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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.

LiFi, light-fidelity, is a wireless technology that uses visible light for data transmission. It has several advantages, such as using a different part of electromagnetic spectrum than radio communication and providing enhanced privacy because light transmission is blocked by walls. Internet of Things applications with low-to-moderate data rates represent a promising arena for LiFi adoption. However, it is difficult to bring LiFi to IoT devices for several reasons, including some of LiFi's strengths. We present TunnelLiFi, a new receiver architecture that acts like a bridge between the light and radio spectrums. A key aspect of TunnelLiFi's design is the use of the unique self-oscillating mixing property of the tunnel diode oscillator, which enables the mixing of a photodiode signal with a locally generated radio frequency carrier signal while drawing under 100 μW of power consumption. In our experiments, Tunnel-LiFi demonstrates the ability to replicate the information contained in light signals onto radio signals at tens of microwatts, even in low-light conditions (300 lux) and at low bitrates (2.93 Kbps). We also show the potential of TunnelLiFi to support high bitrates. TunnelLiFi opens up new possibilities for LiFi technology by enabling communication in areas where light propagation is challenging. It also allows commodity IoT devices to receive LiFi transmissions using their existing transceivers, thus expanding the reach of LiFi.

In modern connected healthcare applications, wearable devices supporting real-time monitoring and diagnosis have become mainstream. However, wearable systems are exposed to massive cyberattacks that threaten not only data security but also human safety and life. One of the fundamental security threats is device impersonation. We, therefore, propose \name; a lightweight real-time detection system that captures spoofing attacks leveraging on body motions. Our system utilizes time series of physical layer features and builds on the fact that it is non-trivial to inject malicious frames that are indistinguishable from legitimate ones. With the help of statistical learning, our system characterizes the signal behavior and flags deviations as anomalies. We experimentally evaluate \name's performance using bodyworn devices in real attack scenarios. For four types of attackers with increasing knowledge of the deployed detection system, the results show that \name detects naive attackers with high accuracy above 99.8\% and maintains good accuracy for stronger attackers at a range from 81.0\% to 98.9\%.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Radiometric fingerprinting is an effective technique for identifying and authenticating wireless devices by leveraging unique imperfections in transmitter electronics. This approach fits well with the low-power and low-complexity philosophy of backscatter systems because their purely passive nature requires no extra resources. Backscatter systems delegate the power-intensive generation of the carrier to an external emitter device and modulate the data by reflecting carrier signals at a low-power tag. In this paper, we systematically analyze the backscatter architecture and decompose the fingerprint, allowing us to accurately distinguish and classify both tags and carrier emitters with a true accept ratio of over 98.4\% and below 1.6\% false accept ratio. Compared to conventional radio transmitters, backscatter fingerprinting presents a greater challenge due to the emitter-tag separation with a simple architecture. To offer a deeper understanding, we assess the importance of features in conjunction with three additional groups of conventional devices. In addition, we seek insights into fingerprint stability due to the voltage variation, which indicates that different from conventional wisdom, the tag fingerprint is susceptible to voltage variations. We also discuss possible system design strategies to improve the tag fingerprinting stability.

Radiometric fingerprinting systems leverage unique physical-layer signal characteristics originating from individual hardware imperfections to identify transmitter devices. The pure passive nature of such mechanisms entirely relieves the overhead of identification and authentication operations from the end devices, which fits well with resource-constrained applications such as wireless sensor networks. However, existing systems are limited by the need for specialized hardware and non-trivial computations to extract fingerprinting features, hindering their pervasive deployment. For the first time, we ask the question whether it is feasible to implement an entire radiometric fingerprinting system on a low-cost and low-power embedded SoC. We introduce ORF, which demonstrates the feasibility of such a system on an embedded SoC that costs less than 6 dollars. Our experiments show that ORF achieves over 92% average accuracy on the task of identifying one out of 32 different transmitter devices.