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1. Secure indoor positioning against signal strength attacks via optimized multi-voting

   https://doi.org/10.1145/3326285.3329068  

   Li, Yunzhi; Hu, Yidan; Zhang, Rui; Zhang, Yanchao; Hedgpeth, Terri (June 2019, IEEE/ACM International Symposium on Quality of Service (IWQoS))

   Indoor positioning systems (IPSes) can enable many location-based services in large indoor venues where GPS signals are unavailable or unreliable. Among the most viable types of IPSes, RSS-IPSes rely on ubiquitous smartphones and indoor WiFi infrastructures and explore distinguishable received signal strength (RSS) measurements at different indoor locations as their location fingerprints. RSS-IPSes are unfortunately vulnerable to physical-layer RSS attacks that cannot be thwarted by conventional cryptographic techniques. Existing defenses against RSS attacks are all subject to an inherent tradeoff between indoor positioning accuracy and attack resilience. This paper presents the design and evaluation of MV-IPS, a novel RSS-IPS based on weighted multi-voting, which does not suffer from this tradeoff. In MV-IPS, every WiFi access point (AP) that receives a user’s RSS measurement gives a weighted vote for every reference location, and the reference location that receives the highest accumulative votes from all APs is output as the user’s most likely position. Trace-driven simulation studies based on real RSS measurements demonstrate that MV-IPS can achieve much higher positioning accuracy than prior solutions no matter whether RSS attacks are present. 

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2. Augmented reality-based vision-aid indoor navigation system in GPS denied environment

   https://doi.org/10.1117/12.2519224  

   Rajeev, Srijith; Wan, Qianwen; Yau, Kenny; Panetta, Karen; Agaian, Sos S. (May 2019, Mobile Multimedia/Image Processing, Security, and Applications)

   Agaian, Sos S.; DelMarco, Stephen P.; Asari, Vijayan K. (Ed.)

   High accuracy localization and user positioning tracking is critical in improving the quality of augmented reality environments. The biggest challenge facing developers is localizing the user based on visible surroundings. Current solutions rely on the Global Positioning System (GPS) for tracking and orientation. However, GPS receivers have an accuracy of about 10 to 30 meters, which is not accurate enough for augmented reality, which needs precision measured in millimeters or smaller. This paper describes the development and demonstration of a head-worn augmented reality (AR) based vision-aid indoor navigation system, which localizes the user without relying on a GPS signal. Commercially available augmented reality head-set allows individuals to capture the field of vision using the front-facing camera in a real-time manner. Utilizing captured image features as navigation-related landmarks allow localizing the user in the absence of a GPS signal. The proposed method involves three steps: a detailed front-scene camera data is collected and generated for landmark recognition; detecting and locating an individual’s current position using feature matching, and display arrows to indicate areas that require more data collects if needed. Computer simulations indicate that the proposed augmented reality-based vision-aid indoor navigation system can provide precise simultaneous localization and mapping in a GPS-denied environment. Keywords: Augmented-reality, navigation, GPS, HoloLens, vision, positioning system, localization 

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3. Integrated Design of Low Complexity RSS Based Visible Light Indoor Positioning and Power-Line Communication System for Smart Hospitals

   https://doi.org/10.1109/ICCE50685.2021.9427699  

   Lang, Tian; Pan, Zijin; Ortiz, Nathaniel; Chen, Gang; Kalay, Yehuda; Pai, Ramdas; Wang, Albert (January 2021, IEEE International Conference on Consumer Electronics (ICCE) 2021)

   null (Ed.)

   Future healthcare systems require smart hospitals with system-wide wireless communications and positioning functions, which cannot be facilitated by existing radio-frequency (RF) wireless technologies. In this paper, we present integrated design of a novel low-complexity received signal strength (RSS) based hybrid visible light communication (VLC) and indoor positioning (VLP) system. This VLC/VLP tracking system consist of host optical transceivers embedded in existing light-emitting diode (LED) bulbs and user-end optical tags, which interface with the existing 120AVC power wiring in a building. The new hybrid VLC/PLC tracking system was validated by simulation and experimentation. This LED VLC tracking system will enable smart hospital operations to modernize next-generation intelligent healthcare systems 

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4. GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices

   https://doi.org/10.1145/3758322  

   Gufran, Danish; Pasricha, Sudeep (November 2025, ACM Transactions on Embedded Computing Systems)

   Accurate indoor localization is crucial for enabling spatial context in smart environments and navigation systems. Wi-Fi Received Signal Strength (RSS) fingerprinting is a widely used indoor localization approach due to its compatibility with mobile embedded devices. Deep Learning (DL) models improve accuracy in localization tasks by learning RSS variations across locations, but they assume fingerprint vectors exist in a Euclidean space, failing to incorporate spatial relationships and the non-uniform distribution of real-world RSS noise. This results in poor generalization across heterogeneous mobile devices, where variations in hardware and signal processing distort RSS readings. Graph Neural Networks (GNNs) can improve upon conventional DL models by encoding indoor locations as nodes and modeling their spatial and signal relationships as edges. However, GNNs struggle with non-Euclidean noise distributions and suffer from the GNN blind spot problem, leading to degraded accuracy in environments with dense access points (APs). To address these challenges, we propose GATE, a novel framework that constructs an adaptive graph representation of fingerprint vectors while preserving an indoor state-space topology, modeling the non-Euclidean structure of RSS noise to mitigate environmental noise and address device heterogeneity. GATE introduces (1) a novel Attention Hyperspace Vector (AHV) for enhanced message passing, (2) a novel Multi-Dimensional Hyperspace Vector (MDHV) to mitigate the GNN blind spot, and (3) a new Real-Time Edge Construction (RTEC) approach for dynamic graph adaptation. Extensive real-world evaluations across multiple indoor spaces with varying path lengths, AP densities, and heterogeneous devices demonstrate that GATE achieves 1.6 × to 4.72 × lower mean localization errors and 1.85 × to 4.57 × lower worst-case errors compared with state-of-the-art indoor localization frameworks. 

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5. Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements

   https://doi.org/10.3390/network4020010  

   Kandula, Harsha; Chidurala, Veena; Cao, Yuan; Li, Xinrong (June 2024, Network)

   Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results. 

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