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NFC and Beacons

Now that some manufacturers have started including NFC in beacons, our customers have started asking about the differences between Beacons and NFC and why NFC is being included.

In proximity detection terms you can think of NFC as being an extension of ‘very near’ in iOS’s ‘near’, ‘far’ and ‘intermediate’ proximity classifications. The range is in the order of cm rather than m. In most applications a ‘near’ beacon or higher value RSSI on Android can perform a similar function as NFC. However, NFC can be made more secure in that it can provide for secure proximity detection in scenarios such as payments. So why have NFC in beacons?

NFC within the context of iBeacons can be used as a complementary technology. For example:

Enhanced Interaction: NFC could be used to provide immediate, zero-setup interaction with an iBeacon for configuration purposes or to trigger specific actions when a user intentionally brings their device close to the beacon. This can be particularly useful in situations where BLE interactions might require more steps or user permissions.

Security and Authentication: NFC’s short range can be advantageous for secure interactions. In scenarios where an iBeacon provides location-based services, NFC could add an additional layer of security by ensuring that certain actions (e.g., payments, access control) are only triggered when the user is very close to the beacon.

Information Retrieval: For cases where iBeacons signify users about something of interest nearby, an NFC tag could provide additional, detailed information or a direct action (like opening a website or downloading an app) without the need for the user to navigate through menus or apps. This could be especially useful in museums, exhibitions, or retail settings where quick information access enhances the visitor experience.

While NFC and iBeacons serve different primary functions, integrating both can lead to innovative applications that leverage the strengths of each technology for enhanced user experiences, particularly in proximity-based interactions and services.

Higher Education Engagement Using Beacons

Recent research Preliminary Mobile Beacon Application Framework for Higher Education Co-Curricular Engagement, presents a conceptual framework aimed at enhancing co-curricular engagement in higher education through mobile beacon technology. This technology uses location-based or proximity sensors to interact with users via mobile apps, potentially serving as a powerful tool for engaging students with co-curricular events on campus.

The motivation behind this work stems from the recognition of co-curricular activities’ importance in developing students’ character, alongside traditional academic curricula. The framework proposed seeks to address the challenges of low engagement in such activities due to students missing out on opportunities due to lack of awareness. By integrating mobile beacon technology with personalised notifications, the framework aims to inform students about co-curricular events happening within their proximity on campus, thus enhancing their engagement and participation.

A preliminary conceptual framework is detailed, focusing on the design and application of mobile beacon technology integrated with personalised notification features to support co-curricular engagement. This framework outlines the components necessary for developing such an application, including beacon devices, mobile apps, event databases and notification profiles. The document elaborates on the characterisation of these components and the processes involved in deploying and interacting with the beacon technology to deliver personalized and relevant co-curricular event notifications to students.

The document concludes by discussing the development of a prototype application based on the framework, using the Flutter framework for cross-platform mobile app development and the integration of beacon technology for real-time event notification.

Using ChatGPT in Beacon Applications

ChatGPT and other Large Language Models (LLMs) can be involved in Beacon-based IoT (Internet of Things) applications for tasks like classification and prediction, but it’s important to understand its limitations and best use cases. The strength of ChatGPT lies in processing and generating text based on natural language interactions, not numbers. Here’s how it might be applied in an IoT context:

Numerical Classification

For numerical classification tasks within beacon-based IoT, such as categorising temperature ranges or identifying equipment status based on sensor data, ChatGPT itself isn’t directly suited since it specialises in text data. However, you can use it to interpret the results of classification tasks done by other, more suitable machine learning models. For example, after a specialised model classifies temperature data into categories like “low”, “medium”, or “high”, ChatGPT can generate user-friendly reports or alerts based on these classifications and the context at the time of the report.

Prediction

In terms of prediction, if the task involves interpreting or generating text-based forecasts or insights from numerical data, ChatGPT can be useful. For example, after an analysis has been performed on traffic flow data by a predictive model, ChatGPT could help in generating natural language explanations or predictions such as, ‘Based on current data, traffic is likely to increase within the next hour’.

Integration Approach

For effective use in beacon-based IoT applications, ChatGPT would typically be part of a larger system where:

  1. Other machine learning models handle the numerical analysis and classification based on sensor data.
  2. ChatGPT takes the output of these models to create understandable, human-like text responses or summaries based on a wider context.

Conclusion

Thus, while ChatGPT isn’t a tool for direct analysis of numerical IoT data, it can complement other machine learning systems by enhancing the user interaction layer, making the insights accessible and easier to understand for users. For actual data handling, classification and prediction, you would generally deploy models specifically designed for numerical data processing and analysis.

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.

View All Beacons

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.