The growing popularity of wearable camera glasses raises pressing concerns about bystanders being recorded without their consent. Most existing privacy-enhancing technologies (PETs) rely on opt-out models that place the burden of privacy protection on bystanders. We conducted a qualitative study on wearers’ and bystanders’ perceptions of opt-in, privacy-by-default approaches for camera glasses. To enable this study, we designed and evaluated an opt-in privacy-by-default protocol. We then conducted semi-structured interviews with camera glass wearers and bystanders (N = 18) to examine their perceptions of the protocol. Our findings show that bystanders viewed the opt-in protocol as essential and advocated for even stronger anonymization. Wearers appreciated the protocol’s safeguards but found it visually limiting, expressing desire for a context-dependent version that can be enabled in relevant scenarios. Our findings highlight the need for context-aware PETs that provide effective mechanisms for consent negotiation.

Large language models (LLMs) are being increasingly deployed in healthcare, influencing diagnostic reasoning and clinical workflows. However, evidence of clinician engagement with these systems, how they prompt, constrain, and verify output, remains scarce, particularly in low- and middle-income countries (LMICs). We conducted a mixed-methods study with physicians in Pakistan: (1) logging their interactions while they solved expert-designed clinical vignettes with optional LLM assistance, and (2) interviewing 12 participants about generative-AI-supported diagnosis. Findings highlight diverse prompting strategies from role assignment to cautious scaffolding, with consistent insistence on human oversight. Interviews reveal pragmatic enthusiasm for LLMs as a “second brain” in resource-constrained settings, tempered by skepticism about reliability, privacy, and patient trust. This study contributes evidence of physician-LLM interaction patterns in an LMIC context, a taxonomy of prompting strategies and oversight mechanisms, and design implications for responsible AI integration in healthcare workflows.

Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs for context-aware code approximations. CheckMate integrates validation of LLM-generated approximations to ensure correct execution and employs Bayesian optimization to fine-tune approximation parameters autonomously, eliminating the need for developer input. Tested across six IoT applications, it reduces power cycles by up to 60% with an accuracy loss of just 8%, outperforming semi-automated tools like ACCEPT in speedup and accuracy. CheckMate's results establish it as a robust, user-friendly tool and a foundational step toward automated approximation frameworks for intermittent computing.

Batteryless IoT devices, powered by energy harvesting, face significant challenges in maintaining operational efficiency and reliability due to intermittent power availability. Traditional checkpointing mechanisms, while essential for preserving computational state, introduce considerable energy and time overheads. This paper introduces Approxify, an automated framework that significantly enhances the sustainability and performance of batteryless IoT networks by reducing energy consumption by approximately 40% through intelligent approximation techniques. Approxify balances energy efficiency with computational accuracy, ensuring reliable operation without compromising essential functionalities. Our evaluation of applications, SUSAN and Link Quality Indicator (LQI), demonstrates significant reductions in checkpoint frequency and energy usage while maintaining acceptable error bounds.

We present hardware/software techniques to intelligently regulate supply voltage and clock frequency of intermittently computing devices. These devices rely on ambient energy harvesting to power their operation and small capacitors as energy buffers. Statically setting their clock frequency fails to capture the unique relations these devices expose between capacitor voltage, energy efficiency at a given operating frequency, and the corresponding operating range. Existing dynamic voltage and frequency scaling techniques are also largely inapplicable due to extreme energy scarcity and peculiar hardware features. We introduce two hardware/software co-designs that accommodate the distinct hardware features and function within a constrained energy envelope, offering varied tradeoffs and functionalities. Our experimental evaluation combines tests on custom-manufactured hardware and detailed emulation experiments. The data gathered indicate that our approaches result in up to 3.75× reduced energy consumption and 12× swifter execution times compared to the considered baselines, all while utilizing smaller capacitors to accomplish identical workloads.

In developing countries, majority of the households use overhead water tanks to have running water. These water tanks are exposed to the elements, which usually render the tap water uncomfortable to use, given the extreme subtropical weather conditions. Externally weatherproofing these tanks to maintain the groundwater temperature is short-lived, and only results in a marginal (0.5°C–1°C) improvement in tap water temperature. We propose Ashray , an IoT-inspired, intelligent system to minimize the exposure of water to the elements thereby maintaining its temperature close to that of the groundwater. Ashray learns the water demand patterns of a household and pumps water into the overhead tank only when necessary. The predictive, machine learning based, approach of Ashray improves water comfort by up to 8°C in summers and 3°C in winters, on average. Ashray is retrofitted into existing infrastructure with a hardware prototyping cost of $27, whereas it can save up to 16% on water heating costs, through reduction in natural gas consumption, by leveraging groundwater temperature. Moreover, we also consider a transiently-powered Ashray , which uses the energy harvested from the ambient environment, and propose an intermittent data pipeline to improve its prediction accuracy. The transiently-powered Ashray is suitable for long-term deployment, requires minimal maintenance and delivers approximately the same performance. Ashray has the potential to improve the thermal comfort and reduce energy costs for millions of households in developing countries.

In developing countries, majority of the households use overhead water tanks to have running water in their taps. These water tanks are exposed to the elements, which usually render the tap water uncomfortable to use, given the extreme subtropical weather conditions. Externally weatherproofing these tanks to maintain the groundwater temperature is short-lived, and only results in a marginal (0.5 −1◦C) improvement in tap water temperature. We propose Ashray, an IoT-inspired, intelligent system to minimize the exposure of water to the elements thereby maintaining its temperature close to that of the groundwater. Ashray learns the water demand patterns of a household and pumps water into the overhead tank only when necessary. The predictive, machine learning based, approach of Ashray improves water comfort by up to 8◦C in summers and 3◦C in winters, on average. Ashray is retrofitted into existing infrastructure with a hardware prototyping cost of $27, whereas it can save up to 16% on water heating costs, through reduction in natural gas consumption, by leveraging groundwater temperature. Our proposed system, Ashray, can positively impact the lives of millions of people in developing countries.

We present the design and evaluation of a 3.5-year embedded sensing deployment at the Mithræum of Circus Maximus, a UNESCO-protected underground archaeological site in Rome (Italy). Unique to our work is the use of energy harvesting through thermal and kinetic energy sources. The extreme scarcity and erratic availability of energy, however, pose great challenges in system software, embedded hardware, and energy management. We tackle them by testing, for the first time in a multi-year deployment, existing solutions in intermittent computing, low-power hardware, and energy harvesting. Through three major design iterations, we find that these solutions operate as isolated silos and lack integration into a complete system, performing suboptimally. In contrast, we demonstrate the efficient performance of a hardware/software co-design featuring accurate energy management and capturing the coupling between energy sources and sensed quantities. Installing a battery-operated system alongside also allows us to perform a comparative study of energy harvesting in a demanding setting. Albeit the latter reduces energy availability and thus lowers the data yield to about 22% of that provided by batteries, our system provides a comparable level of insight into environmental conditions and structural health of the site. Further, unlike existing energy-harvesting deployments that are limited to a few months of operation in the best cases, our system runs with zero maintenance since almost 2 years, including 3 months of site inaccessibility due to a COVID19 lockdown.

Intermittently powered embedded devices ensure forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to the execution times. To minimize this overhead, we present DICE, a system that renders differential checkpointing profitable on these devices. DICE is unique because it is a software-only technique and efficient because it only operates in volatile main memory to evaluate the differential. DICE may be integrated with reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems, and arbitrary code can be enabled with DICE using automatic code-instrumentation requiring no additional programmer effort. By reducing the cost of checkpoints, DICE cuts the peak energy demand of these devices, allowing operation with energy buffers that are one-eighth of the size originally required, thus leading to benefits such as smaller device footprints and faster recharging to operational voltage level. The impact on final performance is striking: with DICE, Hibernus requires one order of magnitude fewer checkpoints and one order of magnitude shorter time to complete a workload in real-world settings.

Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption . Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations. We rather demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.

Embedded devices running on ambient energy perform computations intermittently, depending upon energy availability. System support ensures forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to execution times. To reduce this overhead, we present DICE, a system design that efficiently achieves differential checkpointing in intermittent computing. Distinctive traits of DICE are its software-only nature and its ability to only operate in volatile main memory to determine differentials. DICE works with arbitrary programs using automatic code instrumentation, thus requiring no programmer intervention, and can be integrated with both reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems. By reducing the cost of checkpoints, performance markedly improves. For example, using DICE, Hibernus requires one order of magnitude shorter time to complete a fixed workload in real-world settings.

Transiently-powered computers (TPCs) lay the basis for a battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption, which determine energy efficiency. We demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and develop EPIC, a compile-time energy analysis tool. We use EPIC to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, programmers avoid unnecessary program changes that hurt energy efficiency.

The resource-constrained devices of the battery-less Internet of Things are powered off energy harvesting and compute intermittently, as energy is available. Forward progress of programs is ensured by creating persistent state. Mixed-volatile platforms are thus an asset, as they map slices of the address space onto non-volatile memory. However, these platforms also possibly introduce intermittence bugs, where intermittent and continuous executions differ.

Energy harvesting enables battery-less sensing applications, but causes executions to become intermittent as a result of erratic energy provisioning. Intermittent executions pose challenges to peripheral consistency that threaten to leave peripheral-bound workloads in failed states or to impede forward progress of programs. Intermittent synchronous peripheral operations are supported in existing literature for specific kinds of peripherals. Asynchronous peripheral operations enable reactive concurrency in application implementations, which increases reactivity and improves energy consumption, but lack dedicated support in intermittent settings. We present Karma, the first general abstraction and system design to support both synchronous and asynchronous operations in an intermittent setting. Karma employs a novel combination of peripheral roll-forward and computation roll-back to a rendezvous point guaranteeing consistency. It remains transparent to application programmers and peripheral driver, which favours portability. Our evaluation, based on three applications running on prototype hardware and using diverse energy sources, indicates that intermittent asynchronous peripheral support provided by Karma boosts data throughput by 83% compared to existing literature.

IoT deployments often require communication between devices that employ heterogeneous wireless technologies. Traditionally, expensive gateways are used to relay packets between heterogeneous nodes. Recent cross-technology communication offers a low bandwidth alternative, which is only feasible when communication between such nodes is limited to simple binary commands. In contrast, our work capitalizes on the increasing presence of multi-standard radio chips in mainstream IoT devices, to provide a new perspective on how to enable direct communication between heterogeneous nodes. We design Scylla---a software control layer---that allows multiple wireless stacks to coexist on top of a single radio chip, thereby simultaneously offering multiple communication interfaces. Uniquely, Scylla achieves near stack-native performance and requires no changes to the standards.

We propose a set of differential techniques to allow transientlypowered embedded devices reduce the amount of data written on non-volatile memory during checkpoints used to cross times of energy unavailability. These techniques track modifications in the application state to isolate data from the slice of the previous checkpoint that remains unaltered. At the following checkpoint, our approach may thus only update the parts it detects as modified. This allows us to shift part of the energy budget from checkpointing overhead to useful computations, yielding better overall energy efficiency.

Emerging countries predominantly rely on room-level air conditioning units (window ACs, space heaters, ceiling fans) for thermal comfort. These distributed units have manual, decentralized control leading to suboptimal energy usage for two reasons: excessive setpoints by individuals and inability to interleave different conditioning units for energy savings. We propose a novel inverted HVAC approach: cheaply retrofitting these distributed units with “on-off” control and providing centralized control augmented with room and environmental sensors. Our binary control approach exploits an understanding of device consumption characteristics and factors this into the control algorithms to reduce consumption. We implement this approach as H awadaar in a prototype 180ft 2 room to evaluate its efficacy over a 7-month period experiencing both hot and cold climates. Through a post analysis, we show that our on-off algorithms are not far from a theoretically optimal approach based on a priori information that precisely knows the optimal control points to minimize consumption. We collect enough evidence to plausibly scale our empirical evaluation, demonstrating countrywide benefits: with just 20% market penetration, H awadaar can save up to 6% of electricity per capita in residential and commercial sectors—resulting in a substantial countrywide impact.

Wireless energy transfer has recently emerged as a promising alternative to realize the vision of perpetual embedded sensing. However, this technology transforms the notion of energy from merely a node’s local commodity to, similarly to data, a deployment-wide shareable resource. The challenges of managing a shareable energy resource are much more complicated and radically different from the research of the past decade: Besides energy-efficient operation of individual devices, we also need to optimize networkwide energy distribution. To counteract these challenges, we propose an energy stack , a layered software model for energy management in future transiently powered embedded networks. An initial specification of the energy stack, which is based on the historically successful layered approach for data networking, consists of three layers: (i) the transfer layer, which deals with the physical transfer of energy; (ii) the scheduling layer, which optimizes energy distribution over a single hop; and (iii) the network layer, creates a global view of the energy in the network for optimizing its networkwide distribution. As a contribution, we define the interfacing APIs between these layers, delineate their responsibilities, identify corresponding challenges, and provide a first implementation of the energy stack. Our evaluation, using both experimental deployments and high-level simulations, establishes the feasibility of a layered solution to energy management under transient power.

We propose dynamic reconfiguration of the radio interface in IoT platforms to support multiple link layers simultaneously. This allows us to tackle the increasing link layer heterogeneity in IoT devices, thus bringing a multitude of benefits: extensible and vender agnostic multihop deployments, rapid integration of the new “things” into an existing network, as well as seamless integration of the IoT with the traditional wireless Internet.

We propose incremental checkpointing techniques enabling transiently powered devices to retain computational state across multiple activation cycles. As opposed to the existing approaches, which checkpoint complete program state, the proposed techniques keep track of modified RAM locations to incrementally update the retained state in secondary memory, significantly reducing checkpointing overhead both in terms of time and energy.

Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems’ lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite , yet intermittent , energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about.

Applications and protocols for wireless and mobile systems have to deal with volatile environmental conditions such as interference, packet loss, and mobility. Utilizing cross-layer information from other protocols and system components such as sensors can improve their performance and responsiveness. However, application and protocol developers lack a convenient way of monitoring, experimenting and specifying optimizations to evaluate their cross-layer ideas. We present CRAWLER, a novel experimentation architecture for system monitoring and cross-layer-coordination that facilitates evaluation of applications and wireless protocols. It alleviates the problem of complicated access to relevant system information by providing a unified interface for accessing application, protocol and system information. The generic design of this interface further enables a convenient and declarative way to specify and experiment how a set of cross-layer optimizations should be composed and adapted at runtime. Our evaluation demonstrates the usability of CRAWLER by experimenting, monitoring and improving TCP's congestion control algorithm.

High bit error rates reduce the performance of wireless networks. This is exacerbated by the enforcement of bit-by-bit correct transmissions and the resulting retransmission overhead. Recently, research has focused on more efficient link layer mechanisms and on tolerating payload errors. Header errors, however, still cause today's network and transport protocols to drop the erroneous packets.

Rapidly changing link conditions make it difficult to accurately estimate the quality of wireless links and predict the fate of future transmissions. In particular bursty links pose a major challenge to online link estimation due to strong fluctuations in their transmission success rates at short time scales. Therefore, the prevalent approach in routing algorithms is to employ a long term link estimator that selects only consistently stable links - PRR >; 90% - for packet transmissions. The use of bursty links is thus disregarded although these links provide considerable additional resources for the routing process. Based on significant empirical evidence of over 100,000 transmissions over each link in widely used 802.15.4 and 802.11 testbeds, we propose two metrics, Expected Future Transmissions (EFT) and MAC3, for runtime estimation of bursty wireless links. We introduce the Bursty Link Estimator (BLE) that, based on these two metrics, accurately estimates bursty links in the network rendering them available for packet transmissions.

Long-term deployments of sensor networks in physically inaccessible environments make remote re-programmability of sensor nodes a necessity. Ranging from full image replacement to virtual machines, a variety of mechanisms exist today to deploy new software or to fix bugs in deployed systems. However, TinyOS - the current state of the art sensor node operating system - is still limited to full image replacement as nodes execute a statically-linked system-image generated at compilation time. In this paper we introduce Dynamic TinyOS to enable the dynamic exchange of software components and thus incrementally update the operating system and its applications. The core idea is to preserve the modularity of TinyOS, i.e.~its componentization, which is lost during the normal compilation process, and enable runtime composition of TinyOS components on the sensor node. The proposed solution integrates seamlessly into the system architecture of TinyOS: It does not require any changes to the programming model of TinyOS and existing components can be reused transparently. Our evaluation shows that Dynamic TinyOS incurs a low performance overhead while keeping a smaller - upto one third - memory footprint than other comparable solutions.

We present Statistical Vector Routing (SVR), a protocol that efficiently deals with communication link dynamics in wireless networks. It assigns virtual coordinates to nodes based on the statistical distribution of their distance from a small set of beacons. The distance metric predicts the current location of a node in its address distribution. Our initial results from a prototype implementation over real testbeds demonstrate the feasibility of SVR.

Complex interactions and the distributed nature of wireless sensor networks make automated testing and debugging before deployment a necessity. A main challenge is to detect bugs that occur due to non-deterministic events, such as node reboots or packet duplicates. Often, these events have the potential to drive a sensor network and its applications into corner-case situations, exhibiting bugs that are hard to detect using existing testing and debugging techniques.

We present TinyWifi, a nesC code base extending TinyOS to support Linux powered network nodes. It enables developers to build arbitrary TinyOS applications and protocols and execute them directly on Linux by compiling for the new TinyWifi platform. Using TinyWifi as a TinyOS platform, we expand the applicability and means of evaluation of wireless protocols originally designed for sensornets towards inherently similar Linux driven ad hoc and mesh networks.

Accurate estimation of link quality is the key to enable efficient routing in wireless sensor networks. Current link estimators focus mainly on identifying long-term stable links for routing. They leave out a potentially large set of intermediate links offering significant routing progress. Fine-grained analysis of link qualities reveals that such intermediate links are bursty, i.e., stable in the short term.

The rising complexity of data processing algorithms in sensor networks combined with their severely limited computing power necessitates a in-depth understanding of their temporal behavior. However, today only cycle accurate emulation and test-beds provide a detailed and accurate insight into the temporal behavior of sensor networks.In this paper we introduce fine grained, automated instrumentation of simulation models with cycle counts derived from sensor nodes and application binaries to provide detailed timing information. The presented approach bridges the gap between scalable but abstracting simulation and cycle accurate emulation for sensor network evaluation.By mapping device-specific code with simulation models, we can derive the time and duration a certain code line takes to get executed on a sensor node. Hence, eliminating the need to use expensive instruction-level emulators with limited speed and restricted scalability. Furthermore, the proposed design is not bound to a specific hardware platform, a major advantage compared to existing emulators. Our evaluation shows that the proposed technique achieves a timing accuracy of 99% compared to emulation while adding only a small overhead. Concluding, it combines essential properties like accuracy, speed and scalability on a single simulation platform.

We present KleeNet, a Klee based bug hunting tool for sensor network applications before deployment. KleeNet automatically tests code for all possible inputs, ensures memory safety, and integrates well into TinyOS based application development life cycle, making it easy for developers to test their applications.