The erasure of Indigenous cultures is a painfully enduring legacy of U.S. imperial history, producing disparities in culturally-relevant curricula, thus barring Indigenous students from resonant educational experiences. In Hawaiʻi, where Hawaiian instruction was banned for 91 years, culturally-relevant curricula gaps are most severe in Hawaiian-language education and technical fields like Computer Science (CS). Large Language Models (LLMs) are positioned to address these disparities, yet raise concerns of cultural misrepresentation and data sovereignty. We ask: how can LLMs serve educators of varying familiarity with Hawaiian culture while safeguarding Hawaiian knowledge? Addressing this question, we present Kumu Connect, an LLM-assisted culturally-revitalizing CS lesson plan generator, co-designed with Native Hawaiian educators. This work contributes design requirements, a situated case study, and “future imaginaries” paired with design provocations to highlight how community-engaged development can inform culturally-authentic and pedagogically useful AI tools that address CS and culture knowledge gaps and uphold Indigenous data sovereignty.

Climate change, urbanization, and pollution threaten ecosystems and the treaty-guaranteed rights of Native Nations in the Great Lakes region. Tools that support culturally relevant implementation of policy and meaningful access to environmental data for sentinel species like Manoomin, wild rice, can help uphold treaty rights and ensure environmental stewardship. This paper presents Noondawind, an interactive data platform co-designed with Ojibwe partners to support community members and Tribal staff in interpreting and acting on environmental data and policy resources. We engaged in a participatory design process informed by and deeply integrated with Ojibwe worldviews. Our results highlight how participatory and culturally relevant co-design approaches can enhance environmental governance, support data sovereignty, and foster engagement with environmental data. We offer design implications and lessons learned for projects developing tools in partnership with Indigenous communities. These findings contribute to the growing field of Indigenous HCI and social justice literature in HCI.

Although generative AI is being deployed into classrooms with promises of aiding teachers, educators caution that these tools can have unintended pedagogical repercussions, including cultural misrepresentation and bias. These concerns are heightened in low-resource language and Indigenous education settings, where AI systems frequently underperform. We investigate these challenges in Hawai‘i, where public schools operate under a statewide mandate to integrate Hawaiian language and culture into education. Through four co-design workshops with 22 public school educators, we surfaced concerns about using generative AI in educational settings, particularly around cultural misrepresentation, and corresponding designs for auditing tools that address these issues. We find that educators envision tools grounded in specific Hawaiian cultural values and practices, such as tracing the genealogy of knowledge in source materials. Building on these insights, we conceptualize AI auditing as a community-oriented process rather than the work of isolated individuals, and discuss implications for designing auditing tools.

Edge devices are often deployed at scale, yet their environmental impact, shaped by complex interactions between hardware choices, workload demands, power systems, and deployment context, has been overlooked by the mobile computing community. We present MicroGreen, a design-time framework that enables carbon-aware design for edge ML systems. By combining component-level carbon models with workload profiling and environment-aware energy analysis, MicroGreen identifies carbon-optimal configurations across diverse conditions. Our results show that the most energy-efficient processor is not always the most sustainable, and that ambient energy availability, inference rate, and deployment lifetime can shift the carbon-optimal design by over an order of magnitude. Through a real vision-based visitor detection and counting deployment in New York City parks, we demonstrate that heterogeneous, location-aware designs reduce total emissions by 47.72% compared to a homogeneous baseline.

We introduce BIONIC (duraBle tImekeeper fOr iNtermIttent Computing), a novel power system architecture and software runtime that facilitates time-sensitive intermittent computing for battery-less Internet of Things. BIONIC integrates timekeeping into energy storage hardware: its power system comprises two energy storage capacitors. BIONIC switches between these two energy storage capacitors to estimate ambient power. Using these estimations, BIONIC can predict and track charging times (i.e., off-time durations) to schedule time-sensitive tasks and complete them on time. Our evaluations showed that BIONIC significantly extends the measurable off-time intervals by a factor of 15 to 1620 compared to state-of-the-art, while achieving an accuracy of up to 99.1% and effectively adapting to new energy conditions. Besides, in a typical batteryless application, BIONIC's power-aware intermittent computing runtime boosted the number of completed time-sensitive operations by 30% while reducing failed timely operations by 29%.

Wireless sensor networks (WSNs) deployed in the natural environment offer well-established benefits for prediction, modeling, timely response, and informed decision making in the face of the accelerating climate crisis. To facilitate faster experimentation and deployment, researchers have focused on developing WSN platforms that support rapid prototyping and field readiness. Despite their potential, these platforms have historically seen limited uptake both among domain scientists and the broader IoT research community. In this paper, we present insights gained from the development of ENTS (Environmental NeTworked Sensing)—a WSN platform co-designed with three distinct groups of domain scientists through an iterative development process. We describe the evolution of ENTS from a simple analog measurement tool to an extensible platform actively supporting collaborations with diverse domain experts. We synthesize our experience into a set of design principles aimed at fostering impactful and lasting research partnerships. Finally, we instantiate the outlined design principles for future interdisciplinary sensing efforts, including open-source hardware, firmware, low-cost custom weatherproof enclosures, and a web-based visualization tool.

Amid escalating ecological challenges, Human-Computer Interaction (HCI) researchers have begun adopting More-than-Human design (MtHD) approaches as a means of reimagining and strengthening the bonds between the human and non-human world. Taking a MtHD approach, this work investigates how loofah, a plant-based, biodegradable material, can be used to foster ecological awareness in everyday life. We present a material exploration of loofah, including initial encounters with loofah as a uniquely structural material, a design space focused on how loofah can be combined with various indicators that respond to different environmental factors like temperature, UV radiation, pH of water and soil, the Iron and moisture in soil and utilizing loofah as a substrate for plant growth. Based on this design space, we create two wearables: a hat and a glove. These artifacts incorporate environmental sensing capabilities and host live microgreens, highlighting loofah‘s potential as both an interface and habitat. Through a 15-day autoethnographic journaling process by the first and second authors, we reflect on the embodied experiences of “wearing” environmental change and cultivating on-body ecological practices. This pictorial contributes to the HCI community by introducing a biomaterial-based embodied interaction to provoke reflections on the relationships between materials, non-human forms, and the environment, while integrating functional considerations into MtHD.

We introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480×360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org.

The growth of urban centers, the impacts of climate change, and the need for information to inform city planning and community organizing have intensified the need for monitoring solutions in cities. The advancement of low-cost sensor technologies and digital twin frameworks presents an opportunity for cities to deploy extensive, real-time monitoring systems. However, few examples of long-term, large-scale, publicly available urban sensor datasets exist, limiting the ability of researchers and planners to explore important topics such as hyperlocal environmental variations. In this paper, we introduce a comprehensive dataset from a 118-node LTE-M connected, solar-powered air quality sensor network deployed across Chicago, Illinois, from April 2021 to April 2023. This dataset comprises 94,915,745 readings of United States EPA criteria air pollutants, in two parts: 1) 75,932,596 gas sensor readings for carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) 18,983,149 particulate matter (PM) readings. This open-access dataset uniquely enables researchers to examine critical aspects of smart city sensing, including environmental equity, sensor network deployment dynamics, and interactions between urban infrastructure, natural environments, and residents. Via integration with open datasets from the City of Chicago and other sources, this dataset serves as a foundational tool for advancing research in environmental justice, public health, and urban planning, empowering researchers to build and test ideas before going to deployment to move closer to achieving smart, sustainable urban environments.

Local workers and their knowledge are essential for sustainable and effective conservation efforts. However, many technology-assisted conservation programs are guided by global benchmarks (e.g., forest cover) and industry metrics (e.g., cost per acre), which often devalue local knowledge and fail to consider the economic and conservation goals of local workers. Assets-based design is well-suited to center workers and their strengths, yet it may fail to fully address the complexities of long-term conservation programs by not explicitly emphasizing workers' goals or bolstering their assets. We extend recent approaches in assets-based design literature that address these limitations through our case studies of reforestation, biodiversity monitoring, and carbon sequestration programs in three protected areas in Madagascar. We leverage a mixed-methods approach of direct reactive observations, unstructured interviews, and an informal design workshop, revealing emergent themes surrounding economic sustainability and the value of local ecological knowledge in conservation. Finally, we explore examples, tensions, and design considerations for worker-centered conservation technology to: (1) prioritize local knowledge, (2) foster love of nature, (3) center economic goals, and (4) embrace local autonomy. This work advances the dialogue on assets-based design, promoting the co-creation of equitable and sustainable conservation technologies with workers in Global South settings by centering local economic priorities and enhancing workers' strengths.

Continuous health monitoring is crucial to ensuring better health and taking preventive measures just-in-time. Existing battery-powered health wearables pose a significant limitation to continuous monitoring as batteries wear out after fixed energy cycles and need replacement. Ambient energy harvesting unlocks battery-free sensing but it suffers from spatio-temporal variability, making it unfit for health sensing. Intra-body power transfer (IBPT) provides an alternative energy source for battery-free operation, however, it can only provide limited energy in order to ensure wearer's safety. Existing system support is designed to maximize computational progress in a single energy cycle, thus wasting energy on computations that become stale in the next energy cycle. We instantiate an IBPT-powered health wearable capable of supporting multiple health sensors. To cope with lower incoming energy, we introduce BodyOS; a system support that exposes programming constructs for domain experts to express health applications in terms of the inherent dependencies of bio-signals being monitored by the application. By avoiding unnecessary sensing operations, BodyOS allows energy-efficient application execution and faster capacitor recharge while ensuring that the data sensed by the application is always useful. We evaluate BodyOS to show that it significantly improves energy efficiency, thus increasing the on-time and number of data points collected by the device.

Wearable health devices have transformed the land-scape of vital sign monitoring by enabling continuous, unob-trusive data collection. These compact and lightweight devices bypass the need for large, specialized instruments, facilitating frequent and comprehensive health monitoring essential for di-agnosing various medical conditions. Researchers are leveraging innovative techniques to sense bodily functions through external signals, such as using acoustic signals for joint health and repurposing low-cost sensors like IMUs, temperature sensors, and microphones as biosensors. These advancements aim to create more affordable and widespread health monitoring systems than traditional, costly biosensors. In this work, we present HealthHub, a versatile wearable health prototyping toolkit designed to expedite the development and testing of wearable health devices. HealthHub's modularity and flexibility are demonstrated by its array of onboard sensors and its support for custom snap-on boards that enhance sensing capabilities via the onboard ADC. Our evaluation of HealthHub included testing its power consumption and performance in measuring respiration, where it functioned as a pendant. The system operated for three days on a single coin cell battery, recording data at high sample rates and fidelity. HealthHub proves to be a lightweight, compact, and highly adaptable platform for developing wearable health devices. Its robust performance and extendable design make it an invaluable tool for researchers and developers in wearable health technol-ogy, facilitating the rapid conversion of innovative ideas into functional prototypes.

Ubiquitous sensing technologies, leveraging a network of interconnected sensors and devices, offer multifaceted benefits to society. However, the use of batteries has persistently posed a challenge to their advancement. In recent years, researchers have explored establishing communication between battery-free nodes. The primary objective of this work is to expedite the testing of algorithms for multiple battery-free interconnected nodes by isolating the algorithm from the underlying hardware. Isolating the hardware allows faster tuning of algorithms, and enables testing on a large scale. We developed a novel python-based simulation framework, HarvNet. Simulations using real-world power traces demonstrated a success rate of 81% in establishing connections between nodes which closely emulates the success rate achieved using hardware.

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.

Human studies often rely on wearable lifelogging cameras that capture videos of individuals and their surroundings to aid in visual confirmation or recollection of daily activities like eating, drinking, and smoking. However, this may include private or sensitive information that may cause some users to refrain from using such monitoring devices. Also, short battery lifetime and large form factors reduce applicability for long-term capture of human activity. Solving this triad of interconnected problems is challenging due to wearable embedded systems’ energy, memory, and computing constraints. Inspired by this critical use case and the unique design problem, we developed NIR-sighted, an architecture for wearable video cameras that navigates this design space via three key ideas: (i) reduce storage and enhance privacy by discarding masked pixels and frames, (ii) enable programmers to generate effective masks with low computational overhead, and (iii) enable the use of small MCUs by moving masking and compression off-chip. Combined together in an end-to-end system, NIR-sighted’s masking capabilities and off-chip compression hardware shrinks systems, stores less data, and enables programmer-defined obfuscation to yield privacy enhancement. The user’s privacy is enhanced significantly as nowhere in the pipeline is any part of the image stored before it is obfuscated. We design a wearable camera called NIR-sightedCam based on this architecture; it is compact and can record IR and grayscale video at 16 and 20+ fps, respectively, for 26 hours nonstop (59 hours with IR disabled) at a fraction of comparable platforms power draw. NIR-sightedCam includes a low-power Field Programmable Gate Array that implements our mJPEG compress/obfuscate hardware, Blindspot. We additionally show the potential for privacy-enhancing function and clinical utility via an in-lab eating study, validated by a nutritionist.

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.

Wearables are a potentially vital mechanism for individuals to monitor their health, track behaviors, and stay connected. Unfortunately, both price and a lack of consideration of the needs of low-SES communities have made these devices inaccessible and unusable for communities that would most substantially benefit from their affordances. To address this gap and better understand how members of low-SES communities perceive the potential benefits and barriers to using wearable devices, we conducted 19 semi-structured interviews with people from minority, high crime rate, low-SES communities. Participants emphasized a critical need for safety-related wearable devices in their communities. Still, existing tools do not yet address the specific needs of this community and are out of reach due to several barriers. We distill themes on perceived useful features and ongoing obstacles to guide a much-needed research agenda we term ’Equityware’: building wearable devices based on low-SES communities’ needs, comfortability, and limitations.

Task-based intermittent software systems always re-execute peripheral input/output (I/O) operations upon power failures since tasks have all-or-nothing semantics. Re-executed I/O wastes significant time and energy and risks memory inconsistency. This paper presents EaseIO, a new task-based intermittent system that remedies these problems. EaseIO programming interface introduces re-execution semantics for I/O operations to facilitate safe and efficient I/O management for intermittent applications. EaseIO compiler front-end considers the programmer-annotated I/O re-execution semantics to preserve the task's energy efficiency and idem-potency. EaseIO runtime introduces regional privatization to eliminate memory inconsistency caused by idempotence bugs. Our evaluation shows that EaseIO reduces the wasted useful I/O work by up to 3× and total execution time by up to 44% by avoiding 76% of the redundant I/O operations, as compared to the state-of-the-art approaches for intermittent computing. Moreover, for the first time, EaseIO ensures memory consistency during DMA-based I/O operations.

The growing use of computing and proliferation of computing devices requires a holistic focus on sustainability as the environmental impacts of computing technologies go beyond their energy consumption.Environmental impacts span all stages of the lifecycle -manufacturing, operation, and disposal.A sustainability mindset must permeate all organizations involved in design, manufacturing, operation, and disposal/recycling of computational devices.This panel will discuss current and new research on sustainability in computing that spans the full lifecycle, all layers of the computing stack and across the computing spectrum from edge to cloud.The panel will also discuss potential crossdisciplinary approaches to sustainable computing and new notions and metrics to quantify sustainability.

The growing use of computing and proliferation of computing devices requires a holistic focus on sustainability as the environmental impacts of computing technologies go beyond their energy consumption. Environmental impacts span all stages of the lifecycle - manufacturing, operation, and disposal. A sustainability mindset must permeate all organizations involved in design, manufacturing, operation, and disposal/recycling of computational devices. This panel will discuss current and new research on sustainability in computing that spans the full lifecycle, all layers of the computing stack and across the computing spectrum from edge to cloud. The panel will also discuss potential cross-disciplinary approaches to sustainable computing and new notions and metrics to quantify sustainability.

Smartphone motion sensors provide cybersecurity attackers with a stealthy way to eavesdrop on nearby acoustic information. Eavesdropping on touchtones emitted by smartphone speakers when users input numbers into their phones exposes sensitive information such as credit card information, banking PINs, and social security card numbers to malicious applications with access to only motion sensor data. This work characterizes this new security threat of touchtone eavesdropping by providing an analysis based on physics and signal processing theory. We show that advanced adversaries who selectively integrate data from multiple motion sensors and multiple sensor axes can achieve over 99% accuracy on recognizing 12 unique touchtones. We further design, analyze, and evaluate several mitigations which could be implemented in a smartphone update. We found that some apparent mitigations such as low-pass filters can undesirably reduce the motion sensor data to benign applications by 83% but only reduce an advanced adversary’s accuracy by less than one percent. Other more informed designs such as anti-aliasing filters can fully preserve the motion sensor data to support benign application functionality while reducing attack accuracy by 50.1%.

Perceiving the environment for better and more efficient situational awareness is essential in applications such as wildlife surveillance, wildfire detection, crop irrigation, and building management. Energy-harvesting, intermittently-powered sensors have emerged as a zero maintenance solution for long-term environmental perception. However, these devices suffer from intermittent and varying energy supply, which presents three major challenges for executing perceptual tasks: (1) intelligently scaling computation in light of constrained resources and dynamic energy availability, (2) planning communication and sensing tasks, (3) and coordinating sensor nodes to increase the total perceptual range of the network. We propose an adaptive framework, AdaSens, which adapts the operations of intermittently-powered sensor nodes in a coordinated manner to cover as much as possible of the targeted scene, both spatially and temporally, under interruptions and constrained resources. We evaluate AdaSens on a real-world surveillance video dataset, VideoWeb, and show at least 16% improvement on the coverage of the important frames compared with other methods.

Wearable cameras provide an informative view of wearer activities, context, and interactions. Video obtained from wearable cameras is useful for life-logging, human activity recognition, visual confirmation, and other tasks widely utilized in mobile computing today. Extracting foreground information related to the wearer and separating irrelevant background pixels is the fundamental operation underlying these tasks. However, current wearer foreground extraction methods that depend on image data alone are slow, energy-inefficient, and even inaccurate in some cases, making many tasks–like activity recognition–challenging to implement in the absence of significant computational resources. To fill this gap, we built ActiSight, a wearable RGB-Thermal video camera that uses thermal information to make wearer segmentation practical for body-worn video. Using ActiSight, we collected a total of 59 hours of video from 6 participants, capturing a wide variety of activities in a natural setting. We show that wearer foreground extracted with ActiSight achieves a high dice similarity score while significantly lowering execution time and energy cost when compared with an RGB-only approach.

Intermittently operating embedded computing platforms powered by energy harvesting require software frameworks to protect from errors caused by Write After Read (WAR) dependencies. A powerful method of code protection for systems with non-volatile main memory utilizes compiler analysis to insert a checkpoint inside each WAR violation in the code. However, such software frameworks are oblivious to the code structure---and therefore, inefficient---when many consecutive WAR violations exist. Our insight is that by transforming the input code, i.e., moving individual write operations from unique WARs close to each other, we can significantly reduce the number of checkpoints. This idea is the foundation for WARio: a set of compiler transformations for efficient code generation for intermittent computing. WARio, on average, reduces checkpoint overhead by 58%, and up to 88%, compared to the state of the art across various benchmarks.

We have witnessed explosive growth in computing devices at all scales, in particular with small wireless devices that can permeate most of our physical world. The IoT industry is helping to fuel this insatiable desire for more and more data. We have to balance this growth with an understanding of its environmental impact. Indeed, the ENSsys community must take leadership in putting sustainability up front as a primary design principle for the future of IoT and related areas, expanding the research mandate beyond the intricacies of the computing systems in isolation to encompass and integrate the materials, new applications, and circular lifecycle of electronics in the IoT. Our call to action is seeded with a circularity-focused computing agenda that demands a cross-stack research program for energy-harvesting computational things.

Battery-free and intermittently powered devices offer long lifetimes and enable deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging due to the lack of platform support for more advanced (32-bit) microprocessors and specialized accelerators---which can execute data-intensive machine learning tasks, but add complexity across the stack when dealing with intermittent power. We present Protean to bridge the platform gap for inference-capable battery-free sensors. Designed for runtime scalability, meeting the dynamic range of energy harvesters with matching heterogeneous processing elements like neural network accelerators. We develop a modular "plug-and-play" hardware platform, SuperSensor, with a reconfigurable energy storage circuit that powers a 32-bit ARM-based microcontroller with a convolutional neural network accelerator. An adaptive task-based runtime system, Chameleon, provides intermittency-proof execution of machine learning tasks across heterogeneous processing elements. The runtime automatically scales and dispatches these tasks based on incoming energy, current state, and programmer annotations. A code generator, Metamorph, automates conversion of ML models to intermittent safe execution across heterogeneous compute elements. We evaluate Protean with audio and image workloads and demonstrate up to 666x improvement in inference energy efficiency by enabling usage of modern computational elements within intermittent computing. Further, Protean provides up to 166% higher throughput compared to non-adaptive baselines.

In smart homes, access-control policies increasingly depend on contexts, such as who is taking an action, whether there is an emergency, or whether an adult is nearby. The vast literature on context sensing could potentially be leveraged to support contextual access control, yet this literature mostly ignores attacks, adversaries, and privacy. In this paper, we reevaluate the literature on home context sensing through a security and privacy mindset. We first describe a novel threat model in smart homes focusing on the capabilities of non-technical adversaries. Replay, imitation, and shoulder-surfing attacks are much more likely in this model. We summarize contexts relevant to access control in homes, mapping them to existing sensors. We then systematize the sensing literature to construct a decision framework for home context sensing that considers security, privacy, and usability. Applying our framework, we find that current sensors do not fully mitigate likely threats in homes. Some sensors are susceptible to simple threats like physical denial-of-service attacks, making it easy to bypass policies relying on the absence of a characteristic. Many sensors collect more data than needed and are not effective for all groups of users or under all situations.

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.

Energy-harvesting devices have enabled Internet of Things applications that were impossible before. One core challenge of batteryless sensors that operate intermittently is reliable timekeeping. State-of-the-art low-power real-time clocks suffer from long start-up times (order of seconds) and have low timekeeping granularity (tens of milliseconds at best), often not matching timing requirements of devices that experience numerous power outages per second. Our key insight is that time can be inferred by measuring alternative physical phenomena, like the discharge of a simple RC circuit, and that timekeeping energy cost and accuracy can be modulated depending on the run-time requirements. We achieve these goals with a multi-tier timekeeping architecture, named Cascaded Hierarchical Remanence Timekeeper (CHRT), featuring an array of different RC circuits to be used for dynamic timekeeping requirements. The CHRT and its accompanying software interface are embedded into a fresh batteryless wireless sensing platform, called Botoks, capable of tracking time across power failures. Low start-up time (max 5 ms), high resolution (up to 1 ms) and run-time reconfigurability are the key features of our timekeeping platform. We developed two time-sensitive batteryless applications to demonstrate the approach: a bicycle analytics tool, where the CHRT is used to track time between revolutions of a bicycle wheel, and wireless communication, where the CHRT enables radio synchronization between two intermittently-powered sensors.

In response to the N95 mask shortage caused by the COVID-19 pandemic, the US CDC has recognized moist-heat as one of the most effective and accessible methods for decontaminating N95 masks for reuse. However, it is challenging to reliably deploy this technique in healthcare settings due to a lack of specialized equipment capable of ensuring proper decontamination conditions. To this end, we developed a wireless sensor platform for moist-heat decontamination process verification, capable of monitoring hundreds of masks simultaneously in commercially available heating systems. Our easy-to-use, low-power, low-cost, scalable platform can be broadly deployed to protect front-line healthcare workers by lowering their risk of infection from reused N95 masks.

Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet? an open-hardware, open-software wrist-worn computing device? and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.

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.

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.

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.

For decades sensing systems have relied solely on battery power for execution of all activities; this has caused the focus of much research to go towards reducing energy consumption to extend the usable lifetime of a sensor. Recently, a new class of batteryless devices has arisen that promise operation in perpetuity, but often at the cost of reliability, and complexity. Programming, profiling, debugging, and building these applications is a significant challenge; designers must often be capable of implementing custom hardware to manage energy, while writing code in an environment that does not guarantee task completion. In this abstract, we motivate batteryless sensing, examine the state of the art, and propose a novel approach to programming, profiling, debugging, and building, tiny, batteryless 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.

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.

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.

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.