Josiah D. Hester

Amulet: An Energy-Efficient, Multi-Application Wearable Platform

Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage. We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime.

Tragedy of the Coulombs: Federating Energy Storage for Tiny, Intermittently-Powered Sensors

Untethered sensing devices have, for decades, powered all system components (processors, sensors, actuators, etc) from a single shared energy store (battery or capacitor). When designing batteryless sensors that are powered by harvested energy, this traditional approach results in devices that charge slowly and that are more error prone, inflexible, and inefficient than they could be. This paper presents a novel federated approach to energy storage, called UFoP, that partitions and prioritizes harvested energy automatically into multiple isolated smaller energy stores (capacitors). UFoP simplifies task scheduling, enables efficient use of components with differing voltage requirements, and produces devices that charge more quickly under identical harvesting conditions than a traditional centralized approach. We have implemented a UFoP reference design and conducted extensive experimental evaluation, including a short deployment. Our experimental results using an MSP430-based prototype show that UFoP provides as much as 10% more computational availability, and as much as four times more radio availability than the centralized approach. For all applications and energy environments evaluated, UFoP harvested 0.7-10.2% more energy than the centralized equivalent; meaning UFoP adds zero energy overhead.

Persistent Clocks for Batteryless Sensing Devices

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.

Classroom Uses for BeSocratic

This paper describes how BeSocratic can be used to improve learning and class interaction. BeSocratic is a novel intelligent tutoring system that aims to fill the gap between simple multiple-choice systems and free-response systems. The system includes a set of interactive modules that provide instructors with powerful tools to assess student performance. Beyond text boxes and multiple-choice questions, BeSocratic contains several feedback driven modules that can capture free-form student drawings. These drawings can be automatically recognized and evaluated as complex structures including Euclidean graphs, chemistry molecules, computer science graphs, or simple drawings for use within science, technology, engineering, and mathematics courses. This paper describes three use-cases for BeSocratic and how each scenario can improve learning and class interaction throughout the curriculum. These scenarios are: (1) formative assessments and tutorials, (2) free-response exercises, and (3) in-class real-time activities.

Timely Execution on Intermittently Powered Batteryless Sensors

Tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades by leaving batteries behind and surviving off energy harvested from the environment--- avoiding the cost of replacing and disposing of billions or trillions of dead batteries. However, creating programs for these sensors is difficult. Energy harvesting is inconsistent, energy storage is scarce, and batteryless sensors can lose power at any point in time--- causing volatile memory, execution progress, and time to reset. In response to these disruptions, developers must write unwieldy programs attempting to protect against failures, instead of focusing on sensing goals, defining tasks, and generating useful data in a timely manner. To address these shortcomings, we have designed Mayfly, a language and runtime for timely execution of sensing tasks on tiny, intermittently-powered, energy harvesting sensing devices. Mayfly is a coordination language and runtime built on top of Embedded-C that combines intermittent execution fragments to form coherent sensing schedules---maintaining forward progress, data consistency, data freshness, and data utility across multiple power failures. Mayfly makes the passing of time explicit, binding data to the time it was gathered, and keeping track of data and time through power failures. We evaluated Mayfly against state-of-the art systems, conducted a user study, and implemented multiple real world applications across application domains in inventory tracking, and wearables.

Demo abstract: Ekho: Realistic and repeatable experimentation for tiny energy-harvesting sensors

Harvesting energy from the environment makes it possible to deploy tiny sensors for long periods of time, with little or no required maintenance; however, this free energy makes testing and experimentation difficult. Environmental energy sources vary widely and are often difficult both to predict and to reproduce in the lab during testing. These variations are also behavior dependent---a factor that leaves application engineers unable to make even simple comparisons between algorithms or hardware configurations, using traditional testing approaches. This demonstration presents Ekho, a device that makes it possible to conduct realistic and repeatable experiments involving energy harvesting devices. Ekho is a general-purpose tool that supports a wide range of harvesting technologies. We demonstrate, using a working prototype, that Ekho is capable of reproducing many types of energy harvesting environments accurately and consistently.

Personalized Medicine in the Wearable Era: Translational Barriers and Call to Action

Preventive medicine is heading towards a more personalized future; adjusting care based on the individual needs of the patient. This future is enabled by wearable devices: not just smartwatches, but devices embedded in clothing, necklaces, and other gadgets that will one day invisibly, continuously, and effortlessly monitor and understand the health and wellbeing of a patient in perpetuity. However, this future is not yet translating from engineering to medicine, because of a tendency to treat patients as a monolithic group, and the lack of willpower to study users in the wild. These failures cause us to miss interesting behaviors and features of patients who have unique personalities, habits, medical histories, and medical needs, potentially hurting their quality of care. We believe that the technology is available to make personalized, specialized health platforms keyed to a single persons habits, needs, and identity that is both easy to wear and fully functional. In this vision paper, we discuss a new approach to preventive, personal medicine with wearables, and outline confounding factors and applications in line with our vision.

Realistic Simulation for Tiny Batteryless Sensors

Battery-free sensing promises to revolutionize the scientific and industrial communities by enabling long term, maintenance free deployments in tough to reach places. However, developing applications for these intermittently-powered, batteryless devices is notoriously demanding. Each devices performance is closely tied to its environment at runtime, and developers are often unable to predict how their system will be behave upon deployment. In this paper we present an instruction level simulator based off of MSPsim for intermittently-powered devices that can accurately emulate real-world energy harvesting conditions, taking into account power models of common hardware peripherals like a radio, and accelerometer. These harvester conditions are represented by IV surfaces recorded by the Ekho hardware emulator We have provided this simulator as an open source tool for the benefit of the community.

The Energy Harvesting Mode Abstraction

We propose a new abstraction for understanding energy harvesting behaviors in the wild, especially how these behaviors impact energy constrained and battery-free sensors. The Energy Harvesting Mode abstraction explores ways to make sense of energy harvesting behaviors. We take known energy harvesting datasets, and create a few of our own, then classify energy harvesting behavior into modes. Modes are periodic or repeated elements caused by systematic or fundamental attributes of the energy harvesting environment. We show the existence of these Energy Harvesting Modes using real world data and IV surfaces created with the Ekho emulator. We discuss the impacts and usage of this powerful abstraction, including enabling adaptation, test case generation, and efficiency analysis for energy harvesting and intermittently powered sensing devices.

InK: Reactive Kernel for Tiny Batteryless Sensors

Tiny energy harvesting battery-free devices promise maintenance free operation for decades, providing swarm scale intelligence in applications from healthcare to building monitoring. These devices operate intermittently because of unpredictable, dynamic energy harvesting environments, failing when energy is scarce. Despite this dynamic operation, current programming models are static; they ignore the event-driven and time-sensitive nature of sensing applications, focusing only on preserving forward progress while maintaining performance. This paper proposes InK; the first reactive kernel that provides a novel way to program these tiny energy harvesting devices that focuses on their main application of event-driven sensing. InK brings an event-driven paradigm shift for batteryless applications, introducing building blocks and abstractions that enable reacting to changes in available energy and variations in sensing data, alongside task scheduling, while maintaining a consistent memory and sense of time. We implemented several event-driven applications for InK, conducted a user study, and benchmarked InK against the state-of-the-art; InK provides up to 14 times more responsiveness and was easier to use. We show that InK enables never before seen batteryless applications, and facilitates more sophisticated batteryless programs.