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1. Sensor Localization Using Time of Arrival Measurements in a Multi-Media and Multi-Path Application of In-Situ Wireless Soil Sensing

   https://doi.org/10.3390/inventions6010016  

   Sahota, Herman; Kumar, Ratnesh (March 2021, Inventions)

   null (Ed.)

   The problem of localization of nodes of a wireless sensor network placed in different physical media (anchor nodes above ground and sensor nodes underground) is addressed in this article. We use time of arrival of signals transmitted between neighboring sensor nodes and between satellite nodes and sensor nodes as the ranging measurement. The localization problem is formulated as a parameter estimation of the joint distribution of the time of arrival values. The probability distribution of the time of arrival of a signal is derived based on rigorous statistical analysis and its parameters are expressed in terms of the location coordinates of the sensor nodes. Maximum likelihood estimates of the nodes’ location coordinates as parameters of the joint distribution of the various time of arrival variables in the network are computed. Sensitivity analysis to study the variation in the estimates with respect to error in measured soil complex permittivity and magnetic permeability is presented to validate the model and methodology. 

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2. Large Network UWB Localization: Algorithms and Implementation

   Garg, Nakul; Shahid, Irtaza; Sheshadri, Ramanujan; Sundaresan, Karthikeyan; Roy, Nirupam (April 2025, USENIX Symposium on Networked Systems Design and Implementation (NSDI))

   Localization of networked nodes is an essential problem in emerging applications, including first-responder navigation, automated manufacturing lines, vehicular and drone navigation, asset tracking, Internet of Things, and 5G communication networks. In this paper, we present Locate3D, a novel system for peer-to-peer node localization and orientation estimation in large networks. Unlike traditional range-only methods, Locate3D introduces angle-of-arrival (AoA) data as an added network topology constraint. The system solves three key challenges: it uses angles to reduce the number of measurements required by 4X and jointly uses range and angle data for location estimation. We develop a spanning-tree approach for fast location updates, and to ensure the output graphs are rigid and uniquely realizable, even in occluded or weakly connected areas. Locate3D cuts down latency by up to 75% without compromising accuracy, surpassing standard range-only solutions. It has a 0.86 meter median localization error for building-scale multi-floor networks (32 nodes, 0 anchors) and 12.09 meters for large-scale networks (100,000 nodes, 15 anchors). 

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3. Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

   Mittal, A.; Tiku, S.; Pasricha, S. (January 2018, ACM Great Lakes Symposium on VLSI (GLSVLSI))

   Indoor localization is emerging as an important application domain for enhanced navigation (or tracking) of people and assets in indoor locales such as buildings, malls, and underground mines. Most indoor localization solutions proposed in prior work do not deliver good accuracy without expensive infrastructure (and even then, the results may lack consistency). Ambient wireless received signal strength indication (RSSI) based fingerprinting using smart mobile devices is a low-cost approach to the problem. However, creating an accurate ‘fingerprinting-only’ solution remains a challenge. This paper presents a novel approach to transform Wi-Fi signatures into images, to create a scalable fingerprinting framework based on Convolutional Neural Networks (CNNs). Our proposed CNN based indoor localization framework (CNN-LOC) is validated across several indoor environments and shows improvements over the best known prior works, with an average localization error of < 2 meters. 

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4. Clock and Orientation-Robust Simultaneous Radio Localization and Mapping at Millimeter Wave Bands

   https://doi.org/10.1109/WCNC55385.2023.10119129  

   Gómez-Cuba, Felipe; Feijoo-Rodríguez, Gonzalo; González-Prelcic, Nuria (March 2023, 2023 IEEE Wireless Communications and Networking Conference (WCNC))

   This paper proposes a radio simultaneous location and mapping (radio-SLAM) scheme based on sparse multipath channel estimation. By leveraging sparse channel estimation schemes at millimeter wave bands, namely high resolution estimates of the multi-path angle of arrival (AoA), time difference of arrival (TDoA), and angle of departure (AoD), we develop a radio-SLAM algorithm that operates without any requirements of clock synchronization, receiver orientation knowledge, multiple anchor points, or two-way protocols. Thanks to the AoD information obtained via compressed sensing (CS) of the channel, the proposed scheme can estimate the receiver clock offset and orientation from a single anchor transmission, achieving sub-meter accuracy in a realistic typical channel simulation. 

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5. Revealing Hidden IoT Devices through Passive Detection, Fingerprinting, and Localization

   https://doi.org/10.56553/popets-2025-0011  

   Sun, Wei; Givehchian, Hadi; Bharadia, Dinesh (January 2025, Proceedings on Privacy Enhancing Technologies)

   Internet-of-things (IoT) devices (e.g., micro camera and microphone) are usually small form factor, low-cost, and low-power, which makes them easy to conceal and deploy in the indoor environment to spy on people for human private information such as location and indoor activities. As a result, these IoT devices introduce a great privacy and ethical threat. Therefore, it is important to reveal these concealed IoT devices in the indoor environment for human privacy protection. This paper presents RFScan, a system that can passively detect, fingerprint, and localize diverse concealed IoT devices in the indoor environment by sensing their unintentional electromagnetic emanations. However, sensing these emanations is challenging due to the weak emanation strength and the interference from the ambient wireless communication signals. To this end, we boost the emanation strength through the non-coherent averaging based on the emanation signal's characteristics and design a novel suppression algorithm to mitigate interference from the wireless communication signals. We further profile emanations across frequency and time that act as the emanation source's unique signature and customize a deep neural network architecture to fingerprint the emanation sources. Furthermore, we can localize the emanation source with an angle-of-arrival (AoA) based triangulation approach. Our experimental results demonstrate the efficiency of the IoT devices' detection, fingerprinting, and localization across different indoor environments. 

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