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Artificial phantoms play an important role in the development of body-area radio-communication devices and networks. This work aims to design, fabricate and validate physical anthropomorphic phantoms (PAP) of torso with their complementary digital twin models (DTM) emulating the anatomical and dielectric properties of the human body. Two PAPs-an obese lower abdomen and an athletic upper thorax-were designed and fabricated, with their DTMs. These PAPs cover both the ends of adult human body type spectrum. The heterogeneous phantoms consist of layers representing: skin, fat, muscle, rib cage, spinal column, and internal organ, fabricated using semi-solid, low water content materials. The phantoms were validated by comparing their dielectric properties with the IFAC database. The PAPs and DTMs were analyzed for the performance and safety of implantable/on-body radio-communication devices by measuring the values of reflection coefficient (S-11) and simulating specific absorption rate (SAR). The results obtained are: 1) strong agreement of tissue layers' dielectric properties with the IFAC database over 0.5-10 GHz frequency range; 2) dielectric, physical, and mechanical stability for at least 16 months after fabrication; 3) (re)usability and implantability of the developed PAPs for communication studies on the performance and safety of implantable/on-body devices; 4) reliability of developed DTMs for SAR analysis of implantable and on-body devices.

Radiometric fingerprinting is a promising passive security measure for low-power IoT devices, that exploits the hardware imperfections of their radio signals. In this paper, we focus on extracting radiometric fingerprints from Bluetooth Low Energy (BLE) devices. We depart from previous work in that we facilitate fingerprinting in embedded network scenarios by extracting features from I/Q samples collected using a widely used BLE System-on-Chip. We introduce a novel approach that leverages instantaneous frequency analysis of signal dynamics to reveal hardware imperfections during symbol transitions in the packet payload. Our objective is to identify the most significant features contributing to device identification. Our experimental evaluation demonstrates that augmenting traditional aggregated FFT-based features with our proposed transition-based features increases identification accuracy from 56% to 74%.

This article introduces a detailed analysis about the effect of shielding on the performance of a resonating antenna-based wearable fat intrabody communication (Fat-IBC) system for biotelemetry applications. The Fat-IBC wearable antenna resonator is designed using a planar circular spiral resonator with a notch excited by a loop antenna. The resonator is optimized on a three-layer human body tissue model (skin, fat, and muscle) by numerical studies. A polydimethylsiloxane (PDMS) coating surrounds the resonator to prevent direct contact with the body. Furthermore, ferrite substrate and copper tapes are used to construct a low profile shielding over the PDMS-coated wearable resonator to minimize undesired energy loss and surface wave propagation over the skin tissue. The Fat-IBC system is constructed with two identical antenna resonators acting as transmitting (Tx) and receiving (Rx) elements, placed at varying distances on the skin layer to demonstrate data transmission. The concept of the proposed Fat-IBC system has been validated through numerical simulations and experimental studies for shielded and unshielded configurations with a three-layer phantom model. The data transmission link was characterized using scattering parameters and the IEEE 802.11n wireless communication standard. The measurement study shows the transmission coefficient for shielded antennas of about -42.5 and -52.41 dB, while -40.91 and -51.13 dB were achieved from unshielded antennas at distances of 10 and 20 cm, respectively. Moreover, using low-cost Raspberry Pi single-board computers and the 40-MHz bandwidth provided by the IEEE 802.11n standard, the Fat-IBC system achieved a maximum link speed of 92.4 Mb/s through phantom with shielded resonator, while 79.6 Mb/s for unshielded configuration. The result obtained from the measurements establishes the feasibility of the proposed Fat-IBC system based on resonating antennas and shows the importance of shielding.

In-body communication is a key enabler for next-generation healthcare applications, allowing seamless networking of implants. Fat tissue, with its lower water content and reduced signal attenuation compared to other body tissues at microwave frequencies, has emerged as a promising medium for radio-based in-body networks. Despite this advantage, signal leakage through the body can compromise privacy, exposing sensitive data and the mere presence of implants to external adversaries. This paper investigates the feasibility of covert communication in fat tissue-based in-body networks by leveraging the previously unexplored signal attenuation properties of human tissue to transmit data undetectable to adversaries, ensuring privacy beyond encryption. We develop a system in which an implanted transmitter communicates discreetly with an implanted receiver, shielded from external passive eavesdroppers. Our theoretical analysis and experimental results demonstrate that the attenuation properties of human tissues enable covert communication at reduced transmit power levels without requiring friendly jamming, unlike over-the-air systems. To further enhance covertness, we explore the use of an external friendly jammer and show its significant benefits. Experimental results show a 500% increase in the maximum channel capacity of covert communication, from 2.86 bps/Hz at -56 dBm transmit power without jamming, to 17 bps/Hz with no bit errors at 0 dBm transmit power with a friendly jammer, using the IEEE 802.15.4 standard for communication in the 2.45 GHz frequency band. These findings highlight that covert communication is achievable in fat tissue-based in-body networks at low data rates without additional infrastructure such as an external jammer. For applications requiring higher data rates, a friendly jammer offers a scalable solution, making this approach practical for a wide range of implant communication scenarios.

We report on our experience deploying a CubeSat to study fault and error distributions against different fault tolerance schemes when using Common Off-The-Shelf (COTS) hardware in Low-Earth Orbit (LEO). Space radiation commonly causes faults in COTS hardware, such as bit flips in memory, which can lead to errors in a program's execution. Fault tolerance techniques can prevent faults from turning into errors. Accurately quantifying the fault and error distributions is vital for choosing an appropriate fault tolerance scheme. We equip the CubeSat with heterogeneous hardware combining a regular System on a Chip (SoC) with programmable logic resources. Based on in-orbit experiments and post-processing of logs, we check the validity of two fault models. We find the single fault model to be valid, encouraging the use of techniques such as triple modular redundancy. We also demonstrate, however, that the single-bit error fault model is not valid, which means that common techniques such as Hamming (7,4) codes should not be used. We observe that most errors are short-lived, allowing simple re-executions to correct them. Contrary to intuition, we also conclude that floating-point encodings are more appropriate to build fault-tolerance schemes in our setting, due to faults being easier to detect than in their integer counterparts. Our insights confirm existing findings in the literature while also providing new ones, while providing a foundation to conceive fault tolerance schemes for COTS hardware deployments in space.

Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants (known as Byzantine clients) may upload arbitrary local updates to the central server in order to degrade the performance of the global model. In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning. These solutions were claimed to be Byzantine-robust, under certain assumptions. Other than that, new attack strategies are emerging, striving to circumvent the defense schemes. However, there is a lack of systematical comparison and empirical study thereof. In this paper, we conduct an experimental study of Byzantine-robust aggregation schemes under different attacks using two popular algorithms in federated learning, FedSGD and FedAvg . We first survey existing Byzantine attack strategies, as well as Byzantine-robust aggregation schemes that aim to defend against Byzantine attacks. We also propose a new scheme, ClippedClustering, to enhance the robustness of a clustering-based scheme by automatically clipping the updates. Then we provide an experimental evaluation of eight aggregation schemes in the scenario of five different Byzantine attacks. Our experimental results show that these aggregation schemes sustain relatively high accuracy in some cases, but they are not effective in all cases. In particular, our proposed ClippedClustering successfully defends against most attacks under independent and identically distributed (IID) local datasets. However, when the local datasets are Non-IID, the performance of all the aggregation schemes significantly decreases. With Non-IID data, some of these aggregation schemes fail even in the complete absence of Byzantine clients. Based on our experimental study, we conclude that the robustness of all the aggregation schemes is limited, highlighting the need for new defense strategies, in particular for Non-IID datasets.

Federated learning (FL) facilitates distributed training across different IoT and edge devices, safeguarding the privacy of their data. The inherent distributed structure of FL introduces vulnerabilities, especially from adversarial devices aiming to skew local updates to their advantage. Despite the plethora of research focusing on Byzantine-resilient FL, the academic community has yet to establish a comprehensive benchmark suite, pivotal for impartial assessment and comparison of different techniques. This paper presents Blades, a scalable, extensible, and easily configurable benchmark suite that supports researchers and developers in efficiently implementing and validating novel strategies against baseline algorithms in Byzantine-resilient FL. Blades contains built-in implementations of representative attack and defense strategies and offers a user-friendly interface that seamlessly integrates new ideas. Using Blades, we re-evaluate representative attacks and defenses on wide-ranging experimental configurations (approximately 1,500 trials in total). Through our extensive experiments, we gained new insights into FL robustness and highlighted previously overlooked limitations due to the absence of thorough evaluations and comparisons of baselines under various attack settings. We maintain the source code and documents at https://github.com/lishenghui/blades.

Applications of sensor nodes in our daily lives are increasing. Sensor nodes can be embedded in textiles to monitor various environmental parameters or to measure biomarkers. Embedded nodes in fabrics can use wires as means of communication. This approach, however, has drawbacks in terms of reliability, e.g. wire breaks, and discomfort that can arise in a large fabric network with multitudes of cables. Power-line communication (PLC) can mitigate these problems by using conductive planes instead of wires. However, the capacitance formed as a result of using two parallel plates and fabric in between attenuates the signal transmission between sensor nodes. Therefore, in order to compensate the attenuation effect of the conductive fabric which can be achieved by optimizing the hardware, we need to know the capacitance model of the conductive fabric. In our case it is necessary to know the capacitance model as a result of placing a cotton knit fabric between conductive meshes. In this work, we demonstrate that one can use the parallel plate capacitance model for estimating the capacitance of a cotton knit fabric placed between two conductive meshes.