Public testbeds are essential for replicable experiments and meaningful comparisons on shared physical infrastructure. While many testbeds exist for battery-powered Internet of Things (IoT) systems, there is a lack of public testbeds for observing and profiling the distributed operation of energy-harvesting IoT systems, including battery-free devices. We fill this gap and present Shepherd Nova, the first public testbed designed to support experiments under repeatable energy-harvesting conditions. Shepherd Nova uses field-recorded harvesting data to supply power to devices, consistently replicating real-world spatio-temporal energy availability across multiple experiments. Its virtual power source supports diverse ambient energy sources, harvesting circuitry, and energy storage devices. Moreover, Shepherd Nova provides services like general-purpose input/output (GPIO) tracing, power profiling, and serial output logging, all of which can run synchronously and with high resolution. Sub-microsecond synchronization enables precise correlation between these observations and emulated energy-harvesting conditions, offering unprecedented insights into distributed energy-harvesting IoT systems. In this paper, we describe Shepherd Nova's design, characterize its performance, and demonstrate its capabilities through controlled experiments and an example test case. To access the testbed, documentation as well as open-source harvesting data, hardware designs, and code, visit https://testbed.nes-lab.org/.

The rapidly growing Internet of Things (IoT) can avoid the high cost and environmental burden of replacing trillions of batteries by using sustainable battery-free devices that operate maintenance-free for decades. To develop battery-free IoT systems, researchers and makers require a common platform that is versatile, affordable, and easy to use. However, limited availability and lack of support have prevented widespread adoption of previous battery-free platforms. We introduce Riotee, an open-source and commercially available battery-free platform that includes multiple boards, extensive software, and comprehensive documentation. We demonstrate Riotee's capabilities through a machine-learning application and present results from a user study involving students and customers, who rated its usefulness and usability highly.

This demo uses a machine-learning-based hot word detection application to showcase the capabilities of Riotee, an open-source and commercially available hardware-software platform for the battery-free Internet of Things. We describe the Riotee hardware consisting of a base module, a debug probe for easy firmware updates, and several expansion boards that enhance functionality without the need for custom-designed printed circuit boards (PCBs). The demo features the classification of live audio recordings using TinyML deep neural network inference aboard a Riotee device. The Riotee device transmits the classification results via Bluetooth Low Energy (BLE) to a smartphone given to visitors. Visitors can also observe how the Riotee software checkpoints and restores critical state in case of power failures via the visualization of logic analyzer traces.

Battery-free devices eliminate the need for batteries, which are expensive, environmentally harmful, and require frequent replacement, thus reducing waste and making devices more cost-effective. We introduce Riotee, the next-generation platform for the battery-free Internet of Things. The platform comprises a base module, a debug probe that allows to conveniently update the firmware on the base module, and a number of expansion boards that extend the capabilities of the platform without the need to design a custom printed circuit board (PCB). We provide a brief overview of Riotee, and describe a demo setup that showcases the key functionality and how to get started with the platform in less than three minutes.

The high power consumption of inertial activity sensors limits the battery lifetime of today's wearable devices. Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements into electrical energy while using the harvesting signal to replace conventional activity sensors. However, in human-centric applications, the amount of harvested kinetic energy is not enough to power a real-time activity recognition algorithm and run the wearable device perpetually. In this paper, we propose Solar based human Activity Recognition (SolAR), which uses solar cells simultaneously as an activity sensor as well as an energy source. Our key observation is that the power available from a wrist-worn solar cell changes dynamically while a person moves, encoding information about the underlying activity. We collect empirical solar energy data to explore its activity sensing potential and implement the activity recognition pipeline on an ultra low-power micro-controller unit to evaluate the end-to-end power consumption of the system. Our analysis reveals that SolAR improves activity recognition accuracy by up to 8.3% and harvests more than one order of magnitude higher power compared to its kinetic counterpart. This enables SolAR to generate more energy than required for the entire activity recognition pipeline, which we term as energy positive activity recognition, achieving uninterrupted, autonomous, self-powered and real-time operation.

Communication among batteryless devices is key to their success in replacing traditional battery-supported systems. However, low and unpredictable availability of ambient energy combined with limited energy storage capacity of the devices make efficient communication challenging. As a stepping stone toward addressing this challenge, we propose to leverage common patterns in harvested energy across the devices. In this abstract, we explore one possible approach that exploits a property of many fluorescent light sources used worldwide: their brightness changes with double the power line frequency. We design a circuit that transforms the corresponding changes in energy harvested with a solar panel into a digital signal that is frequency- and phase-synchronized across multiple devices. Based on our design, we build a novel batteryless node, called Flync. Using two Flync nodes, we demonstrate that the synchronized signal can be generated with less than 1 µA and a maximum measured node to node jitter of 363.24 µs.

Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.

Energy-efficient location tracking with battery-powered devices using energy harvesting necessitates duty-cycling of GPS to prolong the system lifetime. We propose an energy and mobility-aware scheduling framework that adapts to real-world dynamics to achieve optimal long-term tracking performance. To forecast energy, the framework uses an exponentially weighted moving average filter to compute a virtual energy budget for the remainder of the forecast period. The virtual energy budget is then used as input for our proposed information-based GPS sampling approach, which estimates the current tracking error through dead-reckoning and schedules a new GPS sample when the error exceeds a given threshold. In order to improve the long-term tracking performance, the threshold is adapted based on the current energy and movement trends to balance the expected information gain from a new GPS sample with its energy cost. We evaluate our approach on empirical traces from wild flying foxes and compare it to strategies that sample GPS using fixed and adaptive duty cycles and by using dead-reckoning with a fixed threshold. Our analysis shows that the proposed information-based GPS sampling strategy reduces the mean tracking error compared to existing methods and approaches the performance of the optimal offline sampling strategy.

Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management algorithms do not exploit prior knowledge of these variations for making better decisions. This paper presents PreAct, the first energy-management algorithm that exploits time-varying utility to optimize application performance. PreAct's design combines strategic long-term planning of future energy utilization with feedback control to compensate for deviations from the expected conditions. We implement PreAct on a low-power microcontroller and compare it against the state of the art on multiple years of real-world data. Our results demonstrate that PreAct is up to 53 % more effective in utilizing harvested solar energy and significantly more robust to uncertainties and inefficiencies of practical systems. These gains translate into an improvement of 28% in the end-to-end performance of a real-world application we investigate when using PreAct.

Collaboration of batteryless nodes is essential to their success in replacing traditional battery-based systems. Energy-harvesting sensor nodes experience spatio-temporal fluctuations of energy availability. These fluctuations become especially critical when sensor nodes do not have sufficient energy storage to compensate for them. Understanding the challenges and opportunities of operating groups of batteryless sensor nodes requires to record and reproduce spatio-temporal characteristics of real energy environments. We thus present Shepherd, a testbed for the batteryless IoT. Shepherd allows to record synchronized energy traces with a resolution of 3 μA and 50μV at a rate of 100 kHz, and to faithfully replay these traces to any number of sensor nodes to study their behavior. We release Shepherd as an open-source tool for the community, facilitating research into time synchronization, wireless networking, and other distributed algorithms for batteryless systems.

Collaboration of batteryless nodes is essential to their success in replacing traditional battery-based systems. This abstract describes a demonstration of the recently proposed Shepherd testbed that allows to record and reproduce spatio-temporal characteristics of real energy environments. It consists of a number of spatially distributed Shepherd nodes that are tightly time-synchronized with each other and record synchronized energy traces with a resolution of 3 μA and 50 μV at a rate of 100 kHz. Additionally, Shepherd can faithfully replay these traces to any number of nodes to study their behavior, both individually and as an ensemble. Shepherd works with various sources of energy harvesting, such as kinetic or solar, is based on a modular design and provides a generic interface for sensor nodes allowing users to experiment with new platforms.

Sensor networks increasingly rely on harvesting energy from the environment to sense, process, and transmit data. Online energy availability forecasting and energy management are critical to ensure long-term energy-neutral operation of battery-powered energy-harvesting sensor nodes. Existing methods focus on applications with time-invariant utility and custom-tailored hardware platforms, which limits their effectiveness across diverse application domains, different platforms, and in the face of aging hardware components. To address these limitations, we formulate an optimisation problem with respect to time-varying utility under the given hardware constraints. We also present PREACT, an online energy-management algorithm that approximates the optimal solution to the optimisation problem by incorporating long-term energy forecasting.