Batteryless sensors promise a sustainable future for sensing, but they face significant challenges when storing and using environmental energy. Incoming energy can fluctuate unpredictably between periods of scarcity and abundance, and device performance depends on both incoming energy and how much a device can store. Existing batteryless devices have used fixed or run-time selectable front-end capacitor banks to meet the energy needs of different tasks. Neither approach adapts well to rapidly changing energy harvesting conditions, nor does it allow devices to store excess energy during times of abundance without sacrificing performance. This article presents Stash, a hardware back-end energy storage technique that allows batteryless devices to charge quickly and store excess energy when it is abundant, extending their operating time and carrying out additional tasks without compromising the main ones. Stash performs like a small capacitor device when small capacitors excel and like a large capacitor device when large capacitors excel, with no additional software complexity and negligible power overhead. We evaluate Stash using two applications—temperature sensing and wearable activity monitoring—under both synthetic solar energy and recorded solar and thermal traces from various human activities. Our results show that Stash increased sensor coverage by up to 15% under variable energy-harvesting conditions when compared to competitor configurations that used fixed small, large, and reconfigurable front-end energy storage.

Designing batteryless sensors presents many challenges, starting with selecting the right components for a particular application. Every new sensor added to a device can be costly, both monetarily and in energy expense. Recent research has begun to look at using the very harvesters that are already on the device as sensors for various applications. Unfortunately, not all harvesters are created equal and it is not as simple as just looking up current standards in a component's datasheet. In this paper, we propose that new metrics for energy harvesters are needed by the community to reduce complexities in the design process of selecting the optimal harvester to use as a sensor. Using a sampling of 9 solar harvesters, we ran 270 experiments to profile and compare how each reacted to a simple motion event. We also propose and explore a few sample metrics useful in selecting solar harvesters as sensors and discuss their potential impact on different applications.

Sensing platforms are becoming batteryless to enable the vision of the Internet of Things, where trillions of devices collect data, interact with each other, and interact with people. However, these batteryless sensing platforms—that rely purely on energy harvesting—are rarely able to maintain a sense of time after a power failure. This makes working with sensor data that is time sensitive especially difficult. We propose two novel, zero-power timekeepers that use remanence decay to measure the time elapsed between power failures. Our approaches compute the elapsed time from the amount of decay of a capacitive device, either on-chip Static Random-Access Memory (SRAM) or a dedicated capacitor. This enables hourglass-like timers that give intermittently powered sensing devices a persistent sense of time. Our evaluation shows that applications using either timekeeper can keep time accurately through power failures as long as 45s with low overhead.

Embedded systems that are wirelessly reprogrammed can be rendered useless by certain programming errors, excessive power consumption, or misconfigurations in the hardware. These types of situations can leave a device in a state that compromises its programmability, often rendering the device useless. Existing attempts to address the problem of robust wireless reprogramming have all been software-based solutions, that are vulnerable to certain errors, such as memory corruption, can corrupt the recovery programs. We propose a hardware-based solution to wireless reprogramming, physically separating the programmer and target device. This separation limits the propagation of errors, and ensures the device will always be recoverable. In this poster we will present the design and an early prototype of our approach -- an ultra-low-power, low-cost hardware solution to ensure recovery from fatal errors and reprogrammability in wireless systems. This poster discusses the current system design, initial results, and system analysis from our current prototype. We also present future and ongoing directions, as well as key research questions. This work was funded by National Science Foundation grants CNS-1314342 and CNS-1453607. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.