of a circuit-based model, which accurately represents non-ideal behavior. The model correlates well with practical validations representing the operation of an EH-WSN and allows behavior to be simulated over long periods.

Energy harvesting allows low-power embedded devices to be powered from naturally-ocurring or unwanted environmental energy (e.g. light, vibration, or temperature difference). While a number of systems incorporating energy harvesters are now available commercially, they are specific to certain types of energy source. Energy availability can be a temporal as well as spatial effect. To address this issue, 'hybrid' energy harvesting systems combine multiple harvesters on the same platform, but the design of these systems is not straightforward. This paper surveys their design, including trade-offs affecting their efficiency, applicability, and ease of deployment. This survey, and the taxonomy of multi-source energy harvesting systems that it presents, will be of benefit to designers of future systems. Furthermore, we identify and comment upon the current and future research directions in this field.