Category: Location

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.

Indoor Locating Using Beacons in Nursing Care

The new paper Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities by Christina Garcia and Sozo Inoue from the Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Japan, presents a study on enhancing machine learning for indoor localisation in caregiving, specifically in nursing homes, using Bluetooth Low Energy (BLE) technology.

The study addresses the challenge of limited data available for training machine learning models in indoor localisation, which is critical for monitoring staff-to-patient assistance and managing workload in caregiving environments. The authors propose a data augmentation method that repurposes the Received Signal Strength (RSSI) from various beacons by re-labeling them to locations with fewer data samples, thus resolving data imbalances. This method uses standard deviation and Kullback–Leibler divergence to measure signal patterns and find matching beacons for re-labeling. Two variations of re-labeling are implemented: full and partial matching.

The performance of this method is evaluated using a real-world dataset collected over five days in a nursing care facility equipped with 25 Bluetooth beacons.

Overall, the study highlights the effectiveness of the proposed re-labelling method in enhancing indoor localisation accuracy in nursing care facilities, providing a valuable contribution to the field of caregiving and workload management.

Improved RSSI Indoor Localisation Using AI Algorithms

The article titled Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms by Ruvan Abeysekera and Ruvan Abeysekera focuses on enhancing indoor localisation in Internet of Things (IoT) systems using AI machine learning algorithms. The paper addresses the limitations of GPS in indoor environments and explores the use of Bluetooth low-energy (BLE) nodes and Received Signal Strength Indicator (RSSI) values for more accurate localisation.

GPS is ineffective indoors so the paper emphasises the need for alternative methods for indoor localisation, which is crucial for various applications like smart cities, transportation and emergency services.

The study uses machine learning algorithms to process RSSI data collected from Bluetooth nodes in complex indoor environments. Algorithms like K-Nearest Neighbors (KNN), Support Vector Machine (SVM, and Feed Forward Neural Networks (FFNN) are used, achieving accuracies of approximately 85%, 84%, and 76% respectively.

The RSSI data is also processed using techniques like weighted least-squares method and moving average filters. The paper also discusses the importance of hyperparameter tuning in improving the performance of the machine learning models.

The research claims to provide significant advancement in indoor localisation, highlighting the potential of machine learning in overcoming the limitations of traditional GPS-based systems in indoor environments.

Simple Indoor iBeacon Positioning Method

New research Using iBeacon Components to Design and Fabricate Low-energy and Simple Indoor Positioning Method (PDF) focuses on developing an effective indoor positioning system using iBeacon. The authors propose an enhanced triangulation technique using signal strength signatures for improved indoor positioning precision.

This system integrates a ‘blind’ device and multiple base stations using iBeacon components to form virtual digital electronic fences, effectively receiving signals from moving devices or tags in a targeted area. The proposed method divides the positioning area into rectangular or triangular subareas and establishes a loss value database for improved location estimation.

The system shows high accuracy, with an average error of less than 0.5 m in the worst-case scenario, making it suitable for various environments. The paper covers the architecture of the system, development phases and experimental results demonstrating the system’s effectiveness. The research offers significant insights into low-cost, high-precision indoor positioning methods suitable for diverse applications such as healthcare, smart cities, and industrial settings.