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Energy harvesting is generally seen to be the key to power cyber-physical systems in a low-cost, long term, efficient manner. However, harvesting has traditionally been coupled with large energy storage devices to mitigate the effects of the source's variability. The emerging class of transiently powered systems avoids this issue by performing computation only as a function of the harvested energy, minimizing the obtrusive and expensive storage element. In this work, we present an efficient Energy Management Unit (EMU) to supply generic loads when the average harvested power is much smaller than required for sustained system operation. By building up charge to a pre-defined energy level, the EMU can generate short energy bursts predictably, even under variable harvesting conditions. Furthermore, we propose a dynamic energy burst scaling (DEBS) technique to adjust these bursts to the load's requirements. Using a simple interface, the load can dynamically configure the EMU to supply small bursts of energy at its optimal power point, independent from the harvester's operating point. Extensive theoretical and experimental data demonstrate the high energy efficiency of our approach, reaching up to 73.6% even when harvesting only 110 µW to supply a load of 3.89 mW.

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With the appearance of wearable devices and the IoT, energy harvesting nodes are becoming more and more important. The design and evaluation of these small standalone sensors and actuators, which harvest limited amounts of energy, requires novel tools and methods. Fast and accurate measurement systems are required to capture the rapidly changing harvesting scenarios and characterize leakage currents and energy efficiencies. The need for real-world experiments creates a demand for compact and portable equipment to perform in-situ power measurements and environmental logging. This work presents the ROCKETLOGGER, a hand-held measurement device that combines both properties: portability and accuracy. The custom analog front-end allows logging at sampling rates up to 64 kSPS. The fast range switching within 1.4 µs guarantees continuous power measurements starting from 4 pW at 1 mV up to 2.75 W at 5.5 V. The software provides remote control and manages data acquisition of up to 13 Mb/ sec in real-time. We extensively characterize the ROCKETLOGGER's performance, demonstrate the need for its properties in three use-cases at different stages of the system design flow, and show its advantages in measuring and validating new harvesting-driven devices for the IoT.

In this work, we present a formal study on optimizing the energy consumption of energy harvesting embedded systems. To deal with the uncertainty inherent in solar energy harvesting systems, we propose the Stochastic Power Management (SPM) scheme, which builds statistical models of harvested energy based on historical data. The proposed stochastic scheme maximizes the lowest energy consumption across all time intervals while giving strict probabilistic guarantees on not encountering battery depletion. For situations where historical data is not available, we propose the use of (i) a Finite Horizon Control (FHC) scheme and (ii) a non-uniformly scaled energy estimator based on an astronomical model, which is used by FHC. Under certain realistic assumptions, the FHC scheme can provide guarantees on minimum energy usage that can be supported over all times. We further propose and evaluate a piece-wise linear approximation of FHC for efficient implementation in resource-constrained embedded systems. With extensive experimental evaluation for eight publicly available datasets and two datasets collected with our own deployments, we quantitatively establish that the proposed solutions are highly effective at providing a guaranteed minimum service level and significantly outperform existing solutions.