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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.

Bluetooth Beacon Advertising Protocols

We recently came a cross a very useful diagram, from a research paper, that clearly shows the main Bluetooth LE advertising formats for Bluetooth 4.2, used by beacons:


This clearly shows how the formats, iBeacon, AltBeacon and Eddystone, all sit within a Bluetooth LE advertising protocol data unit (PDU). i.e. They are all use standard Bluetooth LE. Notice also that the advertising data is always short which is partly why it doesn’t use much transmit power and battery. Advertising is sent periodically, every 100ms to 10 seconds, depending on the beacon settings. It only takes of the order of 1ms or 2ms to send the advertising which means the beacon can sleep most of the time, another reason for the low power use.

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Crowdsensing Proximity Detection

There’s a new study on the performance of a proximity detection system for visitors in indoor museums using a Crowdsensing-based technique, authored by Michele Girolami, Davide La Rosa, and Paolo Barsocchi. This approach uses Bluetooth beacon data collected from visitors’ smartphones to calibrate two proximity detection algorithms: a range-based and a learning-based algorithm, embedded within a museum visiting application tested in the Monumental Cemetery’s museum in Pisa, Italy.

The experimental results demonstrate a significant improvement in performance when using crowd-sourced data, with accuracy metrics showing up to a 30% improvement compared to state-of-the-art algorithms. The research introduces a novel contribution by employing a Crowdsensing approach to improve the accuracy of proximity detection algorithms in a challenging indoor environment.

The study provides a detailed experimental campaign, including the design of the mobile application named R-app, to assess the performance enhancements achieved through this innovative method. The authors conclude that integrating Crowdsensing techniques with proximity detection algorithms offers a promising solution for enhancing visitor experiences in cultural heritage contexts.

The resultant collected data is also available.

Read about Beacons in Events and Visitor Spaces

Using Beacons With Flutter

Flutter is an open-source UI software development kit created by Google. It is used for developing platform-agnostic applications for Android, iOS, Linux, Mac and Windows.

The easiest way to use beacons with Flutter is to use a ready-made library. Flutter Gems is a curated list of 5600+ useful Dart & Flutter packages that are categorised based on functionality. They have a section for Bluetooth, NFC, Beacon packages.

Sample Bluetooth Beacon Museum Data Available

Research on Bluetooth dataset for proximity detection in indoor environments collected with smartphones by Michele Girolami, Davide La Rosa, and Paolo Barsocchi, outlines the creation and details of a dataset aimed at enhancing proximity detection between people and points of interest (POIs) within indoor environments, particularly museums.

This dataset is created from Bluetooth beacon data collected from various smartphones during 32 museum visits, showing the interaction with Bluetooth tags placed near artworks. It includes data such as Received Signal Strength (RSSI) values, timestamps and artwork identifiers, providing a comprehensive ground truth for the start and end times of artwork visits.

The dataset is particularly designed for researchers and industry professionals looking to explore or improve upon methods for detecting the proximity between individuals and specific POIs using commercially available smartphone technologies. The primary aim is to facilitate rapid prototyping and the evaluation of indoor localisation and proximity detection algorithms under realistic conditions, leveraging accurate ground truth annotations and detailed hardware specifications.

The authors highlight the dataset’s significance in enabling the testing of proximity detection algorithms under real-world conditions, using data collected with commercial smartphones and Bluetooth tags. It allows for the examination of how RSS values vary across different devices and conditions, including during non-proximity events, providing insights into how these values change as a person approaches or leaves an artwork. This dataset is invaluable for researchers and startups aiming to analyse and automatically detect proximity between subjects and POIs in realistic conditions.

In creating the dataset, the team focused on replicating real-world museum visit conditions, ensuring visitors behaved naturally and that data collection reflected a variety of smartphones and visiting paths to accommodate device heterogeneity and environmental conditions. The methodology included varying the smartphones used for data collection and the sequence of artworks visited, to simulate different user experiences and conditions encountered in a museum setting.

Read about Beacons in Events and Visitor Spaces

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.

Bluetooth Ad-hoc Collision Avoidance System

Research, Ad-hoc collision avoidance system for Industrial IoT by Dan Garcia-Carrillo, Xabiel G. Paneda, David Melendi, Roberto Garcia, Victor Corcoba, and David Martínez, presents a novel system for enhancing safety in industrial environments where heavy machinery operates near workers. The system, designed to prevent collisions, uses Bluetooth beacons to detect the presence of workers and alerts machinery drivers through visual and haptic signals.

In their methodology, the authors employed Bluetooth devices to detect workers nearby and used Raspberry Pi to manage the Advanced Driver Assistance Systems (ADAS). This system evaluates surrounding Bluetooth emitters and triggers feedback mechanisms such as LED strips and vibrating seatbelts. The study acknowledges the accuracy limitations of Bluetooth for precise location but emphasises its effectiveness in proximity detection. A real excavator and workers carrying Bluetooth emitters were used to implement and test this proof of concept.

The results showed that the system could successfully detect the presence of workers relative to heavy machinery. Drivers received simple yet effective feedback through visual and haptic alerts, based on the proximity of workers. Notably, the system was found to be affordable and less intrusive than camera-based solutions, with Bluetooth proving sufficient for this application.

The authors concluded that their proposed system significantly enhances safety in industrial settings with heavy machinery. It effectively alerts drivers of nearby workers, thereby reducing the risk of accidents.

Process Control in Manufacturing and Logistics with Bluetooth Beacons

In sectors such as aerospace, automotive, logistics, transit management and process-driven manufacturing, the quest for efficiency and precision is unending. The integration of Bluetooth beacons into monitoring process control provides a significant leap in addressing these challenges. Traditionally, manual processes suffer from a range of issues. Bluetooth beacons offer a compelling solution to these age-old problems.

Firstly, there’s the matter of process visibility and optimisation. In complex environments like aerospace or automotive manufacturing, keeping track of components and processes is critical. Bluetooth beacons enable real-time tracking and provide data-driven insights, allowing for better decision-making and process optimisation. This technology ensures that every aspect of the manufacturing process is visible and under control, leading to enhanced efficiency and productivity.

A common issue in logistics and transit management is the misplacement of items; things can’t be located or are found in the wrong place. Bluetooth beacons counteract this by offering precise location tracking. This ensures that items are always where they need to be, thereby reducing the time and resources spent on locating misplaced items. In transit management, this translates to smoother operations and reduced delays.

Interaction, or the lack thereof, between components or processes, is another challenge that Bluetooth beacons can handle. In scenarios where two or more things are (or are not) interacting as they should, beacons provide real-time interaction data. This information is crucial in environments where the interplay between different components or processes is key to successful operations.

When it comes to counting, the issue often lies in having too many or too few items in a certain place. Bluetooth beacons facilitate accurate inventory management, ensuring that the right quantity of materials or products is always available where needed. This precision is particularly vital in just-in-time manufacturing processes, where inventory accuracy is paramount.

Time management is another critical factor in process control. A task taking too long or not long enough can significantly impact overall productivity. Beacons can track the time spent on specific tasks, providing insights into potential bottlenecks or inefficiencies. This data is invaluable for optimising workflow and ensuring that time is utilized effectively.

Lastly, sequence plays a pivotal role in manufacturing and logistics. When things happen in the wrong order, it can lead to a cascade of issues. Bluetooth beacons, with their ability to track and record sequences of events, ensure that processes follow the correct order, thereby avoiding costly mistakes and delays.

Anchor-based Bluetooth Low Energy (BLE) 5.0 Positioning

A recent new paper, BLE-Based Indoor Localization: Analysis of Some Solutions for Performance Improvement, focuses on improving the performance of indoor localisation using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, specifically employing the Received Signal Strength Indicator (RSSI) for distance estimation. Different solutions to enhance this localisation technology’s performance are explored, with an emphasis on combining various approaches to identify the most effective one. These solutions include different RSSI signal conditioning, anchor–tag distance estimation techniques and methods for estimating the unknown tag position.

An experimental analysis was conducted in a complex indoor environment, marked by the continuous movement of working staff and numerous obstacles. The results showed that the exploitation of multichannel transmission, using RSSI signal aggregation techniques, significantly improved the localisation system’s performance, reducing the positioning error from 1.5 meters to about 1 meter.

Other solutions, such as RSSI signal filtering, distance estimation with an empirical propagation model or Machine Learning (ML), numerical optimisation and ML models for estimating the tag’s unknown position, also impacted performance but to a lesser extent. These solutions resulted in either a decrease or an increase in positioning errors, depending on the specific combination of solutions adopted.

The study’s findings suggest that the use of multichannel transmission and the combination of RSSI signals from different transmission channels are crucial for achieving optimal performance. This approach leverages the full potential of BLE 5.0 technology and is the most significant factor in reducing positioning errors. The paper concludes that the results can guide designers in choosing appropriate solutions based on the desired accuracy of the localisation system. However, it’s noted that the results are specific to the tested conditions and may vary under different operating scenarios.