Category: Location

Real-World Performance Evaluation of a Hybrid Bluetooth Low-Energy Positioning and Direction-Finding System

There’s new research evaluating the performance of a Bluetooth Low-Energy (BLE) positioning and direction-finding system under conditions that closely mimic real-world usage. The aim of the study was to enhance a BLE-based hybrid algorithm, which integrates both positioning and direction-finding capabilities. The researchers focused on evaluating the system in realistic conditions, which included using multiple types of devices, separating the devices used for creating the database from those used for evaluation, and ensuring a sufficient time gap between data collection and evaluation measurements.

The hybrid algorithm used in the study combines proximity detection, based on the strongest Received Signal Strength Indicator (RSSI), with a fingerprinting approach, where the evaluation data is compared to a pre-existing database. By limiting the search area for positioning to locations with the highest RSSI, the algorithm aims to reduce significant positioning errors. The study also integrated direction-finding functionality into the algorithm, taking into account issues such as signal obstruction caused by the user’s body, which can block radio signals from certain directions.

The evaluation was conducted in a corridor environment, with BLE beacons installed along the walls and ceilings. The research utilised five different smartphone models for both data collection and evaluation. To simulate real usage, measurements were taken from four directions at each evaluation point. The study compared the performance of this hybrid method with a previously proposed method that only included direction estimation based on signal divergence.

The findings demonstrated that the hybrid algorithm significantly outperformed the earlier method in terms of both positioning and direction-finding accuracy, especially under realistic usage conditions. Although the performance of the system declined when the intervals between the BLE beacons were increased, it remained at an acceptable level even with fewer beacons installed. This suggests that the hybrid algorithm is robust and effective, even when the system’s infrastructure is reduced.

In conclusion, the study demonstrated the effectiveness of the hybrid BLE algorithm for positioning and direction-finding in realistic environments. The findings emphasised the importance of conducting performance evaluations under real-world conditions, which better reflect the challenges and variability of actual usage.

Creating User Indoor Movement Logs

New research (pdf) looks into the development of an application that tracks user indoor movement logs using Bluetooth beacons. The main focus is on creating a system that is easy to install and use without requiring expertise in beacon installation or positioning analysis. This application is designed for personal home use and simplifies the process by allowing users to install beacons in desired locations, name the spaces and track their movements within the home. The application records users’ movements and the time spent in specific spaces, offering statistical insights such as daily and weekly movement patterns.

The Bluetooth beacons used in this system rely on RSSI (Received Signal Strength Indicator) to estimate the distance between the user’s device and the beacons, with methods like the Kalman filter applied to reduce noise and improve accuracy. To verify its effectiveness, the study conducted experiments comparing manually recorded movement logs with those captured by the application. The results showed an accuracy rate of over 99%, making the system a practical solution for indoor movement tracking in homes, small offices, and other limited spaces.

Key advantages include ease of installation, automatic logging of movement data, and statistical analysis of time spent in different rooms. The application is also suitable for environments like small offices with fewer than 10 employees.

Challenges in Deploying a Location-Based Coupon Service

New research Deploying a Location-Based Coupon Recommendation Service in Retail: Challenges and Lessons Learnt explores the implementation of a Bluetooth Low Energy (BLE) beacon-based location service designed to enhance the retail shopping experience by offering personalised coupon recommendations. This system not only improves customer engagement but also provides retailers with valuable insights into consumer behaviour. The study looks into various challenges encountered during the development and deployment phases, expanding on technical, business, and user-related difficulties, and offers lessons that go beyond typical technological issues.

One of the primary technical challenges was ensuring accuracy in tracking customers’ locations within the store. Initially, the system used trilateration to pinpoint exact X-Y coordinates. However, this method proved inadequate due to signal interference and environmental factors. As a result, the team adopted an area-based tracking system, which was better suited for the retail context. To maintain robustness and scalability, advanced techniques such as fingerprinting and machine learning algorithms were employed, which allowed the system to adapt to various store layouts. Expanding the service to over 2,000 stores posed scalability issues that required innovative solutions, particularly in managing different store environments and layouts. Additionally, cost constraints, particularly in regard to hardware and devices, and ensuring compliance with privacy regulations like the GDPR, were significant hurdles. The system had to balance performance with legal requirements while limiting data collection to ensure customer privacy.

From a business perspective, the service needed to align with operational goals. One key challenge was determining the appropriate level of accuracy for tracking customer movements. After discussions with the business stakeholders, it was agreed that precise X-Y positioning was unnecessary; instead, tracking customer movements within specific store areas, such as aisles or product sections, sufficed. Defining these areas of interest was critical, as some store sections required more detailed tracking than others, depending on the season or product demand. For example, chocolate aisles may be more important during the winter, whereas ice cream sections are prioritised in the summer. This required a flexible, business-driven approach to configuring the system.

Beacon placement posed another set of challenges. Initially, the beacons were installed at human height on store shelves, but this led to significant interference from obstacles such as stocked products. Moving the beacons to the ceiling reduced signal interference and provided more stable coverage. However, this required careful calibration to ensure optimal signal strength, battery life, and overall system performance. The team also had to consider different types of mobile devices used by customers, as varying device capabilities affected the system’s performance, requiring additional adjustments and testing.

User acceptance played a crucial role in the success of the system. Initially, employees expressed concerns about the potential health risks of working near BLE beacons. These concerns were alleviated after the staff was educated about the low levels of radiation emitted by the beacons. On the customer side, users were more likely to engage with the system when offered personalised incentives, such as coupons tailored to their shopping preferences. The system proved effective, as it increased average basket size, showing that personalised coupon recommendations not only improved the shopping experience but also contributed to higher sales. Customers appreciated receiving relevant offers as they moved through the store, streamlining their shopping experience and saving them time.

The study concludes by highlighting the importance of integrating technical solutions with business goals, user preferences and privacy considerations. The deployment of location-based services in retail is not just a technical exercise but one that requires close collaboration between developers, retailers, and end-users. The lessons learned from this project provide a valuable roadmap for future implementations of similar services, emphasising the need for flexibility, privacy protection, and user-centric design.

What Bluetooth Systems Can Track Working Using Their Smartphones?

Contrary to popular belief, it’s not possible to directly track smartphones using Bluetooth alone. Both iOS and Android devices have built-in privacy protections and limitations that prevent this kind of tracking.

For iOS devices, Apple has implemented randomised MAC addresses for Bluetooth transmissions. This means that the unique identifier broadcast by an iPhone or iPad changes regularly, making it impossible to consistently track a specific device over time. Android doesn’t continuously send out Bluetooth transmissions.

However, whilst smartphones themselves can’t be directly tracked via Bluetooth, there are systems that can perform location tracking using Bluetooth beacons and gateways. These systems rely on people carrying small Bluetooth beacons, often in the form of keyfobs or badges, which broadcast a unique identifier. Fixed gateway devices are then installed throughout an area to detect these beacons.

When a gateway detects a beacon, it records the beacon’s identifier and signal strength to infer distance, along with a timestamp. By combining data from multiple gateways, the system can estimate the location of the beacon, and by extension the person carrying it, within the covered area. This approach is often used in workplace settings for things like occupancy monitoring or contact tracing.

It’s important to note that these systems require active participation – people must choose to carry the beacon devices. This is quite different from the idea of passively tracking smartphones without user consent.

Some retailers have experimented with using Bluetooth beacons to track customers’ movements within stores. However, this still requires customers to have the store’s app installed and Bluetooth enabled on their phones. These work the other way around by having fixed beacons and the app detecting the beacons. It’s not a covert tracking system, but rather one that customers opt into, often in exchange for discounts or other benefits. It’s less reliable due to the nuances of ensuring the app runs on all phones, at all times.

In summary, whilst it’s not possible to directly track smartphones via Bluetooth due to privacy protections and limitations, there are Bluetooth-based systems that can provide location based services when users actively participate.

Low-Cost AoA Wayfinding

There’s a new paper (pdf) on a low-cost wayfinder system using Bluetooth’s Angle-of-Arrival (AoA) technology. This system is designed to help visually impaired individuals navigate public spaces, such as airports or shopping centres. The innovation lies in moving the antenna array required for angle measurement onto the user’s device, simplifying the beacon infrastructure. Each beacon becomes a low-cost, single-antenna transmitter, significantly reducing the deployment cost compared to traditional indoor positioning systems.

The prototype, built with Bluetooth 5.1 boards and developed using Python, successfully demonstrated accurate angle and distance measurement. The system achieved a 10° angle accuracy within 15 meters and calculated distance using the Received Signal Strength Indicator (RSSI). For visually impaired users, the system could be extended with a voice notification feature. The ultimate goal is to develop the system into a smartphone app.

Future enhancements include addressing front-and-back signal ambiguities by adding orthogonal antennas and extending the system’s range.

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.