Augmented Reality (AR) has been proven beneficial to External Ventricular Drain (EVD) surgery by providing in-situ visual guidance during operations. During this procedure, the key challenge is estimating the spatial relationship between pre-operative images and actual patient anatomy accurately and efficiently. Previous works have revealed conflicts between tracking accuracy, workflow efficiency, and non-invasiveness in tracking pipelines. This research fully utilizes the capabilities of Time of Flight (ToF) depth sensors, including retro-reflective tool tracking and dense surface information, to construct a convenient and accurate EVD guiding pipeline. As previous studies have proven significant depth errors in ToF depth sensors, we first evaluated the feasibility of using ToF sensors in surgical guidance by estimating its accuracy under different conditions and corrected this error in our pipeline. Our results show $ \text{7.580}\pm \text{1.488}\,\text{mm}$7.580±1.488mm depth value errors on human skin under HoloLens 2 depth camera, indicating the significance of depth correction. This error was reduced by over 85% using proposed depth correction method on head phantoms in different materials. The corrected depth information can then be utilized to reconstruct the head surface with sub-millimeter accuracy, validated on a series of 3D-printed models and a sheep head. To demonstrate the effectiveness of the proposed framework, we conducted a case study simulating EVD surgery. Five surgeons were involved in this study, each performing nine k-wire insertions on a head phantom under virtual guidance without tracking for surgical tools. The results revealed $ \text{2.09} \pm \text{1.00}\,\text{mm}$2.09±1.00mm translational and $\text{2.97}\pm \text{1.95}^\circ$2.97±1.95∘ orientational guidance accuracy, demonstrating competitive performance with previous research.

Innovative medical applications based on networked implants foster the development of in-body communication technologies. Among the in-body communication technologies that are being considered, fat intra-body communication (Fat-IBC) is a very recent approach. Its main advantage lies in its higher data rate compared to earlier approaches based on capacitive and galvanic coupling. However, Fat-IBC faces privacy-, security-, as well as safety-related attacks. In this paper, we discuss security and privacy concerns about Fat-IBC, as well as corresponding countermeasures. Furthermore, we present our secure protocol stack for Fat-IBC and suggest directions for future research.

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.

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.

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.

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%.

The Internet of Things (IoT) is connecting a massive scale of everyday objects to the internet. We need to ensure the secure connectivity and authentication of these devices. Physical (PHY)-layer identification methods can distinguish between different devices by leveraging their unique hardware imperfections. But these methods typically require large quantities of training data which makes them impractical for large deployment scenarios. Also, these methods do not address the PHY-layer identification of new devices joining an IoT network. In this paper, we propose a PHY-layer identification method using one-shot learning that can identify new devices using the network solicitation packet of the devices as reference packets. We show that our method can accurately identify new devices without training, achieving a precision and recall over 80% even in the presence of 10 dBm noise. Furthermore, we show that with minimal retraining using only three packets from each device, we can accurately identify all devices in the IoT network with a precision and recall of 93%.

Physical-layer identification using hardware imperfections, known as radiometric fingerprinting, has existed for some time, but little focus has been put on Bluetooth Low Energy (BLE). This work systematically explores features for physical-layer identification of BLE. We evaluate the fingerprinting performance on different feature sets, and we discuss the potential issues with the robustness that may arise in a practical environment. Accuracy results are achieved in excess of 99%, showing potential for the system to work in security settings to safeguard a Bluetooth network.

In this paper we describe and evaluate an approach for predicting conditional RTT probability distributions in an IoT system. From the distributions we derive conditional mean and quantiles, for example, which are essential for performance management and service assurance. The distribution is represented by a histogram, which requires a discretized target space, trained using supervised learning of a random forest classifier.We evaluate the approach using data traces obtained from experimentation in a realistic IoT testbed. The results show high model performance in prediction of quantiles and aggregated distributions, and the trends for conditional mean are captured.For the operator, the approach enables low-overhead and tractable IoT performance assessment, especially compared to traditional approaches using for example active measurements.

In the near future more and more people will have multiple implants to handle their diseases. The implants benefit from being connected using in-body sensor networks. We have previously shown that RF communication through human adipose (fat) tissue is feasible. In this poster, we argue why we believe that backscatter communication within this fat channel is possible. As security is of utmost importance for in-body communication, we also discuss how backscatter-based in-body networks can be secured.

Authentication is challenging in the IoT because most advanced cryptographic algorithms are difficult to afford by constrained devices expected to run many months or years. Radiometric signatures have been used effectively to identify wireless devices based on imperfections in electronic circuits. This technique is also known as Radio frequency (RF) fingerprinting. Previous work proves the feasibility of this technique but mainly considered static channel conditions. In our current work, we systematically and experimentally study the impact of dynamic and complex channel conditions on the radiometric signatures. The results show a threat to identification accuracy for modulation error-based fingerprinting that was considered channel-resilient. Next step, my research aims at improving the robustness of RF fingerprint systems and make this authentication solution ready for widespread implementation.

Radiometric signatures have been shown effective in identifying wireless devices, also known as fingerprinting, which refers to imperfections in their electronics. Previous work mainly considered static channel conditions. In this work, we systematically and experimentally study the impact of dynamic and complex channel conditions on the radiometric signatures. The results show a threat to identification accuracy for modulation error-based fingerprinting that was considered channel-resilient.

Wearable devices, such as implantable medical devices and smart wearables, are becoming increasingly popular with applications that vary from casual activity monitoring to critical medical uses. Unsurprisingly, numerous security vulnerabilities have been found in this class of devices. Yet, research on physical measurement-based authentication and key distribution assumes that body-worn devices are benign and uncompromised. Tiek is a novel authentication and key distribution protocol which addresses this issue. We utilize two sources of randomness to perform device authentication and key distribution simultaneously but through separate means. This creates a two-tier authorization scheme that enables devices to join the network while protecting them from each other. We describe Tiek and analyze its security.