Indoor positioning systems based on Bluetooth 5.1 Direction Finding technology have recently attracted significant attention due to their capability to estimate the Angle of Arrival (AoA) of radio signals using commercial off-the-shelf devices. Despite this progress, the availability of large-scale, well-annotated experimental datasets collected under realistic conditions remains limited. This paper presents a comprehensive experimental dataset for indoor localization based on BLE 5.1 devices, providing synchronized azimuth and elevation AoA measurements together with Received Signal Strength (RSS) values and precise ground-truth annotations. Data were acquired in a 110 m 2 indoor environment with an adjacent corridor, deploying nine anchors in two configurations (wall-mounted and ceiling-mounted) and two wearable BLE tags. The campaign covers three scenarios: calibration (187 static reference points), static measurements with body orientation variations, and multiple mobility use-cases reproducing realistic walking patterns and posture changes. Overall, more than 4.5 million samples were collected. A preliminary analysis highlights the impact of anchor geometry, field of view, and body shadowing on angular accuracy. The dataset is publicly available and provides a comprehensive benchmark for evaluating angle-based localization, fingerprinting, filtering, and machine-learning approaches in realistic indoor environments.

Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.

In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.

Several radio-frequency technologies have been investigated to develop accurate indoor localization systems, each offering distinct techniques for estimating a target’s position in indoor environments. Among them, the Ultra-WideBand (UWB) technology is a promising approach because it can estimate the distance and angle between a tag and an anchor. In this work, we evaluate the performance of a commercial UWB kit with a systematic data collection campaign. We gather data in a realistic setting, comparing estimated and actual Angles of Arrival (AoA) and distances. Results highlight that, while the system performs reliably in most scenarios, a few instances reveal noticeable deviations from the Ground Truth (GT) data.

Indoor localization is crucial for developing intelligent environments capable of understanding user contexts and adapting to environmental changes. Bluetooth 5.1 Direction Finding is a recent specification that leverages the angle of departure (AoD) and angle of arrival (AoA) of radio signals to locate objects or people indoors. This paper presents a set of algorithms that estimate user positions using AoA values and the concept of the Confidence Region (CR), which defines the expected position uncertainty and helps to remove outlier measurements, thereby improving performance compared to traditional triangulation algorithms. We validate the algorithms with a publicly available dataset, and analyze the impact of body orientation relative to receiving units. The experimental results highlight the limitations and potential of the proposed solutions. From our experiments, we observe that the Conditional All-in algorithm presented in this work, achieves the best performance across all configuration settings in both line-of-sight and non-line-of-sight conditions.

The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.

Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.

The widespread use of Location Based Services (LBS) drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs), require managing the transition from one ILS to another in order to continue localizing the user’s device even when moving indoors or outdoor-to-indoor environments. In this paper, we focus on the handoff procedure, whose goal is enabling a device to trigger the transition between ILSs when specific conditions are verified. We describe the activation of handoff procedures by considering three types of ILS design and deployment, each with increasing complexity. Moreover, this work defines three handoff algorithms based on the proximity detection, and we test them in a realistic environment characterized by two contiguous ILSs.

User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.

The Bluetooth 5.1. Direction Finding (DF) specification opens to the possibility of estimating the angle between an emitting and a receiving device. Such angle is generally measured estimating the Angle of Arrival (AoA) or the Angle of Departure (AoD). In particular, knowledge about AoA between a set of anchor nodes and a moving target could be used to localize the target, with greater accuracy with respect to traditional approaches based on the Received Signal Strength of the received messages. In this work, we rigorously evaluate the performance of a commercial kit implementing the DF specification, with the purpose of understanding how the AoA measure varies with respect to the angles' ground truth. We describe two real-world experimental scenarios and we compute the errors between the estimated and actual angles. We also discuss three key aspects for the purpose of adopting BT 5.1 in indoor localization applications.

Indoor localisation systems have been studied in the literature for more than ten years and nowadays are starting to approach the market. While technology is not mature yet, we can argue that the single biggest obstacle to wide adoption is the lack of standard ways to integrate different systems together. The missing pieces are a common taxonomy, definition of services, protocols. This work is an attempt to define what is next for indoor localisation systems in order to promote market adoption. It is a first high-level attempt at defining a taxonomy of indoor positioning systems, at outlining the main phases of a protocol for the utilisation of different cooperating indoor localisation systems, and at drawing a vision of services and applications in the close future.

We present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. SIDEMAN takes advantage of mobile social networking characteristics, such as user membership to a restricted number of communities and interest for similar services among users in the same community. We evaluated the performance of SIDEMAN via simulations in a scenario based on traces collected at the IEEE conference Infocom in 2006. Our algorithm has been compared to the social version of two popular data dissemination techniques, namely, flooding and gossiping. We have measured how proactive an algorithm is in distributing services of interest (Recall), how many services are already with a user when they are needed (Gain), the energy cost for service discovery, and the time needed to reply a service query. We show that SIDEMAN obtains perfect Recall and a Gain that is always comparable to that of the other algorithms. Furthermore, most services are retrieved in reasonable time and at a lower energy cost than that of the flooding and gossiping-based solutions.

Early detection and adaptive support to changing individual needs related to ageing is an important challenge in today's society. In this paper we present a system called GiraffPlus that aims at addressing such a challenge and is developed in an on-going European project. The system consists of a network of home sensors that can be automatically configured to collect data for a range of monitoring services; a semi-autonomous telepresence robot; a sophisticated context recognition system that can give high-level and long term interpretations of the collected data and respond to certain events; and personalized services delivered through adaptive user interfaces for primary users. The system performs a range of services including data collection and analysis of long term trends in behaviors and physiological parameters (e.g. relating to sleep or daily activity); warnings, alarms and reminders; and social interaction through the telepresence robot. The latter is based on the Giraff telepresence robot, which is already in place in a number of homes. A distinctive aspect of the project is that the GiraffPlus system will be installed and evaluated in at least 15 homes of elderly people. This paper provides a general overview of the GiraffPlus system and its evaluation.

ABSTRACT The localization methods based on received signal strength indicator (RSSI) link the RSSI values to the position of the mobile to be located. In the RSSI localization techniques based on propagation models, the accuracy depends on the tuning of the propagation models parameters. In indoor wireless networks, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we present an automatic virtual calibration method of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose a novel RSSI‐based localization algorithm that selects the RSSI values according to their strength, and uses a calibrated propagation model to transform these values into distances, in order to estimate the position of the mobile. Copyright © 2011 John Wiley & Sons, Ltd.

RSS-based indoor localization systems are widely accepted in the literature as one of the less invasive localization technique. In fact, this range-based approach does not require any special hardware and is available in most standard wireless devices. Furthermore, judicious use of RSS has not a significant impact on local power consumption, sensor size, and cost. In front of these interesting characteristics, the performance of the RSS approach is worst with respect to some more invasive ad hoc hardware range-based solutions (such as Angle of Arrival, Time of Arrival etc…). In this paper we propose a localization method that leveraging the context information, such as the knowledge of being in a given room, increases the localization accuracy of RSS-based methods. Performance evaluation is done via real measurements in an office environment composed of three adjacent rooms.

We examine the indoor single-room localisation problem while using multiple fixed transmitters (anchors) and multiple mobile receivers placed on the user (mobiles). Anchors transmit a periodic beacon that the mobiles receive and of which they measure the received power level value (RSSI). Using this information only, which requires no specialised hardware, the mobiles estimate the position and orientation of the user. Many methods have been proposed to tackle this problem. In this paper we describe a purely theoretical procedure that aims to evaluate the maximum attainable performance of any real methods using RSSI for localisation purpose. Our analysis we present is based on a fine grid of RSSI values in a room, which are computed via ray-tracing, and a maximum-likelihood approach to localisation. Here we illustrate the performance gains of using multiple mobiles versus using a single one and and the attainable performance of user orientation estimation.

Current trends in smart homes suggest that several multimedia services will soon converge towards common standards and platforms. However this rapid evolution gives rise to several issues related to the management of a large number of multimedia streams in the home communication infrastructure. An issue of particular relevance is how a context acquisition system can be used to support the management of such a large number of streams with respect to the Quality of Service (QoS), to their adaptation to the available bandwidth or to the capacity of the involved devices, and to their migration and adaptation driven by the users’ needs that are implicitly or explicitly notified to the system. Under this scenario this paper describes the experience of the INTERMEDIA project in the exploitation of context information to support QoS, migration, and adaptation of multimedia streams.