BeaconZone Blog – iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms

Using ESP32 BLE Modules for Indoor Asset Tracking

A new paper titled Evaluation of RSSI-Based Distance Estimation with ESP32 BLE Modules for Indoor Asset Tracking investigates the accuracy and reliability of Bluetooth Low Energy (BLE) technology when using Received Signal Strength Indicator (RSSI) data for determining distances indoors. The study uses ESP32 modules due to their affordability, energy efficiency, and built-in BLE support. Experiments were carried out in three conditions: clear line-of-sight, wall obstruction and a mobile tracking scenario.

Using a log-distance path loss model with a reference RSSI of −47 dBm at one metre and a path loss exponent of 2, the authors found that the ESP32 BLE system could reliably estimate distances within four metres under line-of-sight conditions, with less than 25 per cent error. Beyond five metres, however, the signal became unstable and led to overestimation of distances, particularly in obstructed environments. A wall caused an immediate signal drop of 6 dBm even at one metre, and packet loss rose from zero per cent at short distances to around fifty per cent at 8.5 metres. Mobile tracking showed irregular RSSI jumps, making movement detection inconsistent.

The results demonstrate that raw RSSI values are too variable for accurate standalone tracking, mainly due to reflection, absorption, and interference effects. While simple to use and inexpensive, the technique is unreliable for precise positioning. The study concludes that to achieve dependable performance, especially in complex indoor settings such as hospitals or factories, more advanced methods are needed. These could include Kalman filtering, RSSI fingerprinting, or sensor fusion combining multiple BLE readers or inertial sensors. Such enhancements could reduce estimation errors from about 500 per cent to below 30 per cent over ten metres.

Overall, the research establishes a baseline understanding of the limitations of ESP32-based BLE tracking systems and provides a foundation for future work aimed at improving indoor positioning accuracy through data filtering and sensor integration.

Which Beacons for 2-way radio Motorola TRBOnet Plus?

TRBOnet is a 2-way radio dispatch system sold and supported by Motorola. It can use iBeacons to provide for locating where the Motorola radio GPS doesn’t work. This allows radios and hence people to be located indoors or undercover areas such as trees.

TRBOnet Plus will work with any iBeacons. The ones with higher battery capacity tend to be used so that batteries don’t have to be replaced for years.

The i3 is popular for use with TRBOnet as it takes AA batteries that can be easily sourced and the unit has screw tags for permanent mounting. Some sites also use USB beacons that can be powered from the mains via USB power supplies.

If you use the i3, or any beacon using AA batteries, we recommend you use lithium AA batteries rather than alkaline. This will not only provide a longer battery life but will also provide better resilience at lower temperatures.

New Bluetooth Low Energy Error Correction Using AI

A new paper Error Correction in Bluetooth Low Energy via Neural Network with Reject Option by Almeida et al. (2025) presents a new method for improving data reliability in Bluetooth Low Energy (BLE) communication without modifying the transmitter. The technique combines cyclic redundancy check (CRC) error detection with a neural network that has a reject option, allowing it to identify and correct bit errors more effectively.

The study explains how BLE devices, particularly in Internet of Things (IoT) applications, suffer from data corruption due to multipath fading and interference. Traditional error-correcting codes, such as Turbo or LDPC, are unsuitable for BLE because of their computational and memory demands. Instead, the authors propose an Extreme Learning Machine (ELM) neural network that detects uncertain bits using a reject output (labelled R) and then flips them for CRC revalidation, iterating until the packet is corrected.

Simulations using Rayleigh fading and additive white Gaussian noise channels showed that the method achieved correction rates between 94–98% for single-bit errors and 54–68% for double-bit errors, depending on packet size. It significantly lowered packet error rates and improved throughput compared with uncorrected transmission.

When applied to compressed grayscale image transmission, the method restored visual quality under noisy conditions (signal-to-noise ratios of 9–11 dB). Measured using Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), image quality improved markedly, often recovering most of the lost detail.

The approach outperformed other CRC-based correction algorithms such as CRC-ADMM and CRC-BP while requiring less computational power and memory. Processing times were substantially lower, enabling near real-time correction suitable for BLE and IoT devices.

The proposed neural network with reject option offers an efficient, scalable, and energy-aware method for enhancing BLE reliability without additional transmitter complexity. It reduces retransmissions, improves data integrity, and enhances performance in both data and multimedia transmission scenarios.

Can Beacons Be Used on Aircraft?

We have had enquiries whether beacons can be used on aircraft. While there’s no specific guidance on beacons from aviation authorities, beacons transmit the same radio signals to other Bluetooth devices such as the FitBit, Android Wear, Apple Watch and Bluetooth headphones. These devices are classed as Personal Electronic Devices (PED).

The use of PEDs depends on the airline. Both the FAA and EASA have guidelines for the use of PEDs. Smart Luggage is No Longer Allowed.

Improving Accuracy and Adaptability in Complex Indoor Environments

A new paper presents a framework for indoor localisation and the monitoring of Activities of Daily Living (ADLs) using Bluetooth Low Energy (BLE) signals and machine learning. It tackles the challenges of accuracy and adaptability in complex indoor environments by introducing a novel closed-loop system that recalibrates itself with live Received Signal Strength Indicator (RSSI) data. This allows the system to learn from a small set of samples, enrich them to cover wider distances, and maintain accuracy over time.

The approach achieves localisation errors as low as 0.5 to 0.8 metres, improving on previous methods by about 65 per cent. Once accurate positioning is achieved, the framework can identify ADLs such as cooking, sitting, or sleeping with an accuracy of 91 per cent, relying on patterns of movement and location. The architecture integrates three layers: an edge layer with beacons and devices, a cloud layer for processing and storage, and a public layer providing real-time services to caregivers or family members.

The work combines signal filtering, line-of-sight classification, distance estimation, beacon selection optimisation, and location estimation through advanced algorithms such as Random Forests, Gradient Boosting, and Bi-LSTMs. Evaluation in a controlled living environment confirmed reliable performance, with strong results across multiple test routes.

The authors highlight limitations, such as RSSI’s sensitivity to environmental conditions, hardware variability, and challenges in adapting to diverse residential layouts. They suggest future work in hybrid sensing methods, generative models for dataset expansion, and personalised ADL modelling.

The study demonstrates that a feedback-driven, machine learning-based RSSI system can offer scalable, accurate, and adaptable indoor localisation and ADL monitoring, supporting safer and more independent living environments, particularly for elderly people or those with disabilities

New Finder Solution for Warehouses: Pinpoint What Matters, Effortlessly

Warehouses are dynamic, complex environments. Deeply stacked inventory, towering shelves, and a constant flow of goods create a labyrinth where even the most meticulous organisation can be challenged. This complexity often renders traditional item-finding technologies, like AoA (Angle of Arrival) and RSSI (Received Signal Strength Indicator) trilateration, ineffective due to persistent line-of-sight issues and a multitude of signal reflections.

Enter the our new warehouse Finder, a specialised solution tailored to the unique demands of locating items within warehouses. Developed by BeaconZone, this innovative system leverages a decade of extensive experience and a deep understanding of customer requirements in these challenging environments.

The Problem Solved: Finding What You Need, When You Need It

Imagine the frustration and wasted time spent searching for a crucial pallet, box or container. The Warehouse Finder is designed to eliminate these inefficiencies by allowing you to reliably locate tagged items anywhere in your warehouse.

This translates into significant benefits:

Saves Time, Effort, and Cost: Drastically reduce the time and resources spent manually searching for items.
Eliminate Loss: Prevent the ongoing costs and operational delays associated with losing items you know are on-site.
Proactive Missing Item Detection: Be instantly alerted when items leave the site without authorisation, enabling swift action and preventing extensive searches.

The Warehouse Finder stands apart from existing solutions with its unique blend of simplicity, efficiency, and robustness:

Superior to QR Codes and Traditional RFID/Barcode Systems: Unlike solutions that require manual scanning and re-scanning every time an item moves, the Warehouse Finder offers automated, continuous tracking. Once an item is tagged, its location is dynamically updated without any additional effort.
Simpler and More Cost-Effective: Forget the complexities and expense of installing extensive gateway or anchor infrastructure. The Warehouse Finder operates as a standalone solution, requiring no local network integration, no dedicated servers or platform, and, crucially, no recurring subscriptions.
Outperforms Other Solutions: Where other systems falter in complex environments with line-of-sight obstructions, the Warehouse Finder is engineered to deliver accurate location data, overcoming the challenges that hinder RSSI and AoA systems.
Exceptional Ease of Use: Managing your assets is straightforward. Easily input new items or scan them in using existing barcodes. Warehouse floor personnel can quickly search for item locations using handheld units, putting critical information directly at their fingertips.
Unrivalled Maintainability: With typical beacon battery lifetimes exceeding 10 years and easily replaceable batteries, the Warehouse Finder offers a low-maintenance, long-term solution.

The Simplest Solution That Solves the Problem.

The Warehouse Finder represents a paradigm shift in warehouse inventory management. It’s not just another tracking system. It’s a precisely engineered solution designed to cut through the complexity and deliver clear, actionable location intelligence.

If you’re looking to revolutionise your warehouse operations and experience the benefits of precise, effortless item finding, please do contact us to learn more.

Tracking Workers Indoors on Construction Sites

A newly published paper presents a deployable Real-Time Locating System (RTLS) for tracking workers indoors on construction sites using Bluetooth Low Energy (BLE) technology. Existing systems often face challenges with cost, wiring, reliance on smartphones, accuracy, and adaptability to the constantly changing layout of construction projects. This study introduces a fully beacon-based system that replaces traditional receivers with low-cost, battery-powered BLE beacons and employs a modular placement strategy, reducing costs and simplifying installation.

The proposed system combines a wireless hardware setup with localisation algorithms based on triangulation and filtering techniques. It addresses issues such as inconsistent signal strength and multipath interference by applying post-processing methods including Kalman filters, exponential smoothing and moving averages. Tests carried out in controlled environments showed localisation errors of around 0.56 metres for moving workers and 0.64 metres for stationary ones, which is an improvement over many previous studies. The research also examined different placements of beacons on the body, with hardhat placement giving the most reliable results.

The system balances accuracy with deployability and cost efficiency. It offers flexibility through adjustable smoothing levels, allowing it to serve both safety-critical real-time monitoring and longer-term productivity analysis.

The paper demonstrates that a beacon-only BLE RTLS can provide a scalable, low-cost, and accurate solution for indoor worker tracking in construction, with applications in safety management, productivity monitoring, and automated workspace identification

Can USB Beacons Receive Beacon Transmissions?

You might wonder whether USB beacons can be used to enable desktops/laptops or any USB device to receive beacon transmissions.

USB beacons don’t work this way and only use the USB connection for power. A few such as the Minew U1 have UART USB serial support that can be used to control the beacon but it still doesn’t detect beacons. It beacon only sends and doesn’t receive.

What you need is a ‘Sniffer’ such as the ABSniffer 528. It scans for Bluetooth devices and sends the data via USB to the device powering it.

Alternatively, look for a standard Bluetooth dongle that that supports Bluetooth Low Energy (LE) and an associated programming API for ESP32, Raspberry Pi, Windows or Linux.

Do You Have Bluetooth 6.0 Beacons with Channel Sounding?

The situation with Bluetooth 6.0 is essentially the same as it was with Bluetooth 5. The adoption of Bluetooth Channel Sounding, much like earlier advancements such as Mesh, Direction Finding and Angle of Arrival (AoA), is expected to face significant delays before becoming generally available in deployable products.

Historically, the uptake of newer Bluetooth Low Energy (LE) enhancements has been slow, largely because they require not only new hardware but also updated software that makes use of the latest Software Development Kits (SDKs). This combination adds complexity to both development and deployment. In addition, compatibility constraints mean that new Bluetooth LE features typically cannot be retrofitted to existing smartphone devices, which further limits their early adoption.

At this time, the only device we are aware of that offers Bluetooth 6.0 Channel Sounding capability is the Moko L03. While we do not stock this beacon as a standard item, it can be sourced and supplied as part of our consultancy and development work.

For those who need effective distance measurement, another option is to consider beacons such as the iBS03R, which includes a dedicated Time of Flight (TOF) distance sensing hardware module.

Survey of Smartphone Datasets for Indoor Localisation

A new paper is the result of collaboration between Spanish and Italian university researchers. It surveys publicly available datasets for indoor localisation, with a focus on machine learning approaches that make use of the sensors embedded in smartphones. It reviews twenty datasets released between 2014 and 2024, noting the growing trend towards multi-sensor data collection, not only Wi-Fi and Bluetooth but also accelerometers, gyroscopes, magnetometers and other signals.

Bluetooth Low Energy (BLE), features prominently throughout the survey. It is identified alongside Wi-Fi as one of the most widely used technologies for radio frequency-based localisation, relying on received signal strength from Bluetooth beacons to triangulate or fingerprint positions. Several datasets in the survey focus on BLE signals, in the form of iBeacons deployed within controlled environments such as university buildings or libraries.

The review also discusses hybrid approaches, where BLE data is combined with other signals such as inertial measurements or geomagnetic readings, to improve robustness and accuracy. Machine learning techniques are highlighted as particularly effective in handling the noisy and fluctuating nature of Bluetooth signals in complex indoor environments. Some studies combine BLE with Wi-Fi fingerprints in unified models, enhancing resilience against signal drift.

In conclusion, the survey underscores Bluetooth’s importance as a practical, widely deployed technology in indoor localisation research, while also pointing out the limitations of current BLE datasets in terms of scale, diversity of environments, and long-term variability.