Category: Location

Auto-Adjusting Location Algorithm

A new study uses an indoor localisation system that integrates Bluetooth Low Energy (BLE) with an Internet of Things (IoT) framework to improve accuracy in tracking individuals, particularly those with cognitive impairments such as Alzheimer’s and dementia. The system employs an auto-adjusting algorithm that dynamically optimises received signal strength indicator (RSSI) coefficients based on real-time environmental factors, leading to improved location estimation precision.

Existing systems relying on RSSI often suffer from inaccuracies due to environmental interferences, signal fluctuations, and the use of static coefficient assignments. To address these challenges, this study develops an auto-adjusting algorithm that dynamically selects coefficients based on RSSI classifications.


The system consists of a central unit, a Raspberry Pi, and BLE peripheral nodes that communicate wirelessly. It collects real-time RSSI data and applies a path loss model to estimate distances. A web interface was developed to facilitate real-time tracking and data visualisation. The system was tested in a healthcare environment with five rooms, comparing the performance of fixed coefficient models against the proposed dynamic approach.

The experimental results showed that using fixed coefficients in distance estimation led to an initial error of 28.03%. By implementing the auto-adjusting algorithm, the error was reduced to 8%, while the maximum localisation error was decreased to 2.01 meters. Additionally, the system demonstrated high energy efficiency, with BLE peripherals operating for approximately 499 hours on a standard 230 mAh battery, reinforcing its suitability for IoT applications.

One of the main advantages of the auto-adjusting algorithm is its ability to dynamically adapt parameters. The system adjusts the path loss exponent (n) and reference signal strength (A) based on real-time RSSI classifications, improving accuracy significantly. This approach minimises localisation inaccuracies by continuously recalibrating signal strength values. The system is also energy-efficient, making it ideal for continuous tracking in various environments. Additionally, it is scalable and can be integrated with other indoor positioning systems such as Ultra-Wideband (UWB) and Wi-Fi.

The system achieves higher accuracy, maintaining a maximum error of only 2.01 meters compared to fixed coefficient models. Additionally, the BLE-based approach ensures long battery life and cost-effectiveness, making it suitable for healthcare and security applications. Compared to previous studies, the proposed algorithm proved more reliable for positioning in real-world environments.

Using Support Vector Regression (SVR) with Beacons

A new study (pdf) explores optimising Bluetooth Low Energy (BLE) beacon-based indoor positioning systems using support vector regression (SVR). It addresses the challenge of accurately identifying building occupants’ locations in real time, a critical requirement for applications such as emergency evacuations and asset tracking. Traditional methods, including trilateration and RSSI-based techniques, can face limitations like signal interference and non-line-of-sight issues.

The research adopts a fingerprinting method that uses pre-trained SVR models to improve positioning accuracy. BLE beacons, which are cost-effective and energy-efficient, were deployed across a controlled environment, and extensive RSSI data was collected and pre-processed. The model’s hyperparameters were fine-tuned to achieve optimal performance. Experimental results demonstrated a significant improvement in accuracy, with the lowest root mean squared error (RMSE) recorded as 0.9168 feet.

The findings underscore the potential of machine learning, particularly SVR, in enhancing the reliability of indoor positioning systems. This study provides a benchmark for future research, highlighting its practical applications in emergency scenarios and the advantages of BLE technology in such implementations.

Improving Bluetooth Location Accuracy

New research focuses on enhancing indoor localisation using Bluetooth Low Energy (BLE) technology by addressing challenges in signal instability and noise. The authors propose a system combining the Kalman filter for signal smoothing and deep learning models, specifically Autoencoders and Convolutional Autoencoders, for feature extraction from Received Signal Strength Indicator (RSSI) data. The method uses a fingerprinting approach, collecting data in two phases, offline for creating a reference database and online for matching new measurements to predict locations.

The study demonstrates that integrating the Kalman filter with the Convolutional Autoencoder model yields an average localisation error of 0.98 metres, significantly improving accuracy. Experimental comparisons with existing methods highlight the proposed system’s effectiveness in balancing cost, energy efficiency, and precision. The findings suggest this approach as a robust solution for indoor localisation in environments requiring high accuracy and low energy consumption.

UWB vs Bluetooth Beacons

Ultra-Wideband (UWB) technology has recently emerged as a contender to Bluetooth beacons, with some companies traditionally focused on Bluetooth now marketing UWB as the next generation solution. But does UWB live up to the promise?

UWB undeniably offers a key advantage: more accurate location tracking. With its ability to determine positions down to tens of centimetres, it surpasses Bluetooth in precision. However, this comes with significant trade-offs that should be carefully considered before adopting the technology.

One of the critical drawbacks of UWB is the lack of standardisation. Unlike Bluetooth, which operates on a well-defined and widely supported Bluetooth LE standard, UWB devices are proprietary. This means users are locked into a single vendor’s ecosystem, unable to mix and match devices from different suppliers. If the chosen vendor’s devices become obsolete, the entire solution becomes redundant, forcing costly upgrades or a complete overhaul.

The lack of standardisation also impacts the broader ecosystem. Bluetooth devices benefit from a vibrant market with multi-vendor compatibility, driving competition and keeping costs low. In contrast, UWB solutions rely on custom protocols, devices, and specialist skills, leading to higher costs and limited interoperability. While Bluetooth beacons have a range of up to 50 metres, and even 200 metres or more for certain devices, UWB typically operates within a range of 30 to 40 metres. Some advanced Bluetooth devices can even reach up to 1 kilometre, providing greater flexibility in many applications.

Power consumption is another area where Bluetooth outshines UWB. Bluetooth beacons are designed to operate efficiently, often lasting months or even years on a single battery. UWB devices, on the other hand, are more power-hungry, typically lasting only weeks in positioning applications. This makes them less practical for long-term deployments, especially in IoT scenarios where low maintenance is a priority.

Scalability is a growing concern with UWB. The technology generates and needs to process more data than Bluetooth, which can lead to bottlenecks and reduced performance as the network expands. This poses challenges for large-scale deployments, where simplicity and efficiency are critical.

Moreover, UWB’s compatibility is limited when compared to Bluetooth’s universal presence. UWB devices are primarily detected by iOS devices, with limited support on Android. This constrains their usability in a diverse market. Bluetooth, in contrast, is supported by virtually every modern smartphone and a large number of third party gateways, making it a more versatile choice.

Bluetooth beacons also offer greater functionality beyond location tracking. They can perform various sensing tasks, such as monitoring temperature, humidity, air pressure, light levels, and even detecting smoke, water leaks, or proximity. UWB, being narrowly focused on location tracking, lacks this flexibility, limiting its utility in IoT applications.

Ultimately, the decision between UWB and Bluetooth depends on your specific needs. If you require extremely precise location tracking within a limited range and can accommodate the higher costs and proprietary nature of UWB, it may be worth considering. However, for most use cases, Bluetooth remains the more efficient, flexible and cost-effective option. Its standardisation, broad compatibility, and multi-functional capabilities make it a reliable choice for tracking and IoT applications alike.

Framework for Evaluating Indoor Tracking Systems

There’s new research outlining the use of the MobiXIM framework for developing, evaluating, and refining indoor tracking systems (ITS), addressing challenges related to the lack of standardisation in the field. Indoor tracking, necessary where GPS is ineffective, relies on methods such as infrastructure-based (e.g., Bluetooth beacons using Received Signal Strength Indication), infrastructure-less (inertial and magnetic sensors) and collaborative systems (peer-to-peer communication between devices). These approaches encounter issues like accuracy, reproducibility and data collection costs.

MobiXIM integrates tools to streamline the ITS creation process, incorporating a mobile app for data collection and a web-based orchestrator platform. It employs Bluetooth Low Energy (BLE) iBeacons, both physical and virtual, to enhance location estimates. Physical iBeacons are commercial devices broadcasting signals detectable by smartphones, while virtual iBeacons simulate these signals for testing scenarios without physical deployment. The signals allow devices to calculate their proximity to a beacon, correcting their location estimates based on signal strength.

The framework’s plugin-based architecture promotes modularity, enabling researchers to mix and match existing algorithms. The methodology includes filtering noise from sensor data, positioning via algorithms like Pedestrian Dead Reckoning, and correcting errors through collaborative adjustments among devices and beacon signals. The corrected data is evaluated using metrics such as positioning accuracy and trajectory similarity.

Experiments in a university building demonstrated how collaboration between devices and interaction with beacons significantly improved accuracy. The replay feature of MobiXIM allows researchers to simulate and adjust experimental setups, testing variables like beacon density and device collaboration.

iBeacons play a critical role by providing a reliable reference point for error correction and enhancing the overall accuracy of indoor positioning systems, particularly when combined with collaborative algorithms.

Advanced Bluetooth LE Fingerprinting Techniques

There’s new research that explores advanced methods for indoor localisation focusing on Bluetooth Low Energy (BLE) and fingerprinting techniques. Due to the limitations of GPS in indoor environments, this study evaluates alternative methods, including novel algorithms and hybrid approaches, for improving localisation accuracy.

Key insights include the Positive Weighted Centroid Localisation (PWCL) algorithm, which prioritises stronger signals, and the HYBRID-MAPPED method, which integrates multiple filtering techniques like outlier detection and mapping filters. These methods were tested in a real-world environment with 47 sample points, employing Bluetooth LE based iBeacon devices to collect data. The experimental setup included mapping a space onto a coordinate system and implementing four localisation strategies.

Results demonstrated that PWCL outperformed the traditional Weighted Centroid Localisation (WCL) algorithm by reducing errors. The HYBRID-MAPPED approach achieved the highest accuracy with an average error of 1.44 metres, a significant improvement over WCL’s 2.51 metres. The study’s findings underscore the effectiveness of combining BLE with filtering techniques to overcome noise and environmental challenges.

The research highlights potential applications in healthcare, retail, and other public settings, where accurate indoor localisation is critical.

Enhancing Indoor Navigation for Elderly with Cognitive Impairments Using iBeacon and Augmented Reality

A new research paper explores the development and application of an indoor navigation system using augmented reality (AR) technology, aimed at helping older people, especially those with Mild Cognitive Impairment (MCI) and Alzheimer’s disease. The use of technology aims to reduce the burden on caregivers and improve patient safety.

One of the key technologies used in the navigation system is iBeacon. This supports the identification of a user’s position, providing real-time navigation guidance by transmitting a unique signal to devices such as smartphones. iBeacons are strategically placed in buildings, allowing a smartphone or tablet to detect their signals and calculate the proximity to the beacon, aiding in determining the user’s location.

The indoor navigation system utilises iBeacon technology by placing beacons in different locations throughout the environment. As users move, their mobile device interacts with these beacons, receiving signals that help update their position on a digital map and provide step-by-step directions. This system also integrates augmented reality to superimpose visual cues, such as directional arrows, on the device screen, guiding users to their destination.

The use of iBeacon allows the indoor navigation system to function with high accuracy (typically within a few meters), making it suitable for complex environments like nursing homes or hospitals, where patients may have difficulty finding their way without assistance. It also works without requiring constant internet connectivity, which can be a major advantage for offline operation in secure environments. This technology is low cost, easy to deploy, and scalable, making it an ideal solution for healthcare settings focused on enhancing the mobility and autonomy of elderly individuals with cognitive impairments.

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.