Batteryless wearables use energy harvested from the environment, eliminating the burden of charging or replacing batteries. This makes them convenient and environmentally friendly. However, these benefits come at a price. Batteryless wearables operate intermittently (based on energy availability), which adds complexity to their design and introduces usability limitations not present in their battery-powered counterparts. In this paper, we conduct a scenario-based study with 400 wearable users to explore how users perceive the inherent trade-offs of batteryless wearable devices. Our results reveal users’ concerns, expectations, and preferences when transitioning from battery-powered to batteryless wearable use. We discuss how the findings of this study can inform the design of usable batteryless wearables.

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.

Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because existing standards are usually designed to support reliably powered devices with predictable node availability and accurate timekeeping capabilities for connection and congestion management. In this article, we present Greentooth, a robust and energy-efficient wireless communication protocol for intermittently powered sensor networks. It enables reliable communication between a receiver and multiple batteryless sensors using Time Division Multiple Access–style scheduling and low-power wake-up radios for synchronization. Greentooth employs lightweight and energy-efficient connections that are resilient to transient power outages, while significantly improving network reliability, throughput, and energy efficiency of both the battery-free sensor nodes and the receiver—which could be untethered and energy constrained. We evaluate Greentooth using a custom-built batteryless sensor prototype on synthetic and real-world energy traces recorded from different locations in a garden across different times of the day. Results show that Greentooth achieves 73% and 283% more throughput compared to Asynchronous Wake-up on Demand MAC and Receiver-Initiated Consecutive Packet Transmission Wake-up Radios, respectively, under intermittent ambient solar energy and over 2× longer receiver lifetime.

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.

Tiny energy harvesting sensors that operate intermittently, without batteries, have become an increasingly appealing way to gather data in hard to reach places at low cost. Frequent power failures make forward progress, data preservation and consistency, and timely operation challenging. Unfortunately, state-of-the-art systems ask the programmer to solve these challenges, and have high memory overhead, lack critical programming features like pointers and recursion, and are only dimly aware of the passing of time and its effect on application quality. We present Time-sensitive Intermittent Computing System (TICS), a new platform for intermittent computing, which provides simple programming abstractions for handling the passing of time through intermittent failures, and uses this to make decisions about when data can be used or thrown away. Moreover, TICS provides predictable checkpoint sizes by keeping checkpoint and restore times small and reduces the cognitive burden of rewriting embedded code for intermittency without limiting expressibility or language functionality, enabling numerous existing embedded applications to run intermittently.

Chronic disease is one of the most pressing health challenges facing the United States (and an increasing set of other countries). The onset or progression of diseases like obesity, diabetes, and metabolic disorder are strongly related to eating behavior, and scientists are still trying to fully understand the complex mixture of diet, exercise, genetics, sociocultural context, and physical environment that lead to these diseases. Health science, however, has no effective means for automatically measuring eating behavior in free-living conditions. The Auracle aims to be a wearable earpiece that detects eating behavior, to be fielded by health-science researchers in their efforts to study eating behavior and ultimately to develop interventions useful to individuals striving to address chronic disease related to eating.

An increasing number of wearable devices support the execution of multiple third-party applications, increasing the functionality and flexibility of these devices. These multi-application, multi-tenant devices provide users with more options, and application developers with a standard platform. Typical ultra-low-power wearable devices, however, lack the type of hardware memory protection mechanisms~-- such as Memory Management Units (MMU)~-- needed to safely separate applications. At best, they provide a Memory Protection Unit (MPU), which allows the user to configure read/write/execute permissions for a few distinct regions of memory. At worst, no hardware memory protection is provided. MPU capabilities vary across hardware platforms, with many shortcomings: (1)~the MPU may only support a few distinct memory regions (fewer than one per application), (2)~the MPU may not protect all regions of memory, like hardware registers, and (3)~MPU protection boundary rules can be arcane, because they depend on opaque hardware implementations. Our key observation is that by supplementing a limited segment MPU with runtime checks, and using compile-time static analysis to explicitly layout applications in memory, we can guarantee application isolation (sandboxing) even on these limited MPUs, with lower overhead than software-only solutions.

Batteryless, energy-harvesting sensing systems are critical to the Internet-of-Things (IoT) vision and sustainable, long-lived, untethered systems. Unfortunately, developing new batteryless applications is challenging. Energy resources are scarce and highly variable, power failures are frequent, and successful applications typically require custom hardware and special expertise. In this paper, we present Flicker, a platform for quickly prototyping batteryless embedded sensors. Flicker is an extensible, modular, "plug and play" architecture that supports RFID, solar, and kinetic energy harvesting; passive and active wireless communication; and a wide range of sensors through common peripheral and harvester interconnects. Flicker supports recent advances in failure-tolerant timekeeping, testing, and debugging, while providing dynamic federated energy storage where peripheral priorities and user tasks can be adjusted without hardware changes. Flicker's software tools automatically detect new hardware configurations, and simplify software changes. We have evaluated the overhead and performance of our Flicker prototype and conducted a case study. We also evaluated the usability of Flicker in a user study with 19 participants, and found it had above average or excellent usability according to the well known System Usability Survey.

Sensing has been obsessed with delivering on the "smart dust" vision outlined decades ago, where trillions of tiny invisible computers support daily life, infrastructure, and humanity in general. Batteries are the single greatest threat to this vision of a sustainable Internet of Things. They are expensive, bulky, hazardous, and wear out after a few years (even rechargeables). Replacing and disposing of billions or trillions of dead batteries per year would be expensive and irresponsible. By leaving the batteries behind and surviving off energy harvested from the environment, tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades. The intermittent execution, constrained compute and energy resources, and unreliability of these devices creates new challenges for the sensing and embedded systems community. However, the rewards and potential impact across many fields are worth it, enabling currently impractical applications in health services and patient care, commercial and consumer applications, wildlife conservation, industrial and infrastructure management, even space exploration. This paper highlights major research questions and establishes new directions for the community to embrace and investigate.

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.

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. In this article, we describe the design and evaluation of Ekho, an emulator capable of recording energy harvesting conditions and accurately recreating those conditions in the lab. This makes it possible to conduct realistic and repeatable experiments involving energy harvesting devices. Ekho is a general-purpose, mobile tool that supports a wide range of harvesting technologies. We demonstrate, using a working prototype, that Ekho is capable of reproducing solar, Radio Frequency (RF), and kinetic energy harvesting environments accurately and consistently. Our results show that Ekho can recreate harvesting-dependent program behaviors by emulating energy harvesting conditions accurately to within 77.4μA for solar and 15.0μA for kinetic environments, and can emulate RF energy harvesting conditions consistently.

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.

In this demonstration we present the Amulet Platform; a hardware and software platform for developing energy- and resource-efficient applications on multi-application wearable devices. This platform, which includes the Amulet Firmware Toolchain, the Amulet Runtime, the ARP-View graphical tool, and open reference hardware, efficiently protects applications from each other without MMU support, allows developers to interactively explore how their implementation decisions impact battery life without the need for hardware modeling and additional software development, and represents a new approach to developing long-lived wearable applications. We envision the Amulet Platform enabling long-duration experiments on human subjects in a wide variety of studies.

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.

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.

Energy harvesting is an indispensable mechanism of sensor devices that operate in perpetuity. While harvesting free energy from the environment has enabled many applications, it has also spawned new problems, and new paradigms. Notably, making decisions on when to use high power sensors and external components when energy is scarce, and future supply is unpredictable. Because sensor nodes generally share a single, centralized energy store, seemingly atomic, or unrelated sensing tasks can hamper each other by drawing the supply voltage too low, and draining the energy reservoir. This demonstration presents the United Federation of Peripherals (UFoP), a novel method of separating energy concerns in hardware by allocating dedicated energy storage (in the form of small capacitors) to specific sensor components, and charging them in a prioritized fashion with an analog front end. UFoP gives application designers a more deterministic view of energy and task scheduling, allowing them to make better informed decisions when developing sensor applications. Designers do not have to rely on crude estimates or simulations but can instead depend on in-situ analog measurements to opportunistically drive their applications.

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.

No abstract available.

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.

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 paper summarizes our experiences integrating topics in the software development fundamentals (SDF), programming languages (PL), and software engineering (SE) knowledge areas of the ACM 2013 curriculum within a single course. It is novel in combining object-oriented programming and software development practices with fundamental analytical reasoning about software correctness. The aim is to integrate and cover the topics in an effective fashion. The course description in this paper represents an approach we have applied successfully for over 5 years. Students tend to consider this course to be one of the more challenging encountered in the first two years of study. Interestingly, the challenge appears to stem equally from mastering object-oriented programming and design pattern components of the course, as it does from learning to use specifications for analytical reasoning of component correctness.

Water infrastructure has been degrading on a national scale in the U.S. for years. Much of this degradation is caused by massive leakage in aging water mains. Water is a critical, and finite resource, early identification of these leaks would not only save cities millions of dollars in revenue but also safeguard our limited natural resources. Current methods of leak detection are either too costly, unscalable, or only feasible in the short-term. We propose using environmentally powered embedded adaptive sensors to provide cost-effective water-monitoring infrastructure that can operate maintenance free for the lifetime of a water main. In this poster we will present our early monitoring system, and initial results and analysis from our current deployment in the Clemson University water distribution network. We also present future directions and key research questions.

Due to advances in low power sensors, energy harvesting, and disruption tolerant networking, we can now build mobile systems that operate perpetually, sensing and streaming data directly to scientists. However, factors such as energy harvesting variability and unpredictable network connectivity make building robust and perpetual systems difficult. In this paper, we present a system, Tula, that balances sensing with data delivery, to allow perpetual and robust operation across highly dynamic and mobile networks. This balance is especially important in unpredictable environments; sensing more data than can be delivered by the network is not useful, while gathering less underutilizes the system's potential. Tula is decentralized, fair and automatically adapts across different mobility patterns. We evaluate Tula using mobility and energy traces from TurtleNet-a mobile sensor network we deployed to study Gopher tortoises-and publicly available traces from the UMass DieselNet testbed. Our evaluations show that Tula senses and delivers data at up to 85 percent of an optimal, oracular system that perfectly replicates data and has foreknowledge of future energy harvesting. We also demonstrate that Tula can be implemented on a small microcontroller with modest code, memory, and processing requirements.

Mobile computing and sensing technologies present exciting opportunities for healthcare. Prescription wireless sensors worn by patients can automatically deliver medical data to care providers, dramatically improving their ability to diagnose, monitor, and manage a range of medical conditions. Using the mobile phones that patients already carry to provide connectivity between sensors and providers is essential to keeping costs low and deployments simple. Unfortunately, software-based attacks against phones are also on the rise, and successful attacks on privacy-sensitive and safety-critical applications can have significant consequences for patients.

Transiently powered computing devices such as RFID tags, kinetic energy harvesters, and smart cards typically rely on programs that complete a task under tight time constraints before energy starvation leads to complete loss of volatile memory. Mementos is a software system that transforms general-purpose programs into interruptible computations that are protected from frequent power losses by automatic, energy-aware state checkpointing. Mementos comprises a collection of optimization passes for the LLVM compiler infrastructure and a linkable library that exercises hardware support for energy measurement while managing state checkpoints stored in nonvolatile memory. We evaluate Mementos against diverse test cases in a trace-driven simulator of transiently powered RFID-scale devices. Although Mementos's energy checks increase run time when energy is plentiful, they allow Mementos to safely suspend execution when energy dwindles, effectively spreading computation across zero or more power failures. This paper's contributions are: a study of the runtime environment for programs on RFID-scale devices; an energy-aware state checkpointing system for these devices that is implemented for the MSP430 family of microcontrollers; and a trace-driven simulator of transiently powered RFID-scale devices.

Mobile sensing technologies present exciting opportunities for healthcare. Wireless sensors can automatically provide sensor data to care providers, dramatically improving their ability to diagnose, monitor, and manage a wide range of medical conditions. Using mobile phones to provide connectivity between sensors and providers is essential to keeping costs low and deployments simple. Unfortunately, software-based attacks against phones, which can have significant consequences for patients, are also on the rise.

The ease of deployment of battery-powered and mobile systems is pushing the network edge far from powered infrastructures. A primary challenge in building untethered systems is offering powerful aggregation points and gateways between heterogeneous end-points---a role traditionally played by powered servers. Microservers are battery-powered in-network nodes that play a number of roles: processing data fromclients, aggregating data, providing responses to queries, and actingas a network gateway. Providing QoS guarantees for theseservices can be extremely energy intensive. Since increasedenergy consumption translates to a shorter lifetime, there is a need for a new way to provide these QoS guarantees at minimal energy consumption.

Embedded systems can operate perpetually without being connected to a power source by harvesting environmental energy from motion, the sun, wind, or heat differentials. However, programming these perpetual systems is challenging. In response to changing energy levels, programmers can adjust the execution frequency of energy-intensive tasks, or provide higher service levels when energy is plentiful and lower service levels when energy is scarce. However, it is often difficult for programmers to predict the energy consumption resulting from these adjustments. Worse, explicit energy management can tie a program to a particular hardware platform, limiting portability.This paper introduces Eon, a programming language and runtime system designed to support the development of perpetual systems. To our knowledge, Eon is the first energy-aware programming language. Eon is a declarative coordination language that lets programmers compose programs from components written in C or nesC. Paths through the program (flows) may be annotated with different energy states. Eon's automatic energy management then dynamically adapts these states to current and predicted energy levels. It chooses flows to execute and adjusts their rates of execution, maximizing the quality of service under available energy constraints.We demonstrate the utility and portability of Eon by deploying two perpetual applications on widely different hardware platforms: a GPS-based location tracking sensor deployed on a threatened species of turtle and on automobiles, and a solar-powered camera sensor for remote, ad-hoc deployments. We also evaluate the simplicity and effectiveness of Eon with a user study, in which novice Eon programmers produced more efficient efficient energy-adaptive systems in substantially less time than experienced C programmers.

Maintaining optimal consistency in a distributed system requires that nodes be always-on to synchronize information. Unfortunately, mobile devices such as laptops do not have adequate battery capacity for constant processing and communication. Even by powering off unnecessary components, such as the screen and disk, current laptops only have a lifetime of a few hours. Although PDAs and sensors are similarly limited in lifetime, a PDA's power requirement is an order-of-magnitude smaller than a laptop's, and a sensor's is an order-of-magnitude smaller than a PDA's. By combining these diverse platforms into a single integrated laptop, we can reduce the power cost of always-on operation. This paper presents the design, implementation, and evaluation of Turducken, a Hierarchical Power Management architecture for mobile systems. We focus on a particular instantiation of HPM, which provides high levels of consistency in a laptop by integrating two additional low power processors. We demonstrate that a Turducken system can provide battery lifetimes of up to ten times that of a standard laptop for always-on operation and three times for a system that periodically sleeps.