Julie A. McCann

Sokratis Kartakis, Anqi Fu, Manuel Mazo et al.

Energy constraint long-range wireless sensor/ actuator based solutions are theoretically the perfect choice to support the next generation of city-scale cyber-physical systems. Traditional systems adopt periodic control which increases network congestion and actuations while burdens the energy consumption. Recent control theory studies overcome these problems by introducing aperiodic strategies, such as event trigger control. In spite of the potential savings, these strategies assume actuator continuous listening while ignoring the sensing energy costs. In this paper, we fill this gap, by enabling sensing and actuator listening duty-cycling and proposing two innovative MAC protocols for three decentralized event trigger control approaches. A laboratory experimental testbed, which emulates a smart water network, was modelled and extended to evaluate the impact of system parameters and the performance of each approach. Experimental results reveal the predominance of the decentralized event-triggered control against the classic periodic control either in terms of communication or actuation by promising significant system lifetime extension.

Zhouxiang Zhao, Jiaxiang Wang, Yahao Ding et al.

Efficient digital twin (DT) synchronization relies on maintaining high-fidelity virtual representations with minimal age of information (AoI). However, the synergistic potential of cooperative sensing and autonomous mobility of the sensing agent remains underexplored in existing DT synchronization frameworks. In this paper, we propose an agentic AI-empowered mobile embodied AI network (MEAN) framework for DT synchronization. In the proposed hybrid architecture, the base station (BS) conducts global orchestration, while the agents autonomously execute a five-stage closed-loop workflow: move-to-sense, cooperative sensing, onboard semantic processing, channel-aware mobility, and uplink transmission. To optimize synchronization performance, we formulate a joint topology dispatching and multidimensional resource allocation problem aimed at minimizing the maximum twin deviation across regions, subject to heterogeneous sensing fidelity and energy budget constraints. To tackle this, we develop a hierarchical two-layer optimization algorithm, where the outer-layer refines multi-agent assignment via a dynamic matching game, and the inner-layer iteratively optimizes the continuous resources. Extensive simulation results verify the convergence of the proposed algorithm and demonstrate its substantial superiority over multiple baseline schemes in reducing synchronization deviation. Furthermore, the results reveal that semantic compression serves as a vital substitute for channel resources in latency reduction under constrained bandwidth, while autonomous velocity adaptation provides an essential degree of freedom for the system to navigate the fundamental energy-time trade-off.

Shuokang Huang, Julie A. McCann

Human pose estimation (HPE) detects the positions of human body joints for various applications. Compared to using cameras, HPE using radio frequency (RF) signals is non-intrusive and more robust to adverse conditions, exploiting the signal variations caused by human interference. However, existing studies focus on single-domain HPE confined by domain-specific confounders, which cannot generalize to new domains and result in diminished HPE performance. Specifically, the signal variations caused by different human body parts are entangled, containing subject-specific confounders. RF signals are also intertwined with environmental noise, involving environment-specific confounders. In this paper, we propose GenHPE, a 3D HPE approach that generates counterfactual RF signals to eliminate domain-specific confounders. GenHPE trains generative models conditioned on human skeleton labels, learning how human body parts and confounders interfere with RF signals. We manipulate skeleton labels (i.e., removing body parts) as counterfactual conditions for generative models to synthesize counterfactual RF signals. The differences between counterfactual signals approximately eliminate domain-specific confounders and regularize an encoder-decoder model to learn domain-independent representations. Such representations help GenHPE generalize to new subjects/environments for cross-domain 3D HPE. We evaluate GenHPE on three public datasets from WiFi, ultra-wideband, and millimeter wave. Experimental results show that GenHPE outperforms state-of-the-art methods and reduces estimation errors by up to 52.2mm for cross-subject HPE and 10.6mm for cross-environment HPE.

Shuokang Huang, Kaihan Li, Di You et al.

WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare. However, most previous works focus on single-user sensing, which has limited practicability in scenarios involving multiple users. Although recent studies have begun to investigate WiFi-based multi-user sensing, there remains a lack of benchmark datasets to facilitate reproducible and comparable research. To bridge this gap, we present WiMANS, to our knowledge, the first dataset for multi-user sensing based on WiFi. WiMANS contains over 9.4 hours of dual-band WiFi Channel State Information (CSI), as well as synchronized videos, monitoring simultaneous activities of multiple users. We exploit WiMANS to benchmark the performance of state-of-the-art WiFi-based human sensing models and video-based models, posing new challenges and opportunities for future work. We believe WiMANS can push the boundaries of current studies and catalyze the research on WiFi-based multi-user sensing.

Ivana Tomic, Po Yu Chen, Michael J. Breza et al.

Wireless Sensor Network (WSN) applications range from domestic Internet of Things systems like temperature monitoring of homes to the monitoring and control of large-scale critical infrastructures. The greatest risk with the use of WSNs in critical infrastructure is their vulnerability to malicious network level attacks. Their radio communication network can be disrupted, causing them to lose or delay data which will compromise system functionality. This paper presents Antilizer, a lightweight, fully-distributed solution to enable WSNs to detect and recover from common network level attack scenarios. In Antilizer each sensor node builds a self-referenced trust model of its neighbourhood using network overhearing. The node uses the trust model to autonomously adapt its communication decisions. In the case of a network attack, a node can make neighbour collaboration routing decisions to avoid affected regions of the network. Mobile agents further bound the damage caused by attacks. These agents enable a simple notification scheme which propagates collaborative decisions from the nodes to the base station. A filtering mechanism at the base station further validates the authenticity of the information shared by mobile agents. We evaluate Antilizer in simulation against several routing attacks. Our results show that Antilizer reduces data loss down to 1% (4% on average), with operational overheads of less than 1% and provides fast network-wide convergence.

Laksh Bhatia, Ivana Tomic, Julie A. McCann

Low-Power Wide-Area Networks (LPWANs) are being successfully used for the monitoring of large-scale systems that are delay-tolerant and which have low-bandwidth requirements. The next step would be instrumenting these for the control of Cyber-Physical Systems (CPSs) distributed over large areas which require more bandwidth, bounded delays and higher reliability or at least more rigorous guarantees therein. This paper presents LPWA-MAC, a novel Low Power Wide-Area network MAC protocol, that ensures bounded end-to-end delays, high channel utility and supports many of the different traffic patterns and data-rates typical of CPS.

Xuebin Ren, Chia-Mu Yu, Weiren Yu et al.

High-dimensional crowdsourced data collected from a large number of users produces rich knowledge for our society. However, it also brings unprecedented privacy threats to participants. Local privacy, a variant of differential privacy, is proposed as a means to eliminate the privacy concern. Unfortunately, achieving local privacy on high-dimensional crowdsourced data raises great challenges on both efficiency and effectiveness. Here, based on EM and Lasso regression, we propose efficient multi-dimensional joint distribution estimation algorithms with local privacy. Then, we develop a Locally privacy-preserving high-dimensional data Publication algorithm, LoPub, by taking advantage of our distribution estimation techniques. In particular, both correlations and joint distribution among multiple attributes can be identified to reduce the dimension of crowdsourced data, thus achieving both efficiency and effectiveness in locally private high-dimensional data publication. Extensive experiments on real-world datasets demonstrated that the efficiency of our multivariate distribution estimation scheme and confirm the effectiveness of our LoPub scheme in generating approximate datasets with local privacy.