Category: Location

Google Find My Device

Google’s “Find My Device” network is a feature designed to help users locate their Android devices and other items using a network of over a billion Android devices. It uses Bluetooth to detect nearby devices and securely send their locations to the Find My Device network.

This network is end-to-end encrypted, meaning that while Google processes location data, it does not have access to the specific locations, which are only visible to the owner of the lost device.

The Find My Device network is only compatible with Bluetooth beacons which are specifically built for this network and have compatible firmware. These tags can help locate everyday items like keys, wallets, or luggage.

Bluetooth beacons from brands like Chipolo and Pebblebee are compatible with Find My Device and beacons from other brands will be available soon.

Improving RSSI Using Relabelling

Researchers from Japan have a new Relabelling Approach to Signal Patterns for Beacon-based Indoor Localization in Nursing Care Facility. Bluetooth beacons were used in a nursing care facility to enhance the tracking and location estimation of caregivers. These beacons were strategically placed throughout the facility, particularly outside patient rooms and in common areas. The caregivers carried smartphones with a mobile application called FonLog installed, which recorded the Received Signal Strength Indicator (RSSI) readings from the beacons and logged location labels.

The beacons were set to a frequency of 10 Hz with a coverage range of up to five meters. The main challenge addressed in this study was the signal loss and limited data, which affected the accuracy of indoor localisation. To improve the data quality, a relabelling approach was applied. This involved observing the signal patterns in different rooms and using these patterns to augment the training data by relabelling RSSI values from one location as samples for another location with low data samples.

This approach aimed to increase the dataset and improve the model’s accuracy in recognising the caregivers’ locations. By doing so, the accuracy of the indoor localisation model improved, achieving an accuracy of 74%, which was a 5% improvement over the original data. The use of Random Forest for location recognition further enhanced the performance, demonstrating the effectiveness of combining relabelling with machine learning techniques for indoor localisation in a healthcare setting.

Enhancing Behavioral Health Monitoring Through Bluetooth Proximity Detection

New research by researchers from Department of Behavioural and Social Sciences Brown University, USA looks into A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and Validity.

The study describes a Bluetooth-based smartphone app designed to detect the physical proximity of peers, particularly to monitor health behaviours like alcohol consumption. The app uses Bluetooth beacons and aims to improve upon traditional Ecological Momentary Assessment (EMA) by reducing reliance on participant self-reporting through the passive detection of social interactions.

The primary objective is to develop and validate a system using Bluetooth beacons to passively detect when two or more individuals are in close proximity. The methodology involves 20 participants aged 18-29 years, using a smartphone app to collect data over three weeks. Participants’ influential peers carry Bluetooth beacons, and the app records when beacons come into proximity.

The technology could have significant applications in monitoring and intervening in health behaviours by providing real-time, accurate data on social interactions that influence these behaviours. This could be particularly useful in developing “just-in-time” adaptive interventions targeted at high-risk behaviours as they occur.

Results from the study are expected to be reported by 2025, with potential implications for enhancing the accuracy and efficacy of behavioural health interventions. The technology and methodology developed could be applicable to a broader range of behaviours and settings where social context plays a critical role in health outcomes.

Survey of Bluetooth Indoor Localisation

Recent research provides a detailed survey on Bluetooth indoor localisation. The paper underscores the importance of indoor localisation and the unique challenges it presents, such as the inability of GPS to function indoors.

There’s an overview of the types of localisation methods, including triangulation, scene analysis and proximity, as well as the metrics used in these systems. The main localisation techniques discussed are RSSI (Received Signal Strength Indicator), CSI (Channel State Information), fingerprinting and other methods like Angle of Arrival (AoA) and Time of Flight (ToF). RSSI is widely used in Bluetooth localisation but suffers from poorer accuracy due to environmental factors. In contrast, CSI is rarely used due to protocol limitations. Fingerprinting is sometimes employed, involving the pre-collection of measured signal strengths to create a database for location matching.

The survey identifies issues affecting Bluetooth indoor localisation systems, such as accuracy, latency, coverage range, cost and security. Accuracy can be problematic in complex indoor environments, which introduce obstacles and multipath effects that negatively impact signal transmission and reception. The range of coverage is crucial, especially in large indoor spaces where fewer reference nodes are preferred. Cost considerations include both equipment and setup costs, and security issues arise due to the need to protect location data within personal networks.

The study summarises various existing approaches to Bluetooth indoor localisation, categorising them based on their robustness to environmental changes. In discussing RSSI versus fingerprinting, the survey notes that RSSI-based approaches are prevalent due to their simplicity and widespread use. Fingerprinting, on the other hand, involves creating a detailed database of data, which can provide more accurate localisation but requires substantial pre-processing and regular re-calibration to remain effective. Fingerprinting is susceptible to dynamic changes in the environment, making it less competitive in typically fluctuating conditions such as changes in room layout or occupancy.

Enhancing Indoor Localisation for Ambient Assisted Living

New research Simplified Indoor Localisation Using Bluetooth Beacons and Received Signal Strength (RSSI) Fingerprinting with Smartwatch, introduces an innovative system for indoor localisation using Bluetooth Low Energy beacons and smartwatches, aimed at simplifying the process for users. This system is designed to detect a user’s location within specific areas like rooms within a house, rather than providing exact coordinates, with a particular focus on applications in ambient assisted living, especially for the elderly.

The study presents the methodology, implementation, and evaluation of the system, highlighting its practicality for real-world applications. The system demonstrated high accuracy, achieving 92.1% in environments with five rooms and 85.9% with three rooms, showcasing its effectiveness. The setup process is streamlined to reduce the number of reference points and employs a straightforward nearest neighbour algorithm, which simplifies the use and maintenance for users who may not have extensive technical skills.

The use of common and low-cost hardware components, such as Raspberry Pi for beacons and commercial smartwatches, helps keep the system affordable and simple to manage. Calibration is quick and efficient, which is ideal for residential settings. Despite its current effectiveness, the research suggests there is room for improvement. Future enhancements might include the adoption of multiple reference points per region to refine accuracy, particularly in transitional spaces between rooms.

This system offers a robust solution for indoor localisation with significant implications for healthcare, particularly aiding elderly individuals to live independently while ensuring their safety and mobility within their homes.

App-Based Indoor Mobility Detection

The new paper titled A Mobile App-based Indoor Mobility Detection Approach using Bluetooth Signal Strength (PDF) by Muztaba Fuad, Anthony Smith and Debzani Deb from Winston-Salem State University, explores the development and application of a novel system for detecting indoor mobility patterns using the Bluetooth signal strength from mobile devices. This research is significant for its potential real-world applications, particularly in optimising indoor layouts for efficiency.

The research underscores the limitations of GPS in indoor settings, necessitating alternative localisation techniques such as Bluetooth for indoor mobility detection. The study is motivated by the potential efficiency improvements in industries like healthcare, where space optimisation can significantly enhance operational efficiency and patient care.

The approach uses a mobile application to collect Received Signal Strength Indicator (RSSI) data to determine paths taken by mobile devices within indoor spaces. The system comprises a vectorised algorithm for path determination, highlighting its low-cost and ease of implementation advantages. The methodology faced challenges related to software system creation, data collection and mobility detection. Despite these, the study demonstrates that Bluetooth RSSI data can effectively determine indoor paths with reasonable accuracy.

Experiments conducted in controlled indoor environments validated the system’s ability to detect mobility patterns accurately. Parameters such as data aggregation methods and normalisation significantly impacted the accuracy of detected paths. The study’s findings indicate that the proposed approach can effectively map indoor mobility without specialised hardware, relying solely on mobile devices and a custom application.

The authors conclude that while the system presents a promising solution for indoor mobility detection using Bluetooth RSSI, further research is necessary to improve accuracy and applicability in real-world scenarios. Future work will explore the impact of varying the number of stationary devices and the distance between them on detection accuracy. Additionally, real-world testing in clinical settings is planned to validate the approach’s effectiveness in operational environments.

Construction Worker Analysis Using Beacons

A new paper, authored by Mohammadali Khazen, Mazdak Nik-Bakht, and Osama Moselhi, introduces an innovative system for indoor construction sites, designed to simultaneously track workers’ locations, body orientations, and productivity states. The system, which integrates Real-Time Locating System (RTLS) data with a 4-Dimensional Building Information Model (BIM), employs three modules: workspace discovery, body orientation detection and productivity state identification.

The workspace discovery module maps workers’ locations onto the BIM, enhancing workspace management. The body orientation module, using Bluetooth Low Energy (BLE) beacons, identifies workers’ field of view, while the productivity state module leverages accelerometer data from body-mounted beacons to classify workers’ activities into direct work, support work, or idle states.

Experimental results demonstrate the system’s efficacy in laboratory settings, with orientation detection showing a mean error of less than 30° over eight minutes and productivity state identification achieving an average error of 14% and a maximum of 20%. These findings underscore the system’s potential to improve on-site management by providing real-time insights into workers’ activities, thereby addressing the limitations of manual observation methods.

The integration of RTLS with BIM and the innovative use of sensor data for orientation and activity classification represents a significant advancement in the field of construction site management, offering a promising tool for enhancing productivity and safety on indoor construction projects.

Improving Bluetooth Fingerprinting Using Machine Learning

A new paper titled “Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs” by Suhardi Azliy Junoh and Jae-Young Pyun, explores the development of a data-augmentation method for enhancing the accuracy of indoor localisation systems that use Bluetooth Low Energy (BLE) fingerprinting.

Bluetooth fingerprinting is a technique used to identify and track devices based on the unique characteristics of the Bluetooth signal, such as hardware addresses and signal strength, at specific locations.

The primary challenge addressed is the labour-intensive and expensive nature of traditional site surveys required for collecting Bluetooth fingerprints. The authors propose a novel approach that employs a Conditional Generative Adversarial Network with Long Short-Term Memory (CGAN-LSTM) to generate high-quality synthetic fingerprint data. This method aims to complement existing fingerprint databases, thereby reducing the need for extensive manual site surveys.

The research found that augmenting the fingerprint database using the CGAN-LSTM model significantly improved localisation accuracy. In experimental evaluations, the proposed data augmentation framework increased the average localization accuracy by 15.74% compared to fingerprinting methods without data augmentation. Moreover, when compared to linear interpolation, inverse distance weighting, and Gaussian process regression, the proposed CGAN-LSTM approach demonstrated an average accuracy improvement ranging from 1.84% to 14.04%, achieving average accuracies of 1.065 and 1.956 meters in two different indoor environments. These results underline the effectiveness of the CGAN-LSTM model in capturing the complex spatial and temporal patterns of BLE signals, making it a promising solution for indoor localisation challenges.

The study contributes significantly to the field by demonstrating how synthetic data can enhance the performance of fingerprint-based localisation systems in a cost-effective and efficient manner. The authors suggest that this approach could alleviate the burdensome demands of manual site surveys, offering a viable solution for improving the accuracy of BLE-based indoor localisation while minimizing resource expenditure.

Apple AirTag and Samsung SmartTag Security

The new paper Securing the Invisible Thread: A Comprehensive Analysis of BLE Tracker Security in Apple AirTags and Samsung SmartTags by Hosam Alamleh, Michael Gogarty, David Ruddell, and Ali Abdullah S. AlQahtani, looks into the security of Bluetooth Low Energy (BLE) trackers, particularly Apple AirTags and Samsung SmartTags. The research identifies a broad range of attack vectors, including physical tampering, firmware exploitation, signal spoofing and cloud-related vulnerabilities. It examines the security measures and cryptographic methods used in these devices, revealing that while they provide considerable utility, they also introduce significant security risks.

Apple AirTags and Samsung SmartTags differ in their approach to security and privacy. Apple prioritises user privacy, leading to authentication challenges and successful AirTag spoofing instances. Samsung’s design aims to prevent beacon spoofing but raises concerns about cloud security and privacy. The study highlights the trade-off between battery life and security in the design of Bluetooth trackers, noting the absence of secure boot processes as a vulnerability.

The paper concludes that future developments in Bluetooth tracking technology will likely focus on enhancing security features. This is crucial as these devices become more integrated into the IoT ecosystem and subject to evolving privacy regulations. The research underscores the importance of addressing the security challenges presented by BLE trackers to balance functionality and security in next-generation systems.

What are Bluetooth Tunnel Beacons?

This is a feature in Google Maps on Android that improves navigation through tunnels, addressing the long-standing challenge of maintaining accurate location tracking when GPS signals falter.

Historically, tunnels have posed a significant challenge for digital navigation tools, primarily due to the inability of GPS signals to penetrate the thick layers of earth and concrete. This often results in a loss of real-time location tracking. However, Google Maps has improved the situation through the introduction of Bluetooth tunnel beacons, a feature that uses the power of Bluetooth technology to offer an unprecedented level of location accuracy in subterranean environments.

Bluetooth tunnel beacons operate by emitting signals that are received by a user’s smartphone, providing precise location data to the device. This feature, using technology already implemented by Google-owned Waze utilises these signals in conjunction with the device’s mobile connectivity. Together, they deliver navigation assistance, mirroring the capabilities of a traditional GPS connection.

The feature appears under Settings > Navigation Settings and under the ‘Driving Options’ section near the bottom. The feature is disabled by default, and is described as: ‘Scan for Bluetooth tunnel beacons to improve location accuracy in tunnels’.

The effectiveness of Bluetooth tunnel beacons, however, depends on the presence of these beacons within tunnels. Waze has already installed these beacons in several major cities around the world, such as New York City, Chicago, Boston, Paris, Rio de Janeiro and Brussels.