Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

The latest advancements in visible light communication (VLC) technology have greatly facilitated the evolution of wireless sensing based on visible light, commonly referred to as visible light sensing (VLS) systems. Similar to other wireless sensing technologies, these systems can be categorized into two primary types: device-based (or active) and device-free (or passive), depending on whether targets, such as people or objects of interest, are equipped with an optical receiver (e.g., a photodiode or camera). By reusing the existing Light-emitting diode (LED) lighting infrastructure as transmitters in indoor environments, device-free VLS (DF-VLS) systems can help address the growing energy demands of ubiquitous sensing and communication in Sixth-Generation Wireless Networks (6G). This survey meticulously explores the landscape of DF-VLS technology, proposing a redefinition of the classification of VLS systems and delving into the existing literature in this domain. We conduct a comprehensive analysis of various DF-VLS sensing techniques, focusing on their diverse applications in the Internet of things (IoT). By closely examining the integration and utility of DF-VLS in IoT environments, we highlight the potential of this technology. Furthermore, we underscore critical open challenges that demand attention for the effective development of efficient DF-VLS systems. Finally, the paper outlines a future roadmap for DF-VLS systems, shedding light on potential directions this field may embark upon.

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by $17\%$ in real-time and $13\%$ in batch mode, while the floor detection algorithm achieved a $99.1\%$ and $99.7\%$ floor number accuracy in real-time and batch mode, respectively.

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body–tag–anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

In the rapidly evolving Internet of Things (IoT) landscape, accurate indoor positioning is increasingly vital. The proposed algorithm synergizes Ultra-Wideband (UWB) sensor with Inertial measurement unit (IMU) and Artificial Intelligence to obtain precise positioning in Non-Line-of-Sight (NLOS) scenarios. In the proposed UWB module, Large language model (LLM) such as Bidirectional Encoder Representations from Transformers (BERT) algorithm is designed to utilize the Channel Impulse Response (CIR) for effective NLOS identification and UWB ranging trustworthiness evaluation. Concurrently, the IMU module is also designed with BERT to recognize various pedestrian activity states, thereby optimizing positioning. BERT’s self attention mechanism and deep learning bidirectional training efficiently extract essential features from sequential data, capturing both local and global information. The integration of both UWB and IMU through a proposed tightly coupled algorithm significantly boosts positioning performance. Experiment campaigns demonstrate an average NLOS identification accuracy, Line-of-Sight (LOS) and F2 of 98.8%, 99.4% and 0.9926, respectively. These performance surpass the state-of-the-art Least Squares Support Vector Machine (LS-SVM), Convolutional Neural Network (CNN), CNN with Long Short-Term Memory (CNN-LSTM) up to 17.66% in NLOS identification. In terms of pedestrian activity recognition using BERT, the BERT algorithm achieves a precision(recall) of 99.3%(99.4%), notably outperforming CNN and CNN-LSTM by 17.9%(16.2%) and 11.9%(10.9%), respectively. Finally, the UWB-IMU algorithm significantly enhances positioning accuracy by 80.5%, outperforming Kalman, LSTM-EKF, and Particle filter methods by 68.1%, 48.3%, and 45.0%, respectively. The proposed approach presents a robust solution for indoor positioning for IoT applications, particularly in challenging NLOS environments.

Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site - which were impossible to organise during 2021 and 2022 - in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.