Light Fidelity utilizes light to enable high-bandwidth, low-interference networking and complements traditional radio-frequency links. We demonstrate that Light Fidelity's precise spatial data confinement makes it a natural fit for mixed reality: By exploiting line-of-sight propagation with mixed-reality headset spatial awareness, we create "information spotlights" - which are localized optical zones that provide context-specific data, such as museum exhibit details or retail product information. This avoids radio frequency based localization, which is both computationally intensive and not supported in today's commercial headsets. We present VisibleBits, a proof-of-concept Light Fidelity transceiver integrated with a Meta Quest 3 that converts optical signals into a visual serial protocol, overcoming hardware limitations. In our demo, users receive exhibit-specific real-time information, which is then overlaid onto their mixed reality headsets as they enter a Light Fidelity illumination zone.

Wireless connectivity challenges hinder large-scale deployment of embedded systems. We introduce AudioCast to address two critical issues: spectrum scarcity-induced contention and high power consumption of radio transmitters. The decline of FM-broadcast stations and the ubiquity of FM-receivers motivate the design of Audio-Cast. By leveraging the negative differential resistance of tunnel diodes—which occurs at low power—AudioCast rethinks the conventional transmitter design. Combined with their self-modulation capability, tunnel diode oscillators enable frequency modulated transmissions while consuming under 200 μW. The transmitter achieves upto 130 m range in line-of-sight and tens of meters in non-line-of-sight environments.

Tracking movements in the environment of macroscopic objects enables numerous applications, from monitoring vital signs through body movements to inferring hand gestures. However, current systems overwhelmingly rely on contact-based sensors or energy-consuming radio frequency mechanisms that necessitate complex radio transceivers for receptions. We present ongoing research on a novel low-power sensor that leverages the unique characteristics of tunnel diodes. This sensor can detect minute changes in its vicinity and communicate these changes over radio waves, all while consuming under 150 microwatts of power consumption. Notably, the transmitted radio waves are processed using low-cost, off-the-shelf radio transceivers, resulting in low cost and power consumption. The sensor’s functionality stems from the sensitivity of the resonant frequency of the tunnel diode oscillators to changes in their electromagnetic surroundings. Our early work exhibits its potential for detecting a person’s breathing patterns, and hand gestures.

A confluence of advances in several fields has led to the emergence of mixed-reality headsets. They can enable us to interact with and visualize our environments in novel ways. Nonetheless, mixed-reality headsets are constrained today as their camera systems only capture a narrow part of the visible spectrum. Our environment contains rich information that cameras do not capture. It includes phenomena captured through sensors, electromagnetic fields beyond visible light, acoustic emissions, and magnetic fields. We demonstrate our ongoing work, PixelGen, to redesign cameras for low power consumption and to be able to visualize our environments in a novel manner, making some of the invisible phenomena visible. Pixel-Gen combines low-bandwidth sensors with a monochrome camera to capture a rich representation of the world. This design choice ensures information is communicated energy-efficiently. This information is then combined with diffusion-based image models to generate unique representations of the environment, visualizing the otherwise invisible fields. We demonstrate that together with a mixed reality headset, it enables us to observe the world uniquely.

Large language models are being deployed for various applications, and they are being scaled to larger parameter sizes. However, this makes it challenging to use these models for embedded applications due to the limited computing memory and processing capabilities available on embedded platforms. Consequently, it limits the usage of these models to invocation through function calls to a remotely hosted platform, which introduces latency and privacy challenges. A smaller parameter-sized language model running locally on embedded platforms could mitigate this challenge. However, such smaller models make trade-offs with dataset diversity and parameters, leading to inaccurate prompt responses and high hallucination rates. In response, we present our ongoing work: OTTER, a framework for pre-training custom language models and fine-tuning language models for embedded applications. We observe that smaller parameter-sized models outperform larger ones when curated with appropriate pre-training and fine-tuning datasets. Their smaller size allows these models to perform inference on constrained embedded platforms. Building on this framework, we train a custom model for breathing detection and observe high accuracy.

Large language models have attracted a significant recent interest, with an overwhelming efforts and emphasis on scaling their parameter size to support general-purpose and emergent capabilities. However, memory and processing requirements for model inference also scales proportionally with the model's parameter size. This makes it challenging for state-of-the-art, larger models to perform inference locally on edge and mobile devices, even though such models could benefit numerous tasks on these systems. Consequently, these devices are often restricted to accessing larger models through network calls. This approach introduces challenges related to increased inference latency due to network delays and provider capacity. It also raises concerns about sharing private information with third-party vendors. To address these issues, we are developing a system called OTTER. This system tackles the particular problem by enabling the training of smaller yet highly capable foundational language models. As a result of the reduced parameter size, these models can run locally even on constrained edge devices, such as mobile phones and wearables, and are able to provide low-latency responses compared with their larger remotely hosted counterparts. We present our ongoing work describing the framework as applied to training a smaller foundational model for embedded sensing application for tracking a person's breathing with high accuracy comparable to models orders of magnitude larger in size. Our results demonstrate that carefully pre-trained and fine-tuned smaller sized models outperform much larger counterparts for some tasks, while inferring locally on the constrained edge and mobile devices.

Mixed-reality headsets offer new ways to perceive our environment. They employ visible spectrum cameras to capture and display the environment on screens in front of the user's eyes. However, these cameras lead to limitations. Firstly, they capture only a partial view of the environment. They are positioned to capture whatever is in front of the user, thus creating blind spots during complete immersion and failing to detect events outside the restricted field of view. Secondly, they capture only visible light fields, ignoring other fields like acoustics and radio that are also present in the environment. Finally, these power-hungry cameras rapidly deplete the mixed-reality headset's battery. We introduce PixelGen to rethink embedded cameras for mixed-reality headsets. PixelGen proposes to decouple cameras from the mixed-reality headset and balance resolution and fidelity to minimize the power consumption. It employs low-resolution, monochrome image sensors and environmental sensors to capture the surroundings around the headset. This approach reduces the system's communication bandwidth and power consumption. A transformer-based language and image model process this information to overcome resolution trade-offs, thus generating a higher-resolution representation of the environment. We present initial experiments that show PixelGen's viability.

Significant efforts to address the energy challenges of embedded systems have resulted in the design of new low-power transmitter architectures. These transmitters utilize backscatter or tunnel diode-based mechanisms to enable low-power transmissions by delegating energy-intensive tasks to external infrastructure. However, their widespread adoption is hindered by the need for specialized deployment setups, such as the precise placement of carrier-emitting devices. To overcome this, we are developing GateHaul-a low-cost gateway architecture that provides the required carrier signal, coordinates with other gateways, collects sensor data, and backhauls the information. Notably, GateHaul can backhaul sensor data without conventional networks utilizing emerging opportunistic backhaul networks. We demonstrate an early prototype of GateHaul that collects information from backscatter and conventional devices and backhauls the collected information using Apple's FindMy network.

We have seen rapid deployment of augmented reality devices. However, these devices currently suffer from limited battery life. They require frequent recharge, especially when capturing and streaming information wirelessly. In particular, these devices stream information over the radio spectrum using transceivers that are power-hungry, and wireless communication dominates the energy budget of these devices. We present our ongoing work to design a system called Li-FiAR, which proposes integrating Li-Fi with augmented reality devices. Specifically, we implement a transmitter and receiver and integrate them with an augmented reality device. We argue that Li-Fi receivers may consume less power than their radio counterparts while benefiting from the spatial nature of light propagation. This enables novel application scenarios for augmented reality. We demonstrate the transmission of images and audio through Li-Fi, presented on an augmented reality device in this work.

Light bulbs have been recently explored to design Light Fidelity (LiFi) communication to battery-free tags, thus complementing Radiofrequency (RF) backscatter in the uplink. In this paper, we show that LiFi and RF backscatter are complementary and have unexplored interactions. We introduce PassiveLiFi, a battery-free system that uses LiFi to transmit RF backscatter at a meagre power budget. We address several challenges on the system design in the LiFi transmitter, the tag and the RF receiver. We design the first LiFi transmitter that implements a chirp spread spectrum (CSS) using the visible light spectrum. We use a small bank of solar cells for both communication and harvesting, and reconfigure them based on the amount of harvested energy and desired data rate. We further alleviate the low responsiveness of solar cells with a new low-power receiver design in the tag. We design and implement a novel technique for embedding multiple symbols in the RF backscatter based on delayed chirps. Experimental results with an RF carrier of 17dBm show that we can generate RF backscatter with a range of 92.1 meters/$\mu \text{W}$consumed in the tag, which is almost double with respect to prior work.

In this paper, we demonstrate PassiveLiFi, a battery-free system that works at the intersection of Light-Fidelity (LiFi) and Radio-Frequency (RF) backscatter technologies to enable long-range and ultra-low power wireless communications. We show how the PassiveLiFi tag can transmit real measured data through an interactive demo, and we display the received data in a customized graphical user interface. Our demo shows the suitability of PassiveLiFi for implementing real IoT applications, such as monitoring systems for smart agrifood facilities (i.e., greenhouses and vertical farms).

A stark disparity exists in the energy consumption for performing transmissions and the tasks of sensing and processing in wireless embedded systems. We present our early work to design a novel transmitter that enables transmissions at a similar energy consumption as other tasks in wireless embedded systems. In particular, the proposed transmitter does not require a carrier emitting device, which is essential to support backscatter transmitters, but has also limited their widespread deployment. The proposed transmitter exploits the capability of tunnel diode oscillators to function as a low-power, self-oscillating mixer. This property enables us to mix a weak baseband signal with a locally generated carrier signal at a peak power consumption below 100 microwatts. Nevertheless, the tunnel diode oscillator trades off the stability for low energy consumption leading to poor link reliability. In this study, we investigate error correction codes to increase link reliability. Our experiments demonstrate the potential of the transmitter to support short-range transmissions with a low energy consumption and enhanced link reliability.

Wireless embedded systems assist us in collecting data from the physical world, through sensor data analysis, such systems allow us to understand our environment. However, deploying wireless embedded systems and analyzing the collected data remains significantly challenging. This is due to the steep learning curve required to implement custom machine-learning models and other algorithms for data analysis. Furthermore, it is also challenging to program individual embedded devices. The diversity of the available platforms and their capabilities further compounds this problem. In response, we introduce an end-to-end system, called the Otter. It facilitates simple sensor data collection using commodity-embedded platforms. Moreover, it employs a large language model to design a natural language interface for the analysis and extraction of useful information from the sensor data. We present our preliminary work on prototyping this system, applying it to a specific use case of hand gesture detection. Otter represents one of the first systems to leverage the enhanced capabilities of large language models for simplifying wireless embedded system deployments.

Voice plays a crucial role in our daily lives, enabling communication, conveying emotions, and indicating our health. As a result, tracking vocal interactions can provide valuable insights into various aspects of our lives. This poster presents our preliminary work for a novel voice tracker (VoCopilot) that effectively tracks various vocal interactions. For example, the VoCopilot tracker can help document meetings and generate notes, even when participants speak different languages. Additionally, it can serve as a life-logger, monitoring daily conversations and extracting key points to summarize their content. Central to VoCopilot's design is an energy-efficient, co-developed acoustic hardware and firmware combined with a comprehensive integration of VoCopilot tracker with advanced machine learning systems. This harmonious integration ensures precise voice transcription, summarization, and analysis. We present our early thoughts on VoCopilot hardware design and share early results of utilizing Whisper for efficient multilingual transcribing. We acknowledge VoCopilot may raise privacy issues; therefore, we provide early thoughts to address these concerns.

Obtaining hands-on experience with wireless communication can be challenging due to the limited configurability of most commercial off-the-shelf transceivers. We present a low-cost and open-source educational platform designed to help students experiment and investigate wireless concepts through real-world testing. Our platform provides students with the ability to control the physical-layer configuration, design and implement their backscatter system, and conduct link quality experiments to investigate the resulting system performance. This platform offers students a unique opportunity to gain practical experience and deepen their understanding of wireless communication, while also providing a valuable tool for researchers and educators in the field.

An important step in the deployment of wireless embedded systems is the analysis of the sensor data. Traditionally, this requires machine learning models tailored to the application use case. However, this step requires significant expertise from the end user and can be less adaptable to the dynamics of real-world deployments. In recent years, large language models have seen significant developments. These models have been shown to be capable of performing general-purpose tasks. In this work, we explore the hypothesis that large language models can be used to aid in sensor data analysis. Our preliminary findings through real-world experiments show significant promise for two tasks: inferring hand gestures through tracking of light and vibration sensor data. We believe these findings highlight the potential of large language models in sensor data analysis, and thus, it warrants further study.

The rapid growth in wireless embedded systems is threatened by the challenges associated with programming and deploying them. In addition, there is also the complexity inherent in analyzing of the sensor data. Notably, these tasks require high levels of end-user expertise. In this way, an entry barrier is introduced to deploying wireless embedded systems. In this work, we introduce Otter, an end-to-end system designed to simplify these tasks by leveraging the emergent properties of large language models. We demonstrate that Otter allows commodity embedded platforms to capture sensor data, such as light and vibration sensors, which can then be used to identify hand gestures in a near real-time manner. This is all while being prompted using natural language prompts by the end-user. Otter is the first system of its kind and has the potential to facilitate wireless embedded systems proliferation significantly.

The past decade has seen a great interest in developing radio frequency sensing technology and its applications. At a basic level, these systems operate by tracking changes in the wireless signal reflected from a physical object. These reflections contain a wealth of information about the object, such as its motion and material. However, existing radio frequency sensing solutions are constrained by the complexity of the deployment and their high power consumption. This is because these systems extract weak reflections in presence of a strong incident signal. This paper introduces a new radio frequency sensing modality that allows tracking of the incident signal and not reflections from the object. This allows us to simplify the receiver and algorithm design significantly. We design the system using tunnel diode oscillators to generate a high-frequency carrier signal at only tens of microwatts of power consumption. The critical contribution that we make is to show that the frequency of the tunnel diode oscillator is sensitive to the physical environment. As an example, we demonstrate that performing simple hand gestures near the tunnel diode oscillator causes notable changes to its frequency. Thus, the receiver tracks the frequency of the carrier signal generated by the tunnel diode oscillator, and not reflections from physical objects. It enables inferring of sensing information using a commodity receiver costing only a few USD. Our system enables radio frequency sensing at low cost, complexity and power.

The radio transmitter is the most power-consuming component of a wireless embedded system. We present Judo, a radio transmitter that enables power balance between the wireless transmission, sensing, and processing tasks of a wireless embedded system. Judo transmitters leverage the fact that modern radio transceivers offer high receive sensitivity at low power. Therefore, even if the radio transmitter emits a weak signal, the link budget and transmission range will often remain high. With this key insight, we revisit the radio transmitter architecture by dramatically reducing the radiated power and hence the overall power draws. Specifically, Judo transmitter uses a tunnel diode oscillator to integrate the stages of a radio transmitter into a single energy-efficient step. In this step, baseband signals are generated and mixed using peak power below 100 μW. However, we sacrifice stability of tunnel diode oscillator for low-power consumption. We use the injection-locking phenomenon to stabilise the tunnel diode oscillator with an external carrier signal. Based on this novel architecture, we implement a transmitter that supports frequency-shift keying as a modulation scheme. Judo transmits over distances greater than 100 m at a bit rate of 100 kbps. It does so with an emitter device providing the carrier signal, and located at a distance of more than 100 m from Judo transmitter. In terms of critical link metrics, it outperforms the radio transmitters commonly used in wireless embedded systems.

Backscatter enables wireless transmissions at dramatically lower power compared to mainstream Internet of Things (IoT) transmitters. This significantly improves battery life or even eliminates the need of batteries for backscatter-based 'tags' operating on energy harvested from the environment. However, trading off complexity for low power consumption exposing backscatter tags to a multitude of security risks. A significant challenge is imposter tags that mimic legitimate tag behaviour, compromising the system data integrity. In this work, we argue that tag simplicity and operation on harvested energy can help identify imposter transmissions, thus safeguarding data integrity. Our experimental study reveals that commonly used low-power tag oscillators demonstrate unique fingerprint patterns when exposed to the dynamics of harvested energy. Based on this observation, we design and propose HarvestPrint, which leverages these fingerprints to differentiate authentic backscatter transmissions from imposter transmissions. Experiments with a tag powered through a small solar cell show the potential of HarvestPrint.

Light bulbs have been recently explored to design Light Fidelity (LiFi) communication to battery-free tags, thus complementing Radiofrequency (RF) backscatter in the uplink. In this paper, we show that LiFi and RF backscatter are complementary and have unexplored interactions. We introduce PassiveLiFi, a battery-free system that uses LiFi to transmit RF backscatter at a meagre power budget. We address several challenges on the system design in the LiFi transmitter, the tag and the RF receiver. We design the first LiFi transmitter that implements a chirp spread spectrum (CSS) using the visible light spectrum. We use a small bank of solar cells for communication and harvesting and reconfigure them based on the amount of harvested energy and desired data rate. We further alleviate the low responsiveness of solar cells with a new low-power receiver design in the tag. Experimental results with an RF carrier of 17 dBm show that we can generate RF backscatter with a range of 80.3 meters/μW consumed in the tag, which is almost double with respect to prior work.

Backscatter enables transmissions at orders of magnitude lower energy consumption when compared to conventional radio transmitters. Backscatter tags achieve this by the reflection or absorption of carrier signal generated from emitter devices. However, backscatter systems are limited by these emitter devices, as they are significantly energy-expensive when compared to the tags. While backscatter tags can operate without requiring batteries, relying on the minuscule amounts of energy harvested from the ambient environment. However, the emitter devices, are commonly tethered to an external power supply or operate on large batteries.

The growth rate of Internet-of-Things (IoT) devices sold globally is constantly lower than the forecast. This deceleration is caused in part by the need for batteries and the scalability cost for their replacement. Backscatter has attracted significant interest over the past couple of years to enable sustainable sensing devices by eliminating batteries. IoT devices have been designed for transmitting sensed data with backscatter, but the question of efficient reception of data with battery-free devices is still open. As shown in this paper, classical low-power Radio Frequency (RF) envelope detectors are affected by low sensitivity, false detection alarms, and low energy efficiency. We argue that Light Fidelity (LiFi) can provide downlink and harvesting medium as LED lights are becoming pervasively deployed for illumination. We show, for the first time, that the advantages of LiFi and RF backscatter can be combined for battery-free communication. We design a low-power platform that leverages the complementary nature of these two mediums. We demonstrate that our platform removes energy-inefficiency in the downlink reception typical of RF backscatter, and significantly expands the deployment scenarios for battery-free tags when compared to conventional single-technology designs.

Due to extremely low power consumption, backscatter has become the transmission mechanism of choice for battery-free devices that operate on harvested energy. However, a limitation of recent backscatter systems is that the communication range scales with the strength of the ambient carrier signal~(ACS). This means that to achieve a long-range, a backscatter tag needs to reflect a strong ACS, which in practice means that it needs to be close to an ACS emitter. We present TunnelScatter, a mechanism that overcomes this limitation. TunnelScatter uses a tunnel diode-based radio frequency oscillator to enable transmissions when there is no ACS, and the same oscillator as a reflection amplifier to support backscatter transmissions when the ACS is weak. Our results show that even without an ACS, TunnelScatter is able to transmit through several walls covering a distance of 18m while consuming a peak biasing power of \SI57 \micro\watt. Based on TunnelScatter, we design battery-free sensor tags, called TunnelTags, that can sense physical phenomena and transmit them using the TunnelScatter mechanism.

Backscatter communication enables wireless transmissions at orders of magnitude lower power consumption when compared to conventional radio transceivers. This introduces novel opportunities for battery-free and ubiquitous sensing. We take advantage of backscatter communication to enable networked utensils. We imagine a scenario where such utensils can provide essential information about the state of the food or the beverage; for instance, the temperature or the quality of food contained in the utensils. We propose flex sensors, to achieve this capability, by augmenting utensils with flexible and inexpensive battery-free sensors that can communicate wirelessly. We demonstrate our efforts by designing a smart cup that tracks the temperature of the beverage.

Light as a medium for sensing and communication enables new scenarios, such as controlling devices with gestures, or communication for Internet of Things~(IoT) devices. However, a limitation of existing systems is that they often sense only a narrow part of the light spectrum. We argue that the ability to sense a broad light spectrum significantly enhances ability of such systems expanding possible application scenarios. We demonstrate our work in progress to develop the concept of polymorphic light sensing~(PLS). PLS sensor morphs itself according to applications requirements, to track desired parts of the light spectrum (colours, infrared, and ultraviolet light). We couple the PLS sensor with ultra-low power backscatter mechanism, and demonstrate this enables us to sense and communicate the broad spectrum, while operating battery-free.

We introduce BackVLC, a system to connect battery-free IoT tags using LED bulbs. We make use of bulbs beyond illumination. We send data to the tags with visible light communication (VLC), and retrofit the bulbs with simple circuitry to enable the uplink channel current VLC systems lack, using Radio Frequency (RF) backscatter communication from the tags. Tags process and send data, harvesting energy from light and radio. We present our system design and implementation, evaluate it in preliminary simulation studies and experiments, and discuss the research challenges to develop a complete network architecture. BackVLC is the first work that combines VLC with RF backscatter.

We present the architecture for an 802.15.4 receiver that enables battery-free operation. To reach micro-power consumption, the architecture diverges from that of commodity receivers in the following ways: First, similar to backscatter transmitters, it offloads the power-hungry local oscillator to an external device. Second, we avoid the energy cost of demodulating a phase-modulated signal by treating 802.15.4 as a frequency-modulated one, allowing us to receive with a simple passive detector and an energy-efficient thresholding circuit. We demonstrate an off-the-shelf prototype of our receiver receives 802.15.4 from a distance of 470 cm with the carrier generator 30 cm away. This range is sufficient to integrate with deployed wireless sensor networks (WSNs). We demonstrate this integration by pairing our receiver with a 802.15.4 backscatter transmitter and integrating it with unmodified commodity sensor nodes running the TSCH protocol.

We present the architecture of an 802.15.4 receiver that, for the first time, operates at a few hundred microwatts, enabling new battery-free applications. To reach the required micro-power consumption, the architecture diverges from that of commodity receivers in two important ways. First, it offloads the power-hungry local oscillator to an external device, much like backscatter transmitters do. Second, we avoid the energy cost of demodulating a phase-modulated signal by treating 802.15.4 as a frequency-modulated one, which allows us to receive with a simple passive detector and an energy-efficient thresholding circuit. We describe a prototype that can receive 802.15.4 frames with a power consumption of 361 μW. Our receiver prototype achieves sufficient communication range to integrate with deployed wireless sensor networks (WSNs).We illustrate this integration by pairing the prototype with an 802.15.4 backscatter transmitter and integrating it with unmodified 802.15.4 sensor nodes running the TSCH and Glossy protocols.

Security mechanisms for battery-free devices have to operate under severe energy constraints relying on harvested energy. This is challenging, as the energy harvested from the ambient environment is usually scarce, intermittent and unpredictable. One of the challenges for developing security mechanisms for such settings is the lack of hardware platforms that recreate energy harvesting conditions experienced on a battery-free sensor node. In this demonstration, we present an energy harvesting security (EHS) platform that enables the development of security algorithms for battery-free sensors. Our results demonstrate that our platform is able to harvest sufficient energy from indoor lighting to support several widely used cryptography algorithms.

Radio Tomographic Imaging (RTI) enables novel radio frequency (RF) sensing applications such as intrusion detection systems by observing variations in radio links caused by human actions. RTI applications are, however, severely limited by the requirement to retrofit existing infrastructure with energy-expensive sensors. In this demonstration, we present our ongoing efforts to develop the first battery-free RTI system that operates on minuscule amounts of energy harvested from the ambient environment. Our system eliminates the energy-expensive components employed on state-of-the-art RTI systems achieving two orders of magnitude lower power consumption. Battery-free operation enables a sustainable deployment, as RTI sensors could be deployed for long periods of time with little maintenance effort. Our demonstration showcases an intrusion detection scenario enabled by our system.

We present our efforts to design the first Visible Light Sensing (VLS) system that consumes only tens of μ Ws of power to sense and communicate. Our system requires no modification to the existing light infrastructure and uses unmodulated ambient light as sensing medium. We achieve this by designing a sensing mechanism that uses solar cells to achieve sub-μWs of power consumption. Further, we devise an ultra-low power backscatter based transmission mechanism we call Scatterlight that transmits digital readings without incurring the processing and computation overhead of existing sensors. Based on these principles we build a preliminary prototype. Our initial results demonstrate its ability to sense and communicate three hand gestures at 20 μWs of power.

The sensing capabilities of most sensor networks are fixed at the time of deployment. Adding new sensing capabilities to such networks is a costly and cumbersome process. We present Passive Sensor Tags, battery-free sensing devices that could be used to extend the sensing capabilities of an existing network. Sensor tags feature our new 802.15.4 receiver design which is suitable for micro-power operation, making battery-free tags possible. Because our tags can both transmit and receive 802.15.4 frames there is no need for any modification to the deployed hardware. We present preliminary measurements of transmission and reception range.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LOREA an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LOREA costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LOREA's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1 m distance from a 28 dBm carrier source while consuming 70 μW at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75 m, a significant improvement over existing RFID readers.

We present LOREA an architecture consisting of a backscatter tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing backscatter systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions. Second, by mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, by decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. LOREA's communication range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4km when the tag is located 1 m from the carrier source while consuming 70 μWs at the backscatter tag. We present various ultra-low power and long-range features of the LoRea architecture.

Visible Light Communication (VLC) is an emerging communication channel serving as a complement to traditional wireless communication. Visible light has many advantages over other communication channels like its inability to penetrate opaque objects. Securely sharing secret keys is a known problem in computer security. Sharing security keys using Radio Frequency (RF) communication is prone to sniffing attacks. In this paper, we introduce a system called BouKey which uses visible light to share keys. This makes BouKey immune to through the wall sniffing attacks. BouKey divides the key and shares it using multiple transmission bulbs allowing the key to be received in a specific area only. Our initial results show that the characteristics and height of the receiver, as well as the transmission angle play a key role in determining the shape and size of this area.

Most existing indoor localization systems are battery-powered and use the changes in Radio Frequency (RF) signals to localize objects. In this paper, we present LocaLight: a battery-free indoor localization system that localizes objects using visible light by tracking the shadow they cast. By sensing a drop in the intensity of ambient light caused by the presence of a shadow, LocaLight localizes the object. Since the position of the shadow can be predicted, it is possible to localize the object in a sensitive area by carefully positioning the light sensors and the overhead lights. Our initial results suggest that LocaLight achieves an accuracy comparable to many of the state-of-the art solutions that use RF.

The sensing capabilities of an Internet-of-Things (IoT) network are usually fixed at deployment. Adding new sensing modalities is a cumbersome process because it requires altering the deployed hardware. We introduce passive sensor tags that allow to easily and seamlessly add new sensors to existing IoT deployments without requiring hardware modifications or additional energy sources. Passive sensor tags employ backscatter communication to generate transmissions that can be decoded by the radio transceivers present in today's IoT devices. Furthermore, unlike recent works, our approach does not require dedicated infrastructure to generate the unmodulated carrier used for backscatter communication.

Radio tomographic imaging (RTI) enables device free localisation of people and objects in many challenging environments and situations. Its basic principle is to detect the changes in the statistics of radio signals due to the radio link obstruction by people or objects. However, the localisation accuracy of RTI suffers from complicated multipath propagation behaviours in radio links. We propose to use inexpensive and energy efficient electronically switched directional (ESD) antennas to improve the quality of radio link behaviour observations, and therefore, the localisation accuracy of RTI. We implement a directional RTI (dRTI) system to understand how directional antennas can be used to improve RTI localisation accuracy. We also study the impact of the choice of antenna directions on the localisation accuracy of dRTI and propose methods to effectively choose informative antenna directions to improve localisation accuracy while reducing overhead. Furthermore, we analyse radio link obstruction performance in both theory and simulation, as well as false positives and false negatives of the obstruction measurements to show the superiority of the directional communication for RTI. We evaluate the performance of dRTI in diverse indoor environments and show that dRTI significantly outperforms the existing RTI localisation methods based on omni-directional antennas.

We present DPT: a wireless sensor network protocol for bulk traffic that uniquely leverages electronically switchable directional (ESD) antennas. Bulk traffic is found in several scenarios and supporting protocols based on standard antenna technology abound. ESD antennas may improve performance in these scenarios; for example, by reducing channel contention as the antenna can steer the radiated energy only towards the intended receivers, and by extending the communication range at no additional energy cost. The corresponding protocol support, however, is largely missing. DPT addresses precisely this issue. First, while the network is quiescent, we collect link metrics across all possible antenna configurations. We use this information to formulate a constraint satisfaction problem (CSP) that allows us to find two multi-hop disjoint paths connecting source and sink, along with the corresponding antenna configurations. Domain-specific heuristics we conceive ameliorate the processing demands in solving the CSP, improving scalability. Second, the routing configuration we obtain is injected back into the network. During the actual bulk transfer, the source funnels data through the two paths by quickly alternating between them. Packet forwarding occurs deterministically at every hop. This allows the source to implicitly "clock" the entire pipeline, sparing the need of proactively synchronizing the transmissions across the two paths. Our results, obtained in a real testbed using 802.15.4-compliant radios and custom ESD antennas we built, indicate that DPT approaches the maximum throughput supported by the link layer, peaking at 214 kbit/s in the settings we test.

Wireless sensor networks are often deployed indoors where artificial lighting is present. Indoor lighting is increasingly being composed of Light Emitting Diodes (LEDs) that offer the ability to precisely control the intensity and the frequency of the light carrier. This can be used to coordinate wireless sensor networks (WSN). The periodic variations in the light intensity can synchronise the clocks on the sensor nodes, while the ability to modulate the light carrier enables the transmission of control information like channel assignment or transmission schedules.We present Guidelight, a simple mechanism that uses controlled fluctuations in the light intensity to coordinate sensor nodes. Guidelight can wake-up or time synchronise sensor nodes or even send small bits of control information to them. All of these have separate dedicated solutions in WSN. Guidelight aims to provide a single solution to all these problems. Our initial experiments demonstrate the ability of Guidelight to trigger sensor nodes. We demonstrate Guidelight is able to trigger sensor nodes selectively at a mean error of 21 μ s.

Energy is one of the most important resources in wireless sensor networks (WSN). Energy directly translates to lifetime which is an important ingredient of performance control in WSNs. We use an idealized mathematical model to study the impact of routing on energy consumption. We find explicit bounds on the minimal and maximal energy routings will consume, and use them to bound the lifetime of the network. We demonstrate the practical relevance of our theoretical results by experimenting with two different MAC layers, GinLITE, a MAC protocol explicitly designed for performance control, and ContikiMAC.

Many sensor network applications generate large amounts of sensed data. These often need to be delivered reliably to the sink node for further processing. In such applications, high communication throughput allows for more data to be sensed. Intra-path interference is a problem in reliable forwarding of data and affects the end-to-end throughput. We show that using antennas that allow directional transmissions and receptions significantly reduces intra-path interference and enables high throughput forwarding of packet bursts over multiple hops using only one wireless channel.