Category: RSSI

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.

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

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

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.

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.

Novel iBeacon Localisation Algorithm Modelling

Recent research A Novel Optimized iBeacon Localization Algorithm Modeling by Jiajia Shi et al, addresses the challenges in achieving high accuracy in indoor object localisation or tracking using iBeacon systems. These systems, which use Bluetooth sensors, are appealing due to their low cost and ease of setup but there can be challenges with accuracy and they can sometimes be susceptible to interference and environmental noise.

To overcome these challenges, the study focuses on developing error modeling algorithms for signal calibration, uncertainty reduction and noise elimination. The novel approach is based on the Curve Fitted Kalman Filter (CFKF) algorithms. The research demonstrates that the CFKF algorithms significantly improve the accuracy and precision of iBeacon localisation.

The paper discusses the limitations of current indoor localisation technologies, including the Received Signal Strength Indicator (RSSI) method, which is affected by multipath fading in indoor environments.

The authors propose a novel CFKF error modelling approach to enhance the estimation accuracy of iBeacon systems in field experiments. This approach includes a developed Kalman Filter (KF) state estimate algorithm based on the modified Least Squares Algorithm (LSA), a system calibration process for the RSSI and estimated distance and the CFKF error modelling for improved accuracy.

An Enhanced Triangulation Technique

Researchers from universities in Taiwan have developed a simple Bluetooth low-energy indoor positioning method using iBeacon components. The system aims to be lightweight, low-cost, and highly precise. The paper, Using iBeacon Components to Design and Fabricate Low-energy and Simple Indoor Positioning Method (PDF), introduces an enhanced triangulation technique using strength signatures of transmitted signals to improve positioning precision in planar locations.

The physical system consists of an observation (they call blind) device and multiple base stations using iBeacon components. These base stations can form virtual digital electronic fences and receive signals from blind devices, such as wearable devices or equipment tags. The positioning area is divided into rectangular or triangular subareas and the location of a blind device can be accurately located in real time using the measured strength of received signals and topology analysis.

The proposed method has an average error of less than 0.5 meters in the worst scenario and can be easily used in various environments. It integrates an STSS database and a triangulation method by evaluating the power values of received directional signals. Compared to traditional triangulation technologies, this method offers better positioning accuracy with simpler implementation procedures, reducing the overall cost of deployment.

View iBeacons

Improving Safety on Construction Sites

Researchers from Spain have recently developed a safety system that uses Bluetooth Low Energy (BLE) to ensure the correct use of Personal Protection Equipment (PPE) on construction sites. This innovative system is not only robust and reliable but also easily adaptable to various dangerous machines.

The system is built on RSSI (Received Signal Strength Indicator) information transmitted by BLE devices arranged in a particular rig combined with a Bayesian distance estimator. The aim is not merely to signal risky situations caused by the misuse of PPE but to intervene swiftly and robustly to eliminate the safety risk.

The researchers have built upon previous results on the statistically sound measurement of distances and closeness in construction sites. By collocating several BLE transmitters near orthogonally, they have managed to reduce interferences while avoiding the cost of more advanced technologies.

The practical contributions of this research include the design of the system, a working prototype and a thorough statistical analysis for finding the optimal parameters for both the software and the equipment. The research shows that using several orthogonally collocated BLE transmitters improves robustness and overall performance without requiring more complex and costly equipment.

The improvements are most significant as the number of transmitters increases. Using a diversity of devices is better when these devices are noisy and it also enhances the robustness of the solution. An arrangement of orthogonal BLE beacons allows for an increased rate of advertising messages, and an extended Kalman filter plus a discrete filter can benefit from that increased flow of data, providing a simple and efficient approximation to the problem of safety estimation.

The use of an additional beacon to notify the correct use of the PPE, implemented inside a wearable microcontroller, is a very flexible solution. It allows for different local implementations using various sensors and measurements without the need to modify the RSSI-only method in the receiver, and with any number of users. The system can be easily integrated into a wide variety of dangerous machines and tools such as angle grinders, concrete mixers and pneumatic drills.

Why is There Variation of RSSI?

We sometimes get asked whether a beacon is faulty because a customer is seeing a lot of fluctuation in the Received Signal Strength Indicator (RSSI) values, even in a seemingly stable environment and with no change in distance. The short answer is: this is normal. The reason for this lies in the complex nature of radio signals and how they interact with the environment.

Radio signals are susceptible to a variety of factors that can affect their received strength. When a beacon sends out a signal, it doesn’t just travel in a straight line to the receiver. Instead, it disperses in multiple directions and can bounce off walls, floors and other objects.

Reflections can cause the signal to take different paths before reaching the receiver. Each path can have a different length and, therefore, a different time delay. This results in a phenomenon known as multipath fading, where multiple copies of the signal arrive at the receiver at slightly different times. This can cause fluctuations in the RSSI values you observe.

While reflections are a primary cause of RSSI fluctuation, they are not the only one. Other physical changes in the environment can also contribute to this variability. For example, the presence of people moving around can affect the signal, as the human body is mostly water and can absorb radio frequencies. Similarly, other electronic devices emitting radio frequencies can interfere with the signal, causing further fluctuations.

To get a more accurate understanding of the signal strength, it’s advisable not to rely on a single RSSI value. Instead, you should look at many RSSI values over a period of time and calculate the average. This approach helps to mitigate the effects of temporary fluctuations and provides a more stable and reliable measure of signal strength.

Many people, particularly researchers, have looked into the intricacies of RSSI and its variability. Various algorithms and methods have been developed to improve the accuracy of RSSI-based distance estimation and location tracking. For those interested in a deeper understanding or potential solutions to this issue, we recommend looking at the articles tagged RSSI and RSSIStability on our blog.

BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory

There’s new research into BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory. In life science automation laboratories, monitoring and managing the position of samples is crucial. One emerging solution for sample position estimation in these settings is the use of Bluetooth Low-Energy (BLE) beacons.

Historically, many fingerprinting models that harness received signal strength (RSS) data have been proposed for indoor positioning. However, a large number of these methods require an extensive installation of beacons. In contrast, proximity estimation, which relies solely on a single beacon, emerges as a more apt solution, especially for vast automated laboratories.

The intricacies of the life science automation laboratory environment present hurdles for the conventional path loss model (PLM), a prevalent method of proximity estimation based on radio wave propagation. Addressing this challenge, the paper introduces BLE sensing devices crafted specifically for sample position estimation. The proximity estimation rooted in BLE beacon technology is explored within a machine learning framework. Here, support vector regression (SVR) is employed to capture the nonlinear correlation between RSS data and distance. Concurrently, the Kalman filter is applied to reduce deviations in the RSS data.

Experimental outcomes spanning diverse settings underline the superiority of SVR over PLM. Remarkably, SVR achieved 1m absolute errors for an impressive 95% of test samples. The addition of the Kalman filter augments stable distance predictions, effectively smoothed the raw data and mitigated extreme value impacts.

When estimating positions between parallel workbenches, the framework achieved an average mean absolute error (MAE) of just 0.752m across 12 test positions. And for position estimation on workstations, identification accuracies beyond 99.93%.

In conclusion, for labs aiming to enhance sample position estimation, the BLE beacon paired with an IoT node presents a flexible sensing solution. By integrating machine learning, particularly SVR, and the Kalman filter, this framework offers increased accuracy in both corridors and labs.