Monitoring the presence and movements of individuals or crowds in a given area can provide valuable insight into actual behavior patterns and hidden trends. Therefore, it is crucial in areas such as public safety, transportation, urban planning, disaster and crisis management, and mass events organization, both for the adoption of appropriate policies and measures and for the development of advanced services and applications. In this paper, we propose a non-intrusive privacy-preserving detection of people’s presence and movement patterns by tracking their carried WiFi-enabled personal devices, using the network management messages transmitted by these devices for their association with the available networks. However, due to privacy regulations, various randomization schemes have been implemented in network management messages to prevent easy discrimination between devices based on their addresses, sequence numbers of messages, data fields, and the amount of data contained in the messages. To this end, we proposed a novel de-randomization method that detects individual devices by grouping similar network management messages and corresponding radio channel characteristics using a novel clustering and matching procedure. The proposed method was first calibrated using a labeled publicly available dataset, which was validated by measurements in a controlled rural and a semi-controlled indoor environment, and finally tested in terms of scalability and accuracy in an uncontrolled crowded urban environment. The results show that the proposed de-randomization method is able to correctly detect more than 96% of the devices from the rural and indoor datasets when validated separately for each device. When the devices are grouped, the accuracy of the method decreases but is still above 70% for rural environments and 80% for indoor environments. The final verification of the non-intrusive, low-cost solution for analyzing the presence and movement patterns of people, which also provides information on clustered data that can be used to analyze the movements of individuals, in an urban environment confirmed the accuracy, scalability and robustness of the method. However, it also revealed some drawbacks in terms of exponential computational complexity and determination and fine-tuning of method parameters, which require further optimization and automation.

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.

Direction-of-arrival (DoA) estimation in the Internet of Things (IoT) and embedded devices is typically performed using single-RF chain systems and antenna switching using time-division multiplexing. The accuracy of the DoA estimation is reduced due to the additional phase shifts between the samples on the antenna elements caused by the carrier frequency offset (CFO). We propose to use optimized antenna switching patterns (ASPs) to mitigate the effect of CFO on accuracy in DoA estimation using a multiple signal classification (MUSIC) algorithm. We evaluated two switching methods referred to as EvenCFO-SP and Mirror-SP with simulations and confirmed the validity with measurements. Performance is analyzed and compared with a standard sequential sampling (SS) method. Uniform linear and circular array configurations are considered to evaluate the impact of the noise and the CFO value on estimation accuracy. The results show that the optimized ASPs outperform the SS method, with lower performance gain at small signal-to-noise ratios. The ASP sensitivity to the CFO value is studied, and the frequency bounds within which the optimized ASPs maintain high estimation accuracy are identified. A coarse CFO correction, effective for frequency estimation errors within the kHz range, extends the frequency bounds. Compared to a fine calibration, this reduces computational requirements, making it a viable option for embedded and IoT devices with limited hardware resources.

Accurate distance estimation in two-way multi-carrier phase difference (MCPD) ranging systems is degraded by carrier frequency offset (CFO) and timing discrepancies caused by asynchronized clocks between nodes. In previous work, carrier frequencies were typically utilized in ascending order during the MCPD process, a method referred to as sequential switching (SS) frequency switching pattern (FSP). Under asynchronous conditions, the SS FSP induces a linear phase trend across the frequency spectrum, resulting in systematic biasing. Existing works address this problem by estimating and compensating for the phase shifts caused by the asynchronization, but introduce additional computational complexity, latency, and power consumption. In this paper, we present a new signal model accounting for the relevant hardware imperfections, characterizing the effects of asynchronization on the phase measurements, and highlighting the limitations of the SS FSP. Building on this analysis, we propose rearranging carrier frequencies in the FSP and introduce a set of novel FSPs that eliminate the linear phase trend across the frequency spectrum and reduce the estimation error without requiring estimation or correction procedure. The results of the simulation study show that the proposed FSPs outperform the SS FSP under different oscillator mismatches achieving sub-centimeter systematic error at high signal-to-noise ratios (SNR), with performance gain decreasing with decreasing SNR and performance variability occurring with different numbers of carriers. Real-world experimental validation confirms a strong agreement between simulated and measured channel impulse responses and that the proposed FSPs produce more robust distance estimates to CFO variations than the SS FSP.

This study provides a multimodal dataset for underground mine tunnels to support the analysis and modeling of radio propagation for communication, sensing, and localization applications. Specifically, the dataset focuses on highly irregular mine-tunnel geometries where waveguide-like behavior is strongly influenced by rough surfaces, changes in cross-section, and discontinuities that cause strong multipath propagation and fading. The released dataset comprises three complementary parts designed to enable measurement-driven and simulation-driven research. First, a Light Detection and Ranging (LiDAR)-derived 3D point cloud of the mine tunnel is provided, together with simplified polygon mesh models to support digital-twin reconstruction and deterministic simulation. Second, a Ultra-Wideband (UWB) measurement dataset includes complex Channel Impulse Response (CIR) samples and receiver-reported metrics collected at accurately defined transmitter and receiver locations, enabling studies of large-scale attenuation, temporal dispersion, multipath structure, and ranging-related performance. Third, ray-tracing simulation records are released, including the simulation setup based on the LiDAR-derived meshes, material and propagation parameters, and outputs containing link-level path loss and ray-level information to interpret dominant propagation mechanisms. This dataset aims to facilitate reproducible calibration and validation workflows that couple geometry, CIR measurements, and deterministic predictions, which are difficult to establish using measurements or simulations alone.