Cardiovascular diseases remain the leading cause of death worldwide, and Doppler blood flow indices play a crucial role in their early detection and diagnosis. Existing ultrasound devices, though effective, are costly, bulky, and unsuitable for daily or at-home use. In this work, we introduce Phonotonos, a system that transforms commodity smartphones into portable and accessible tools for through-skin ultrasonic sensing of blood flow velocity. We demonstrate that despite smartphones' inherent limitations—including low ultrasound power, coarse spatial resolution, and lack of beamforming—blood flow velocity waveforms can still be recovered using novel phase-based signal processing, including nonlinearity cancellation and baseline drift removal. We further propose a triple-modality framework that fuses Doppler ultrasound, arterial sound, and IMU for robust artery localization and interfering motion (e.g., involuntary hand motion) rejection. Extensive simulations, phantom experiments, and IRB-approved human studies validate that our proposed system can measure four key Doppler indices (AT, S/D ratio, RI, PI) with accuracy comparable to dedicated medical devices. These results highlight the potential of smartphones to democratize vascular health monitoring and enable continuous cardiovascular screening in everyday environments.

This paper explores an emerging sensing modality for wearable metabolic-rate estimation: using a harvesting-optimized thermoelectric generator (TEG) as a metabolic sensor. Rather than optimizing harvesting hardware or studying intermittent runtimes, we treat the electrical energy produced by a commercial wrist-worn TEG as a physiological signal reflecting on-body heat transfer. Because harvested energy is influenced by both metabolic heat and transport effects (e.g., convection, contact, and microclimate), we pair it with lightweight thermal and motion context to assess feasibility.

Wireless charging is a cornerstone technology for next-generation mobile and ubiquitous computing. However, its practical deployment has long been constrained by short range, poor flexibility, and lack of support for dynamic multi-device scenarios. In this paper, we propose ChargeX—a system that enables long-range and mobility-resilient wireless charging for multiple small devices. ChargeX pioneers the integration of metasurface-assisted magnetic beamforming, a high-frequency compact transceiver design, and a real-time closed-loop feedback-control mechanism. It further advances the field by introducing a joint optimization framework for dynamically allocating energy across mobile receivers with heterogeneous priorities and spatial-temporal demands. Experimental results demonstrate that it achieves meter-level charging distance, real-time response to device movement, and efficient coordination among multiple receivers, significantly outperforming state-of-the-art prototypes.

This paper presents a novel human-body communication technology that enables capacitive intra-body backscatter (C-IBB) communication between a batteryless ring sensor and a wrist-worn transceiver. C-IBB leverages the finite conductivity of human skin and air coupling capacitance to facilitate nearfield communication (NFC) between wearable devices. The C-IBB system features a radio frequency energy harvester connected to an impedance-matched wearable electrode, which charges a capacitor. This energy storage capacitor powers an ultra-lowpower microcontroller, enabling backscatter communication by modulating the electrode's load impedance. In this work, we developed a modular heterodyne transceiver system and intrabody channel gain emulator. These tools optimize transceiver and tag systems for realistic channel gains tailored to specific electrode configurations. We validated the system's performance on the human body, optimizing it for sensing applications in a wearable ring format. Our preliminary study reveals that the system supports a bit rate of 20.83 kbps with a bit error rate of 10−3to 10−2and operates effectively within a range of 23 cm.

Latent spaces offer an efficient and effective means of summarizing data while implicitly preserving meta-information through relational encoding. We leverage these meta-embeddings to develop a modality-agnostic, unified encoder. Our method employs sensor-latent fusion to analyze and correlate multimodal physiological signals. Using a compressed sensing approach with autoencoder-based latent space fusion, we address the computational challenges of biosignal analysis on resource-constrained devices. Experimental results show that our unified encoder is significantly faster, lighter, and more scalable than modality-specific alternatives, without compromising representational accuracy.

Voice-based authentication is widely used for secure access, yet it remains vulnerable to replay attacks. We present a lightweight privacy-aware liveness detection system leveraging acoustic feature engineering to distinguish genuine voices from spoofed attempts. Using the ASVspoof 2017 dataset, our method achieves EER of 16.2% while being faster than existing DNN-based liveness detection models. We show that higher order formant frequencies, reverberation, and group delay play a crucial role in liveness detection. Our approach provides a privacy-conscious method for preventing attacks on voice-based authentication systems, so that security does not come at the cost of privacy.

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.

EEG signals contain highly sensitive information about an individual's mental state, cognitive processes, and health conditions, making privacy preservation crucial. With the rise of commercial headwear capable of capturing EEG signals, developing robust mechanisms for ensuring privacy of such data is imperative. This work aims to protect EEG data privacy in cloud-based processing systems by sending intermediate output after neural network layer splitting to the cloud. We propose a novel holistic Combined Privacy Metric (CPM) that quantifies privacy leakage between raw EEG signals and intermediate outputs. Our study focuses on EEG-based seizure detection using a 1D CNN architecture, achieving accuracy of 96.25%. We evaluate various splitting configurations to optimize the trade-off between privacy preservation and computational efficiency. We find that splitting after the second convolutional layer achieves a CPM of 0.82 with a modest client-side model size of 509kB. This approach significantly enhances EEG data privacy while enabling effective cloud-based analysis, potentially facilitating wider adoption of secure EEG technologies in healthcare and research applications.

Wireless charging has achieved promising progress in recent years. However, the severe bottlenecks are the small charging range and poor flexibility. This paper presents ChargeX to enable smart and long-range wireless charging for small mobile devices. ChargeX incorporates emerging smart metasurface into the magnetic resonance coupling-based wireless charging to extend the charging range and accommodates the mobility of charging device. Unlike previous endeavors in metasurface-assisted wireless charging that focused on simulation, ChargeX makes efforts across software and hardware to meet three crucial requirements for a practical wireless charging system: (i) realize high-freedom and accurate metasurface control under the premise of low loss; (ii) obtain real-time feedback from the receiver and make effective manipulation for transmitted magnetic flux; and (iii) generate a proper AC signal source at the desired frequency band. We developed a prototype of ChargeX, and evaluated its performance through controlled experiments and real-world phone charging. Extensive experiments demonstrate the great potential of ChargeX for long-range and flexible wireless charging with a compact receiver design.

Acoustic signal has been used to sense important contextual information of targets such as location and movement. This paper explores its potential for measuring the geometric information of moving targets, i.e., shape and size. We propose SONDAR, a novel shape and size measurement system using Inverse Synthetic Aperture Radar (ISAR) imaging on commodity devices. We first design a Doppler-free echo alignment method to accurately align target reflections even if there exists a severe Doppler effect. Then we down-convert the received signals reflected from multiple scatter points on the target and construct a modulated downchirp signal to generate the image. We further develop a lightweight approach to extract the geometric information from a 2-D frequency image. We implement and evaluate a proof-of-concept system on both a Bela platform and a smartphone. Extensive experiments show that we can correctly estimate the target shape and achieve a millimeterlevel size measurement accuracy. Our system can achieve high accuracies even when the target moves along a deviated trajectory, at a relatively high speed, and under obstruction.

Sub-6GHz and mmWave complement each other in the next generation of wireless communications for wide coverage and high capacity. However, there is still a gap between current network technology and seamless connection in indoor environments due to the inevitable occlusions that particularly affect higher frequency bands like Wi-Fi 5GHz and mmWave. To overcome this gap, economical tunable metasurfaces offer a promising solution by redirecting the beam direction of incoming waves to bypass blockages. However, existing metasurface technologies focus on a single frequency band and lack a theoretical framework to guide surface design for multiple desired bands, limiting their potential for diverse applications.

Over the past couple of years, the Capacitive Intra-Body Power Transfer (C-IBPT) technology, which uses the human body as a wireless power transfer medium via capacitive links, has received tremendous attention in the field as a potential solution to support a network of battery-free body sensors. However, circuit modeling of C-IBPT systems, despite its importance in supporting the reliable operation of battery-free body sensors, has been significantly understudied in the field. This paper proposes a finite element model (FEM) and equivalent linear circuit models to estimate path loss and inter-electrode capacitance of a C-IBPT system. As a demonstrative example, the model approximates a typical human forearm (from wrist to elbow) and allows for investigation of the transmission loss between a skin-coupled power transmitter and a receiver in the electro-quasistatic domain. The computed transmission loss from the proposed model is further validated against experimental measurements obtained from five healthy human subjects using a wearable 40 MHz radio frequency (RF) transmitter and an isolated power receiver system in a laboratory environment. The preliminary experimental data show an approximate 40 dB transmission loss within 10 cm body channel length for the parallel plate electrode configuration with dimensions of 30 mm ×40 mm. The simulation finding shows a lower transmission loss of 35 dB and 13.5 fF coupling capacitance across a 10 cm body channel.

Advances in flexible conductive substrates such as conductive wallpaper and paint present new opportunities for optimizing the performance of IoT nodes in smart homes and buildings. In this paper, we explore an unconventional use of such substrates for pulling frequencies of oscillators across IoT devices and wireless front-ends connected to the substrate. We show that by using this technique, we can replace precise crystal oscillators by lower precision and lower cost ceramic oscillators without compromising their ability to be used for tasks that require precise frequencies such as frequency-synchronized multi-static backscatter and synchronized sampling. We present an end-to-end design including a) analysis of conditions under which frequency pulling of oscillators across conductive substrates can work, b) a new technique to detect frequency locking across oscillators without requiring explicit communication, and c) an adaptive method that can be used to synchronize oscillators at minimum power consumption. We then show that these elements can be composed to design a high-performance multi-static backscatter system that performs as well as one that uses a shared high-precision clock but at an order of magnitude less monetary cost. We show that our system can scale and operate at very low power, while having low complexity since it requires no explicit interaction among devices attached to the substrate.

Small IoT devices deployed in challenging locations suffer from uneven 3D coverage in complex environments. This work optimizes indoor coverage with LAVA, a Large Array of Vanilla Amplifiers. LAVA is a standard-agnostic cooperative mesh of elements, i.e., RF devices each consisting of several switched input and output antennas connected to fixed-gain amplifiers. Each LAVA element is further equipped with rudimentary power sensing to detect nearby transmissions. The elements report power readings to the LAVA control plane, which then infers active link sessions without explicitly interacting with the endpoint transmitter or receiver. With simple on-off control of amplifiers and antenna switching, LAVA boosts passing signals via multi hop amplify-and-forward. LAVA explores a middle ground between smart surfaces and physical-layer relays. Multi-hopping over short inter-hop distances exerts more control over the end-to-end trajectory, supporting fine-grained coverage and spatial reuse. Ceiling testbed results show throughput improvements to individual Wi-Fi links by 50% on average and up to 100% at 15 dBm transmit power (193% on average, up to 8x at 0 dBm). ZigBee links see up to 17 dB power gain. For pairs of co-channel concurrent links, LAVA provides average per-link throughput improvements of 517% at 0 dBm and 80% at 15 dBm.

Recent years have seen exciting developments in the use of RFID tags as sensors to enable a range of applications including home automation, health and wellness, and augmented reality. However, widespread use of RFIDs as sensors requires significant instrumentation to deploy tethered readers, which limits usability in mobile settings. Our solution is WearID, a low-power wrist-worn backscatter reader that bridges this gap and allows ubiquitous sensing of interaction with tagged objects. Our end-to-end design includes innovations in hardware architecture to reduce power consumption and deal with wrist attenuation and blockage, as well as signal processing architecture to reliably detect grasping, touching, and other hand-based interactions. We show via exhaustive characterization that WearID is roughly 6× more power efficient than state-of-art commercial readers, provides 3D coverage of 30 to 50 cm around the wrist despite body blockage, and can be used to reliably detect hand-based interactions. We also open source the design of WearID with the hope that this can enable a range of new and unexplored applications of wearables.

Continuous monitoring of hand function in individuals with upper limb paresis, such as stroke survivors, could provide a quantitative assessment of their real-world functional performance, which has great potential to enhance the clinical guidance of rehabilitation interventions. In this paper, we explore a novel wearable approach to quantify the amount of hand use by leveraging Radio Frequency Identification (RFID) technologies. We introduce a prototype implementation of our wearable RFID system composed of a wrist-worn reader (antenna) and a small passive tag placed on a fingernail. Then, we discuss a machine learning-based data analytic pipeline that analyzes the backscattered RF signal to estimate the amount of hand use. The accuracy of the system is validated against an optoelectronic motion capture system - the gold standard for human movement analyses - using a dataset collected from five neurologically intact individuals. The proposed wearable RFID system could accurately estimate the amount of hand use with R2of 0.67 and Normalized Root Mean Square Error of 7.3%, and shows great potential for clinical applications.

The ability to monitor eye closures and blink patterns has long been known to enable accurate assessment of fatigue and drowsiness in individuals. Many measures of the eye are known to be correlated with fatigue including coarse-grained measures like the rate of blinks as well as fine-grained measures like the duration of blinks and the extent of eye closures. Despite a plethora of research validating these measures, we lack wearable devices that can continually and reliably monitor them in the natural environment. In this work, we present a low-power system, iLid, that can continually sense fine-grained measures such as blink duration and Percentage of Eye Closures (PERCLOS) at high frame rates of 100fps. We present a complete solution including design of the sensing, signal processing, and machine learning pipeline and implementation on a prototype computational eyeglass platform.

In this work, we present SkinnyPower, a technology for Intra-Body Power Transfer (IBPT) that wirelessly transfers power through human skin to operate batteryless wearable sensors. We envision a scenario, in which batteryless sensors placed on small body parts (e.g., on-finger, in-ear, and in-mouth) can obtain operating power from another body-worn energy sources (e.g., already existing battery-powered wearable devices such as a smartwatch or a BandAid-like battery patch attached to the neck). The key technical challenges in realizing this vision include 1) providing a robust return path in the body channel - where the forward (power signal) and return (ground) paths are not explicitly defined - using implicit capacitances formed between the devices and earth ground, and 2) achieving reliable operation despite variations in capacitive coupling between the skin and devices, devices and earth ground, and conductance of the subdermal layer. We identify and optimize critical system design parameters to maximize the power transfer between a transmitter and a receiver with the capacitively coupled return path. To demonstrate and validate the concept of IBPT, we implemented a prototype consisting of 1) a wrist-worn, battery-equipped power transmitter that sends alternating current through the human body and 2) a finger-worn, batteryless sensor device that operates solely on body-transferred power. Evaluations on five subjects show that we can reliably support the power of approximately 1 mW, which can be used to operate an embedded system, continuously collect sensor (e.g. accelerometer) data, and wirelessly transfer the collected data in real-time using Bluetooth Low Energy. Moreover, we achieve a power transfer rate of 14.5% between the transmitter and receiver, which is significantly higher than other wireless power transfer techniques such as RFID. We believe that the proposed system has great potential to transform current architectures and designs for body-area networks, promoting the development of innovative on-body sensors that would otherwise not be possible with on-device batteries.

In this work, we present SkinnyPower, a concept of Intra-Body Power Transfer (IBPT) that wirelessly transfers power through human skin to operate batteryless wearable sensors. We envision a scenario, in which batteryless sensors placed on small body parts (e.g., on-finger, in-ear, and in-mouth) can obtain operating power from another body-worn energy sources (e.g., already existing battery-powered wearable devices such as a smartphone, smartwatch, or BandAid-like battery patch attached to the neck). To demonstrate and validate the concept of IBPT, we implemented a prototype consisting of 1) a wrist-worn, battery-powered power transmitter that sends alternating current through the human body and 2) a finger-worn, batteryless sensor device that operates solely on body-transferred power. Evaluations on five subjects show that we can reliably support the power of approximately 1 mW, which can be used to operate an embedded system, continuously collect sensor (accelerometer) data, and wirelessly transfer the collected data in real-time using Bluetooth Low Energy. We believe that the proposed system has great potential to transform current architectures and designs for body-area networks, promoting the development of innovative on-body sensors that would otherwise not be possible with on-device batteries.

Smart apparel with embedded sensors have the potential to revolutionize human behavior sensing by leveraging everyday clothing as the sensing substrate. However, existing textile-based sensing techniques rely on tight-fitting garments to obtain sufficient signal to noise, making it uncomfortable to wear and limiting the technology to niche applications like athletic performance monitoring.

Duty-cycling has emerged as the predominant method for optimizing power consumption of low-power radios, particularly for sensors that transmit sporadically in small bursts. But duty-cycling is a poor fit for applications involving high-rate sensor data from wearable sensors such as IMUs, microphones, and imagers that need to stream data to the cloud to execute sophisticated machine learning models.

Smart spaces, such as smart homes and smart offices, are common Internet of Things (IoT) scenarios for building automation with networked sensors. In this paper, we suggest a different notion of smart spaces, where the radio environment is programmable to achieve desirable link quality within the space. We envision deploying low-cost devices embedded in the walls of a building to passively reflect or actively transmit radio signals. This is a significant departure from typical approaches to optimizing endpoint radios and individual links to improve performance. In contrast to previous work combating or leveraging per-link multipath fading, we actively reconfigure the multipath propagation. We sketch design and implementation directions for such a programmable radio environment, highlighting the computational and operational challenges our architecture faces. Preliminary experiments demonstrate the efficacy of using passive elements to change the wireless channel, shifting frequency "nulls" by nine Wi-Fi subcarriers, changing the 2 x 2 MIMO channel condition number by 1.5 dB, and attenuating or enhancing signal strength by up to 26 dB.

Communication costs dominate the energy consumption, and ultimately limit the utility, of low power devices and sensor nodes. Backscatter communication based on deliberate and ambient sources has the potential to radically alter this paradigm by offering two to three orders of magnitude better communication efficiency (in terms of nJ/Bit) then conventional radio architectures. Initial work on ambient backscatter shows promising results but has focused on narrow band operation in well controlled laboratory settings. The goal of this work is to enable the ubiquitous deployment of ultra-low power nodes that communicate via ambient backscatter to wired Universal Backscatter Readers, in real-world environments. This is accomplished through ultra-wideband backscatter techniques that leverage the breath of commercial broadcast signals in the 80 MHz to 900 MHz range from FM radios, digital TVs, and cellular networks. Additionally the use of powered Universal Backscatter Readers allows a network of ultra-low power nodes to operate on ambient carriers as low as -80 dBm, which is typical for indoor home and office environments. For the first time we demonstrate the simultaneous use of 17 ambient signal sources to achieve node-to-reader communication distances of 50 meters, with data rates up to 1 kbps.

This paper presents TypingRing, a wearable ring platform that enables text input into computers of different forms, such as PCs, smartphones, tablets, or even wearables with tiny screens. The basic idea of TypingRing is to have a user wear a ring on his middle finger and let him type on a surface - such as a table, a wall, or his lap. The user types as if a standard QWERTY keyboard is lying underneath his hand but is invisible to him. By using the embedded sensors TypingRing determines what key is pressed by the user. Further, the platform provides visual feedback to the user and communicates with the computing device wirelessly. This paper describes the hardware and software prototype of TypingRing and provides an in-depth evaluation of the platform. Our evaluation shows that TypingRing is capable of detecting and sending key events in real-time with an average accuracy of 98.67%. In a field study, we let seven users type a paragraph with the ring, and we find that TypingRing yields a reasonable typing speed (e.g., 33-50 keys per minute) and their typing speed improves over time.

This paper presents a remote gesture input solution for interacting indirectly with user interfaces on mobile and wearable devices. The proposed solution uses a wearable ring platform worn on users index finger. The ring detects and interprets various gestures performed on any available surface, and wirelessly transmits the gestures to the remote device. The ring opportunistically harvests energy from an NFC-enabled phone for perpetual operation without explicit charging. We use a finger-tendon pressure-based solution to detect touch, and a light-weight audio based solution for detecting finger motion on a surface. The two level energy efficient classification algorithms identify 23 unique gestures that include tapping, swipes, scrolling, and strokes for hand written text entry. The classification algorithms have an average accuracy of 73% with no explicit user training. Our implementation supports 10 hours of interactions on a surface at 2 Hz gesture frequency. The prototype was built with off-the-shelf components has a size similar to a large ring.

Paper displays have a number of attractive properties, in particular the ability to present visual information perpetually with no power source. However, they are not digitally updatable or re-usable. Bistable display materials, such as e-paper, promise to enable displays with the best properties of both paper and electronic displays. However, rewriting a pixelated bistable display requires substantial energy, both for communication and for setting the pixel states.

Near Field Communication (NFC) on mobile phones presents new opportunities and threats. While NFC is radically changing how we pay for merchandise, it opens a pandora's box of ways in which it may be misused by unscrupulous individuals. This could include malicious NFC tags that seek to compromise a mobile phone, malicious readers that try to generate fake mobile payment transactions or steal valuable financial information, and others. In this work, we look at how to protect mobile phones from these threats while not being vulnerable to them. We design a small form-factor "patch", EnGarde, that can be stuck on the back of a phone to provide the capability to jam malicious interactions. EnGarde is entirely passive and harvests power through the same NFC source that it guards, which makes our hardware design minimalist, and facilitates eventual integration with a phone. We tackle key technical challenges in this design including operating across a range of NFC protocols, jamming at extremely low power, harvesting sufficient power for perpetual operation while having minimal impact on the phone's battery, designing an intelligent jammer that blocks only when specific blacklisted behavior is detected, and importantly, the ability to do all this without compromising user experience when the phone interacts with a legitimate external NFC device.

Near Field Communication (NFC) on mobile phones presents new opportunities and threats. While NFC is radically changing how we pay for merchandise, it opens a pandora's box of ways in which it may be misused by unscrupulous individuals. This could include malicious NFC tags that seek to compromise a mobile phone, malicious readers that try to generate fake mobile payment transactions or steal valuable financial information, and others. In this demo, we show the capabilities of our hardware solution, EnGarde, that protects a mobile phone from these vulnerabilities.

RFID-scale sensors present a new frontier for distributed sensing. In contrast to existing sensor deployments that rely on battery-powered sensors, RFID-scale sensors rely solely on harvested energy. These devices sense and store data when not in contact with a reader, and use backscatter communication to upload data when a reader is in range. Unlike conventional RFID tags that only transmit identifiers, RFID sensors need to transfer potentially large amounts of data to a reader during each contact event. In this paper, we propose several optimizations to the RFID network stack to support efficient bulk transfer while remaining compatible with existing Gen 2 readers. Our key contribution is the design of a coordinated bulk transfer protocol for RFID-scale sensors that maximizes channel utilization and minimizes energy lost due to idle listening while also minimizing collisions. We present an implementation of the protocol for the Intel WISP, and describe several parameters that are tuned using empirical measurements that characterize the wireless channel. Our results show that the burst protocol improves goodput in comparison to vanilla EPC Gen 2 tags, improves energy-efficiency, allows multiple RFID sensors to share the channel, and also coexists with passive, non-sensor tags.

Near Field Communications (NFC) is an emerging technology that conveniently establishes radio communication by bringing two entities in close proximity of one another. Many use cases for devices equipped with this technology have been proposed ranging from payment systems to convenient data exchange.

Backscatter communication offers an ultra-low power alternative to active radios in urban sensing deployments - communication is powered by a reader, thereby making it virtually "free". While backscatter communication has largely been used for extremely small amounts of data transfer (e.g. a 12 byte EPC identifier from an RFID tag), sensors need to use backscatter for continuous and high-volume sensor data transfer. To address this need, we describe a novel link layer that exploits unique characteristics of backscatter communication to optimize throughput. Our system offers several optimizations including 1) understanding of multi-path self-interference characteristics and link metrics that capture these characteristics, 2) design of novel mobility-aware probing techniques that use backscatter link signatures to determine when to probe the channel, 3) bitrate selection algorithms that use link metrics to determine the optimal bitrate, and 4) channel selection mechanism that optimize throughput while remaining compliant within FCC regulations. Our results show upto 3x increase in goodput over other mechanisms across a wide range of channel conditions, scales, and mobility scenarios.

Mobile sensing is difficult without power. Emerging Computational RFIDs (CRFIDs) provide both sensing and general-purpose computation without batteries--instead relying on small capacitors charged by energy harvesting. CRFIDs have small form factors and consume less energy than traditional sensor motes. However, CRFIDs have yet to see widespread use because of limited autonomy and the propensity for frequent power loss as a result of the necessarily small capacitors that serve as a microcontroller's power supply. Our results show that hybrid harvesting CRFIDs, which use an ambient energy micro-harvester, can complete a variety of useful workloads--even in an environment with little ambient energy available.

To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distanceYC while being robust to wireless effects caused by node mobility. In this paper, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range and interference diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system while handling node mobility. Our results also show that our system can handle short, medium and long-term wireless interference in such environments.

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distances while being robust to wireless effects caused by node mobility. In this paper, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogeneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system.

RFID sensor networks comprising batteryless devices that are passively powered by RFID readers present exciting possibilities for ubiquitous computing applications. They require minimal maintenance, are cheap to manufacture and have small form factor. However, their lack of autonomy due to the need for constant power from an RFID reader hinders their deployment. We demonstrate that RFIDs augmented with ambient energy-harvesting capabilities may be used as a first-class sensor platform, allowing them to operate untethered from reader infrastructure. Specifically, we show that a CRFID-based accelerometer sensor can provide both real-time and delayed access to time-stamped sensor data when provided with a small amount of solar energy. The data is collected using a standard RFID reader and displayed in a graphical interface.