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Analog backscatter communication facilitates ultra- low-power wireless sensing by communicating using reflected radio frequency (RF) signals rather than generating their own at vastly reduced power consumption in the microwatt range. The reduced power consumption makes analog backscatter tags suitable for battery-free applications in the Internet-of-Things (IoT), with the caveat of limited querying and management capabilities when multiple tags are present in the same envi- ronment. These limitations stem from the inherent constraints of analog designs, which lack sophisticated addressing and multiple access techniques commonly found in digital systems that require power-hungry digital circuitry. In this paper, we present Chord, a novel querying mechanism that scales effectively with the number of analog backscatter tags. Our approach leverages frequency selective querying using multi-carrier excitation signals combined with combinatorial filtering at the tags to enable precise targeting of individual devices. This mechanism allows the reader to query specific tags by transmitting signals at their designated frequency sets, significantly reducing interference and collisions. Compared to O(N) querying complexity in traditional methods that require N separate querying frequencies, our approach achieves O(logN) complexity, significantly enhancing scalability for large deployments of distributed analog backscatter systems.

In this article, the design of a non-invasive wearable antenna-based fat intra-body communication (Fat-IBC) system is presented for biomedical applications. The Fat-IBC system is used for uninterrupted communication between various wearable, implanted, and semi-implanted devices, facilitating the exchange of data and information within body area networks (BAN). Herein, to eliminate the design complexity, a simple planar-loop antenna is considered to establish the Fat-IBC link. For the numerical analysis, a three-layer human body tissue model (skin, fat, and muscle) is considered to optimize the antenna. A polydimethylsiloxane (PDMS) coating layer is deposited around the wearable antenna to eliminate direct contact with the human body. In addition, the antenna has also been shielded by a ferrite substrate and copper tape to reduce the loss of energy in undesired directions and stop the surface wave propagation over the skin tissue. The Fat-IBC system is constructed by using two identical wearable antennas that act as transmitting (Tx) and receiving (Rx) elements. These antennas have been placed on the three-layer human body tissue models at different distances to demonstrate the data transmission. The concept of the proposed wearable antenna-based Fat-IBC system has been established by numerical simulations and validated by experimental studies using phantoms. The proposed data transmission link was characterized using scattering parameters and the IEEE 802.11n wireless communication standard with combinations of on-skin wearable antennas. To achieve high in-body data rate using on-body antennas through the fat layer, a wireless LAN at the 2.4 GHz band was tested using low-cost Raspberry Pi single-board computers. The phantoms are utilized for measurement purposes to emulate the human body. For the proposed Fat-IBC system, a maximum link speed of 93 Mb/s is achieved using the 40 MHz bandwidth provided by the IEEE 802.11n standard at the frequency of 2.4 GHz. The obtained results demonstrate that the proposed Fat-IBC system, utilizing low-cost off-the-shelf hardware and established IEEE 802.11 wireless communication, can achieve high-speed data communication through three-layer phantom tissue.

Novel backscatter communication techniques allow battery-free sensor tags to operate with standard IoT devices, thereby augmenting a network's sensing capabilities. For communicating, sensor tags rely on an unmodulated carrier provided by neighboring IoT devices, with a schedule coordinating this provisioning across the network. Computing schedules to interrogate all sensor tags while minimizing energy, spectrum utilization, and latency-i.e., carrier scheduling-is an NP-hard problem. While recent work introduces learning-based systems for carrier scheduling, we find that their advantage over traditional heuristics progressively decreases for networks with hundreds of IoT nodes. Moreover, we find that their generalization is not consistent: it greatly varies across identically trained models while fixing the dataset, hyperparameters and random seeds used. We present RobustGANTT, a Graph Neural Network scheduler for backscatter networks that learns from optimal schedules of small networks (up to 10 nodes). Our scheduler generalizes, without the need for retraining, to networks of up to hundreds of nodes ( 100x training topology sizes), and exhibits consistent generalization across independent training rounds. We evaluate our system on both simulated topologies of up to 1000 nodes and real-life IoT network topologies of up to 300 IoT devices. RobustGANTT not only exhibits better generalization than existing systems, it also computes schedules achieving up to 2x less energy and spectrum utilization. Additionally, its polynomial runtime complexity allows it to react fast to changing network conditions. Our work facilitates the operation of large-scale IoT networks, and our machine learning findings further advance the capabilities of learning-based network scheduling. We release our code, datasets and pre-trained models.

Analog backscatter communication enables ultra-low-power wireless sensing by conveying information through reflected radio frequency (RF) signals, eliminating the need for active RF generation and reducing power consumption to the microwatt range. This makes analog backscatter tags particularly well-suited for battery-free Internet-of-Things (IoT) applications. With 6G’s Massive IoT vision, the number of connected devices is expected to grow dramatically, with Zero-Energy IoT (ZE-IoT) devices providing sensing and communication capabilities at varying levels of complexity. In this context, analog backscatter tags with their low-complexity and inherent energy efficiency, offer a compelling approach to significantly enhance large-scale data collection. We consider a hierarchical architecture in which ZE-IoT devices act as gateways for clusters of analog backscatter tags, enabling data aggregation and local processing. This paper investigates the key challenges and opportunities associated with integrating analog backscatter networks into 6G infrastructures, including scalability, coexistence, and signal processing constraints. We further present the design and implementation of a multi-tag analog backscatter system and propose practical solutions to support efficient integration of such networks.

Wireless IoT networks are becoming increasingly important as they support a diverse range of applications. However, due to their resource constraints and limited security capabilities, these networks are particularly susceptible to attacks. Among these, jamming attacks pose a significant threat by degrading packet delivery, disrupting communications, and depleting the limited networking and energy resources of IoT systems. Hence, effective jamming detection is essential for safeguarding wireless IoT networks. However, dynamic operating environments and diverse application scenarios make it challenging to design a robust jamming detection system. To address the challenge, this paper introduces AJDet, a lightweight, online, Adaptive Jamming attack Detection system. AJDet leverages an online adaptation approach that enables the detection of both proactive and reactive jamming attacks without relying on a deployment-specific configuration. Our experimental results demonstrate that AJDet achieves over 96.5% detection accuracy without the need for manual intervention after deployment. Moreover, the system exhibits an efficient memory footprint, making it well-suited for resource-constrained devices.

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%.