Category: Location

New research from Italy explores whether Bluetooth Low Energy signals broadcast by smartphones can be used to infer when people are nearby, without needing cameras, user identification or extra hardware. It proposes treating smartphones as stand-ins for their owners and summarising the visibility of their BLE advertisements over short time windows. Features such as how many devices remain visible for several consecutive scans, how often new devices appear or disappear, and simple patterns in signal strength can give a broad sense of presence and density in a space. These measures are deliberately conservative to protect privacy and avoid misinterpreting brief passers-by or irrelevant devices.

The approach builds on earlier work showing that patterns of BLE visibility are sufficiently stable to distinguish different mobility contexts. The authors argue that, although smartphones and wearables may inflate counts and nearby IoT devices may add noise, careful thresholding and calibration for each environment can still provide useful density estimates. Rather than aiming for exact headcounts, they propose coarse categories such as low, medium or high occupancy, which are more realistic for BLE-based sensing given signal variability and environmental interference.

Possible uses range from managing building occupancy, energy systems and lighting to understanding crowding in public spaces and transport hubs. It could also support accessible navigation by guiding people along less busy routes, or allow digital interfaces to adapt according to whether others are present. Future work will focus on calibrating these methods across different scenarios, combining them with lightweight additional signals such as door sensors or approximate Wi-Fi counts, and embedding strong privacy safeguards. The study stresses that the system should only rely on aggregated stability patterns rather than any attempt to track or identify individuals.

A new paper is the result of collaboration between Spanish and Italian university researchers. It surveys publicly available datasets for indoor localisation, with a focus on machine learning approaches that make use of the sensors embedded in smartphones. It reviews twenty datasets released between 2014 and 2024, noting the growing trend towards multi-sensor data collection, not only Wi-Fi and Bluetooth but also accelerometers, gyroscopes, magnetometers and other signals.

Bluetooth Low Energy (BLE), features prominently throughout the survey. It is identified alongside Wi-Fi as one of the most widely used technologies for radio frequency-based localisation, relying on received signal strength from Bluetooth beacons to triangulate or fingerprint positions. Several datasets in the survey focus on BLE signals, in the form of iBeacons deployed within controlled environments such as university buildings or libraries.

The review also discusses hybrid approaches, where BLE data is combined with other signals such as inertial measurements or geomagnetic readings, to improve robustness and accuracy. Machine learning techniques are highlighted as particularly effective in handling the noisy and fluctuating nature of Bluetooth signals in complex indoor environments. Some studies combine BLE with Wi-Fi fingerprints in unified models, enhancing resilience against signal drift.

In conclusion, the survey underscores Bluetooth’s importance as a practical, widely deployed technology in indoor localisation research, while also pointing out the limitations of current BLE datasets in terms of scale, diversity of environments, and long-term variability.

New research titled Seamless Indoor–Outdoor Localization Through Transition Detection by Jaehyun Yoo presents a system for maintaining accurate and continuous positioning of a person or object as they move between indoor and outdoor environments. Standard GPS technologies struggle in indoor areas due to signal loss, while indoor systems often fail outdoors. Transition zones, such as building entrances, pose particular challenges. This paper addresses those issues by introducing a transition detection algorithm that combines data from WiFi and BLE signals, GPS metrics, and inertial sensors.

The core of the proposed system is a handover strategy that classifies an environment as indoor, transition, or outdoor using a probabilistic model based on signal strength (RSSI), position estimation, and satellite signal quality. The classification process relies on machine learning, specifically a neural network model trained on unlabelled data using an unsupervised approach. The transition zones are especially crucial for switching accurately between positioning engines.

The system comprises three separate engines. The indoor engine uses WiFi and BLE fingerprinting fused with inertial sensor data through a particle filter. The transition engine works exclusively with AI-augmented inertial data to maintain tracking where both WiFi and GNSS are unreliable. The outdoor engine integrates GNSS data with inertial measurements using an extended Kalman Filter, adapting dynamically to account for signal reliability and expected movement.

Experimental tests were conducted using three Samsung smartphones in three office buildings of various sizes and layouts. The new method demonstrated improved positional accuracy and better state classification compared to Google’s Fused Location Provider (FLP) and standard GPS. Results showed the proposed system consistently outperformed the alternatives, particularly in recognising transition zones and reducing localisation errors. However, the method was sensitive to direction errors and GNSS multi-path interference, which could affect the overall accuracy, particularly in densely built environments.

The study concludes that the approach offers significant improvements in seamless localisation using consumer smartphones without requiring labelled data or external GNSS hardware. Future work aims to replace manually tuned parameters with data-driven methods and to further address GNSS multi-path errors by leveraging raw satellite data.

New research (pdf) introduces a method to reduce battery consumption in mobile devices, particularly small, wearable computing devices like smartwatches, rings, or glasses, by using proximity beacons instead of traditional satellite-based location tracking.

Typically, these devices use GNSS (Global Navigation Satellite Systems) such as GPS, which consume significant power due to the effort required to detect and process weak satellite signals. This is especially problematic for small devices with limited battery capacity. The proposed method embeds low-power proximity beacons, such as Bluetooth trackers (e.g., AirTags), into wearable components like watch bands. These beacons emit Bluetooth signals detected by nearby devices, which can then use their own GNSS services to triangulate the location and report it via existing networks like Apple’s Find My or Android’s equivalent.

The system allows dynamic association of the mobile device with new beacons. If a user switches out a watch band, for instance, the device can recognise which beacons are moving with it and prompt the user to reassign the new beacons for location tracking. This flexibility helps maintain tracking without user intervention in most cases.

In scenarios where beacons or networks are unavailable, such as remote locations, the device may still fall back on traditional GNSS. However, in populated areas with ample nearby devices, the reliance on GNSS can be minimised, significantly conserving battery life.

The document also outlines user control features, allowing manual pairing or unpairing of beacons, enabling/disabling location tracking based on time, place, or battery level, and even letting a device query a location network for its own location. This approach not only reduces power consumption but also extends tracking functionality even when the device’s battery is depleted, as the beacons can continue to transmit signals.

The technique provides an efficient and user-friendly alternative to power-intensive GNSS location services, enhancing practicality and longevity for small wearable devices while retaining robust location tracking capabilities.