Category: IndoorPositioning

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.

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.

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

Location System Anchor Optimisation

Researchers from Department of Computer Science, University of Jaén, Spain have a new paper on OBLEA: A New Methodology to Optimise Bluetooth Low Energy Anchors in Multi-occupancy Location Systems.

This paper introduces a new methodology called OBLEA, which aims to optimise BLE anchor configurations in indoor settings. It takes into account various BLE variables to enhance flexibility and applicability to different environments. The method uses a data-driven approach, aiming to obtain the best configuration with as few anchors as possible.

The OBLEA method offers a flexible framework for indoor spaces where the occupants are fitted with wrist activity bracelets (beacons) and BLE anchors are set up. The anchors then collect and aggregate data, sending it to a central point (fog node) via MQTT.

A dataset was generated with the maximum number of anchors in the indoor environment, and different configurations were then trained and tested based on this dataset. The best balance between fewer anchors and high accuracy was chosen as the optimal configuration.

This methodology was tested and optimised in a real-world scenario, in a Spanish nursing home in Alcaudete, Jaén. The experiment involved seven inhabitants in four shared double rooms. As a result of this optimisation, the inhabitants could be located in real time with an accuracy of 99.82%, using a method called the K-Nearest-Neighbour algorithm and collating the signal strength (RSSIs) in 30-second time windows.

View wearable beacons

Using Beacons to Improve Location of Mobile Robots

There’s new research from King Mongkut’s Institute of Technology Ladkrabang, Thailand on Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line (pdf).

Mobile robots are broadly divided into automated guided vehicles (AGVs) and autonomous intelligent vehicles (AIVs). AGVs are confined to predetermined paths while AIVs have the flexibility to move in any direction without any infrastructural alterations. Factories often face challenges when it comes to synchronising mobile robots with target machinery. The paper presents a solution to reduce errors in robot localisation and improve parking accuracy.

Adaptive Monte Carlo Localisation (AMCL), a probability-based localisation system which relies on LiDAR and odometry data often misjudges robot positions in environments where the factory production line and room shapes are alike. To mitigate this, a novel landmark-based localisation strategy using iBeacon, a Bluetooth Low Energy (BLE) device, is proposed. This approach aims to provide more accurate localisation of mobile robots, addressing the shortcomings of the AMCL system.

View iBeacons

Indoor Tracking of Individuals with Mild Cognitive Impairment

There’s new research from the USA on Indoor Localization using Bluetooth and Inertial Motion Sensors in Distributed Edge and Cloud Computing Environment (PDF). The paper describes a low-cost, scalable, edge computing system for tracking indoor movements in a large indoor facility. The system uses Bluetooth Low Energy (BLE) and Inertial Measurement Unit sensors (IMU) and is designed to facilitate therapeutic activities for individuals with Mild Cognitive Impairment.


The implementation involved instrumenting a facility with 39 edge computing systems and an on-premise fog server. Subjects carried BLE beacon and IMU sensors on-body. The researchers developed an adaptive trilateration approach that considered the temporal density of hits from the BLE beacon to surrounding edge devices to handle inconsistent coverage of edge devices in large spaces with varying signal strength. They also integrated IMU-based tracking methods using a dead-reckoning technique to improve the system’s accuracy.


The conclusions of the study showed that the proposed system could robustly localise the position of multiple people with an average error of 4 meters across the entire study space, also showing 87% accuracy for room-level localisations. The integration of IMU-based dead-reckoning with Bluetooth-based localisation further enhanced the system’s accuracy.

Using Packet Loss to Infer Location

There’s new research from the University of Illinois titled Packet Reception Probability: Packets That You Can’t Decode Can Help Keep You Safe (pdf). Many existing systems estimate distance using the Receiver Signal Strength Indicator (RSSI) which is negatively impacted by sampling bias and multipath effects. As an alternative, the study uses Packet Reception Probability (PRP) that utilises packet loss to estimate distance.

Localisation is achieved through a Bayesian-PRP approach that also includes an explicit model of multipath. To facilitate straightforward deployment, there’s no need for any modifications to hardware, firmware, or driver-level on standard devices and only minimal training is required.

A variety of devices were used including Bluvision iBeeks, BluFi, a Texas Instrument Packet Sniffer, a laptop, and Android smartphones (Nexus5x). 60 iBeacons were deployed in a library and 38 in a retail store. The Texas Instrument Packet Sniffer, connected to a Windows laptop was used for packet reception from beacons. Android phones were equipped with a purpose-built Android app.

PRP was found to provide metre-level accuracy with just six devices in known locations and 12 training locations. Combining PRP with RSSI was found to be beneficial at short distances up to 2m. Beyond distances of 2m, fusing the two is less effective than using PRP alone because RSSI becomes de-correlated with distance.