Support – BeaconZone Blog

What is a Bluetooth WiFi Gateway?

A Bluetooth–Wi-Fi gateway is a device that links Bluetooth devices to a Wi-Fi network. It enables Bluetooth equipment such as sensors, beacons and other IoT devices to communicate over Wi-Fi and exchange data with other devices on the local network, with remote servers or with cloud services.

These gateways contain both Bluetooth and Wi-Fi radios and act as a bridge between the two technologies. They are widely used in Internet of Things (IoT) applications, where they provide a simple way to bring a wide range of Bluetooth devices onto an existing Wi-Fi network so they can interoperate and share data.

Configuration is typically carried out through web pages hosted on the gateway itself. From these pages you can configure the Wi-Fi access point that the gateway connects to, specify the destination server (often using protocols such as HTTP or MQTT) and define which Bluetooth devices are permitted to have their data relayed. The configuration usually includes filtering options, allowing you to manage data sources based on Bluetooth advertising data and/or Bluetooth MAC address. Power for the gateway is generally supplied via a USB connection, which is used purely for power and not for data transfer.

In addition to Wi-Fi models, there are also gateways that connect to the network via Ethernet rather than wireless connectivity, providing a wired alternative for more throughput or better reliability.

View Gateways

How Many Connections Can an iBeacon Support?

We sometimes get asked how many connections an iBeacon can support? The answer is ‘1’ but it’s often the right answer to the wrong intended question! The intended question is usually “How many receivers can see a beacon?”

Beacons don’t usually connect. They just advertise and can be seen by a very large number of receivers that include phones, gateways or single board computers such as the Raspberry Pi. The beacon doesn’t even know the receivers are there.

The receivers only usually connect once, during setup via an app, to set the initial beacon parameters. When connected, the beacon doesn’t advertise which prevents extra receivers from connecting. Once set up, the app disconnects and the beacon starts advertising again.

This post discusses the factors relating to how many receivers can see a beacon.

Indoor Positioning Using Bluetooth RSSI and Video Data

New research from Portugal aimed to develop an indoor positioning system for use in a museum space where conventional GPS didn’t work. Its overall goal was to identify which area or installation a visitor was currently viewing, so that additional digital material such as explanations, videos or interactive experiences could be offered to them in real time. This was intended to enrich the visitor’s engagement without altering the physical environment of the museum or disrupting its architectural character. The system also needed to be low cost, easy to deploy and discreet, because the building was historically protected and could not be modified to accommodate large infrastructure .

Bluetooth beacons formed one of the main components of the positioning system. They were installed throughout the rooms and central atrium of the museum, each associated with a specific region of interest. The beacons continually broadcast Bluetooth Low Energy signals, and the visitor’s smartphone measured the strength of these signals. Since the signal weakens with distance and is also affected by room layout, objects and walls, the pattern of received signal strengths was used to infer location. The researchers placed the beacons carefully to minimise visual impact, often above doorways or high on walls. Capsules were added to some beacons to direct their signals more reliably and reduce interference caused by reflections from surfaces within the building .

A custom Android application collected both the Bluetooth signal data and video footage from the phone camera as visitors walked through the space. The video frames were later processed to help recognise which room or display was being viewed. For each second of recording, the average signal strength from each beacon was taken to form a consistent data sample. If a beacon was not detected in that second, it was assigned a low placeholder value, ensuring the input dimensions remained stable across the dataset. The video frames were separately used to train image-based models. Both data sources were labelled according to the exact region of interest being walked through at that time, creating the training datasets.

The core scientific aim was to compare positioning models based solely on Bluetooth signal strength with models based on visual recognition, and then test whether combining both improved accuracy. The results showed that while Bluetooth alone could locate visitors with reasonable precision, and video alone could also distinguish rooms effectively, the combination of the two consistently performed better, especially in areas where lighting, open space or multipath signal reflections made a single method less reliable. This demonstrated that using multimodal data can produce stronger and more dependable indoor positioning in complex environments where infrastructure placement is limited.

Should I Use the Manufacturer iBeacon SDKs?

Some manufacturers offer SDKs to allow programmatic access to their beacons from iOS and Android.

Most SDKs tend to be poorly implemented/documented, tie your code into using that particular beacon and rarely get updated to use newer mobile platform APIs. They also tend to be very thin abstractions over the Android and iOS Bluetooth APIs.

If you rely on a beacon manufacturer that doesn’t update their SDK, it’s eventually the case that the underlying Android and/or iOS API changes such that your solution becomes non-optimal and, in the worse case, breaks.

Instead, when you can, we recommend you use the iOS and Android Bluetooth APIs directly to make your code independent of the beacon type. In this way you don’t end up depending on intermediate code and this has the benefit that you can more easily change beacon providers.

Alternatively, use an independent 3rd party library such as Radius Network’s iOS SDK or Nordic’s Android Library.

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.