Category: Location

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.

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.

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.

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.

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.