Support – BeaconZone Blog

What are Beacon OTA Updates?

Beacons are small computers that run software, more specifically firmware. Beacon manufacturers write the firmware that uses Bluetooth software libraries to send out iBeacon, Eddystone and/or sensor data advertising and allow setup via their iOS and Android apps.

When a beacon supports over-the-air (OTA) update, it allows that firmware to be updated without physically connecting to the beacon with wires. A smartphone app, such as the manufacturers’ app or the generic Nordic nRF Toolbox is used to connect to the beacon via Bluetooth and update the firmware.

In practice, manufacturers hardly ever update their firmware so whether a beacon supports OTA update or not isn’t usually an issue.

A further use of OTA is the facilitation of custom firmware when the standard firmware needs to be updated to provide for specially required functionality. This is non-trivial and ideally needs to be performed by the original manufacturer because they have the original source code. We have arranged this for a few customers but it tends to only be financially viable for large orders.

It’s also possible to completely replace the software in some beacons, something we provide via custom solutions and previously used in our social distancing and Bluetooth mesh solutions. In these cases, OTA tends to be too slow for large numbers of beacons so wired programming jigs are sometimes used instead.

What is Bluetooth LINE Service Advertising?

We have just one beacon that can advertise LINE. This post explains LINE advertising with information on the packet format.

LINE Beacons are used alongside the LINE messenger service, which enables users to exchange text, video, and voice messages on both smartphones and personal computers. This service is currently available in Japan, Taiwan, Thailand, and Indonesia. LINE offers developer APIs for both iOS and Android platforms, allowing developers to integrate LINE functionality into their own applications.

The LINE Beacon system works by sending webhook events to a LINE bot whenever a user with the LINE app comes into close range of a registered beacon. This enables developers to create context-aware interactions, tailoring the bot’s behaviour based on the user’s proximity to specific physical locations. In addition, there is a feature known as the beacon banner, which is accessible to corporate users. This allows a promotional banner to appear in the LINE messenger app when the user approaches a LINE Beacon, providing another layer of engagement for location-based services and marketing campaigns.

Unlike iBeacon, LINE Beacon packets have a secure message field to prevent packet tampering and replay attacks. The secure data is 7 bytes long containing a message authentication code, timestamp and battery level. Secure messages are sent to the LINE platform for verification.

Generating LINE advertising — Generating LINE Advertising

LINE recommend LINE beacon packets be sent at a very high rate of every 152ms. In addition, LINE recommend advertising iBeacon (UUID D0D2CE24-9EFC-11E5-82C4-1C6A7A17EF38, Major 0x4C49, Mino 0x4E45) to notify iOS devices that the LINE Beacon device is nearby. This is because an iOS app can only see iBeacons when in background and LINE beacons can’t wake an app.

We observe that the high advertising rate and concurrent iBeacon advertising aren’t battery friendly and the beacon battery isn’t going to last long.

There’s more information on the LINE developer site on using beacons and the LINE packet format.

Solving Indoor-Outdoor Transitions

New research titled Seamless Indoor–Outdoor Localization Through Transition Detection by Jaehyun Yoo presents a system for maintaining accurate and continuous positioning of a person or object as they move between indoor and outdoor environments. Standard GPS technologies struggle in indoor areas due to signal loss, while indoor systems often fail outdoors. Transition zones, such as building entrances, pose particular challenges. This paper addresses those issues by introducing a transition detection algorithm that combines data from WiFi and BLE signals, GPS metrics, and inertial sensors.

The core of the proposed system is a handover strategy that classifies an environment as indoor, transition, or outdoor using a probabilistic model based on signal strength (RSSI), position estimation, and satellite signal quality. The classification process relies on machine learning, specifically a neural network model trained on unlabelled data using an unsupervised approach. The transition zones are especially crucial for switching accurately between positioning engines.

The system comprises three separate engines. The indoor engine uses WiFi and BLE fingerprinting fused with inertial sensor data through a particle filter. The transition engine works exclusively with AI-augmented inertial data to maintain tracking where both WiFi and GNSS are unreliable. The outdoor engine integrates GNSS data with inertial measurements using an extended Kalman Filter, adapting dynamically to account for signal reliability and expected movement.

Experimental tests were conducted using three Samsung smartphones in three office buildings of various sizes and layouts. The new method demonstrated improved positional accuracy and better state classification compared to Google’s Fused Location Provider (FLP) and standard GPS. Results showed the proposed system consistently outperformed the alternatives, particularly in recognising transition zones and reducing localisation errors. However, the method was sensitive to direction errors and GNSS multi-path interference, which could affect the overall accuracy, particularly in densely built environments.

The study concludes that the approach offers significant improvements in seamless localisation using consumer smartphones without requiring labelled data or external GNSS hardware. Future work aims to replace manually tuned parameters with data-driven methods and to further address GNSS multi-path errors by leveraging raw satellite data.

Is it Possible to Continuously Scan for Bluetooth Devices on iOS and Android?

We sometimes get asked if it’s possible to use a smartphone as a gateway to scan for Bluetooth devices. The thinking is usually that workers or users already have devices so why not make use of them?

While it is possible, there are many reasons why you might not want to do this:

On iOS, Apple hide Bluetooth MAC addresses and for some APIs hide the iBeacon ids making unique identification more difficult.
You will find it very difficult to get a continuously scanning app through Apple app store review. You will need strong justifications.
Scanning continuously uses lots of battery power, even when advertising with periodic ‘off’ and ‘on’ periods.
Capabilities of devices vary meaning you will almost certainly get some end user devices where your implementation won’t work. For example, some manufacturers stop long running processes.
On Android there’s a limit of six scans every 30 seconds. Also, it’s necessary to scan in a foreground activity to prevent the operating system from throttling detections. There are hacks to instead run scanning in threads but these aren’t officially supported and so might not be viable in future OS releases. It’s best not to create production apps based on hacks as they can suddenly stop working.
Some users will play with their phones and end up purposely or inadvertently disabling your application.

The best scenarios are those where you can dictate the phone type, it can be mains (PSU) powered and the phone isn’t owned by a user (i.e. it’s just used as a gateway). It’s almost always better to use a dedicated gateway.

FollowMe Bluetooth-based Robot Positioning

The FollowMe project (pdf) explores the feasibility of using Bluetooth and computer vision (CV) technologies to enable a legged robot to autonomously follow a human operator, with control mediated via a smartwatch interface. While the system ultimately relied on CV for effective tracking, considerable effort was invested in developing and assessing the Bluetooth-based approach.

The Bluetooth component of the system was designed to offer an infrastructure-free method of localisation using Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI) analysis. The setup featured a Silicon Labs BG22 Bluetooth antenna array mounted on the robot and a BLE-emitting tag carried by the user (emulated via Thunderboard Kits). By measuring the phase of incoming signals from a Constant Tone Extension (CTE) in Bluetooth packets, the system estimated the direction of the tag relative to the robot. This directional information was combined with signal strength data to estimate the distance to the tag, effectively calculating the user’s position in 3D space.

However, this Bluetooth-based tracking system proved unreliable in practice. The AoA method, though theoretically capable of sub-degree resolution, suffered from high noise levels and poor accuracy in real-world conditions. The resulting positional data often diverged significantly from ground truth, with only about 5% accuracy in controlled trials. These shortcomings were attributed to the use of a single locator antenna, multipath interference, and environmental variability. The project team noted that using multiple receivers or integrating inertial sensors might improve robustness, but time constraints precluded further refinement during this study.

It should be noted that commercial systems that rely on Bluetooth Angle of Arrival (AoA) positioning consistently employ multiple locator antennas to achieve accurate localisation. This multi-antenna configuration enables triangulation of the signal source by capturing AoA data from different spatial perspectives, thereby significantly improving the precision and robustness of position estimates. Each locator provides a unique angular measurement relative to its own position, and when these are combined, the system can more reliably compute the target’s location in two or three dimensions. Single-locator setups, by contrast, are inherently limited because they lack the spatial diversity necessary for resolving ambiguity in signal direction and distance.

What Can Block Beacon Signals?

We often get questions asking what kinds of things can block Bluetooth signals and enquiries about the relative blocking of different materials.

Metal obstructions or metal-based surfaces such as metal-reinforced concrete cause the most blocking followed by other dense building materials such as plaster and concrete. Next comes water that you might not think would be a problem but, as people are made up of 60% water, bodies blocking Bluetooth signals can be a significant factor. Least blocking are glass (but not bulletproof), wood and plastics.

Blocking can be caused by wireless noise as well and physical obstructions. This includes electrical noise from other electrical equipment as well as interference from devices using the same 2.4GHz frequency. WiFi on 2.4GHz causes negligible interference.

In extreme cases, a very large number of Bluetooth devices can cause interference with each other because only one can advertise at a time without there being collisions and hence lost data. The maximum number of Bluetooth devices depends on how long and how often the Bluetooth devices transmit. It also depends on whether devices are just advertising or additionally using GATT connections. Bluetooth also has adaptive frequency hopping that helps reduce packet interference.

We have a deeper analysis of interference in the post on Bluetooth LE on the Factory Floor.

Which Beacons are Compatible with iOS and Android?

We often get asked the question which beacons are compatible with iOS and Android. All beacons, whether iBeacon, Eddystone or sensor beacons can be used with iOS and Android. The compatibility is achieved through the implementation of common Bluetooth standards on these mobile platforms.

However, there are some caveats:

Android only supported Bluetooth LE as of Android 4.3. Older devices can’t see Bluetooth beacons. Over 99% of users are on Android 4.3 or later so most people can see beacons.
Apple iOS doesn’t have background OS support for Eddystone triggering. While iOS apps can scan for, see and act on Eddystone beacons, the iOS operating system won’t create a notification to start up your app when there’s an Eddystone beacon in the vicinity.
Apple can’t see beacon’s or other Bluetooth devices’ MAC address or iBeacon ids due to over zealous privacy concerns. It can see iBeacons but you have to pre-declare, already know, their ids.

Rather than beacons being compatible with iOS/Android, we find that there are more problems with particular Android devices not seeing beacons, when in background, due to some manufacturers killing background services.

Also see Which Beacon’s Are the Most Compatible?

View iBeacons

Optimising the Web Bluetooth API

New research titled Minimising Data Loss in Bluetooth Low Energy (BLE): A Parallel Transmission and Application-Layer Modification Approach by Ahmad Asili investigates how BLE communication via the Web Bluetooth API can be made more reliable in industrial IoT (IIoT) settings.

BLE is widely used due to its low energy consumption, but it suffers from packet loss, limited throughput, and configuration constraints, especially when accessed via browsers using the Web Bluetooth API. The research sets out to explore how software-level solutions can improve BLE reliability without modifying hardware or the protocol stack.

Three experiments were conducted. The first tested whether using multiple BLE modules in parallel could reduce packet loss. Results showed that two parallel modules eliminated packet loss and minimised jitter, while three or four modules introduced diminishing returns or slight instability. The second experiment tested how different data transmission rates (1 Hz, 2 Hz, and 5 Hz) and packet sizes (50, 75, and 100 bytes) affected performance. A 2 Hz rate consistently gave the best results, balancing speed and reliability. The third experiment assessed long-term connection stability over five hours. With system-level improvements such as real-time scheduling and CPU pinning, jitter was reduced and packet reliability improved.

The research concluded that reliable and stable BLE communication using the Web Bluetooth API is achievable with careful application-level design. Key contributions include demonstrating that two BLE modules in parallel and a 2 Hz transmission interval optimise performance, and that software optimisations significantly improve long-term connection quality.

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 in Bluetooth technology 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 Bluetooth-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 Bluetooth Solutions: Where other Bluetooth-based 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.

Currently in Beta!

We’re excited to be in the beta phase of this innovative solution. 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 and potentially participate in our beta programme.

Location in Underground Mines

New research titled Reliable Proximity Sensing for Underground Mining by Johnny Lam explores the challenge of accurately tracking personnel and vehicles in underground mines using digital positioning systems. While vehicle tags use onboard sensors like accelerometers and gyroscopes for precise positioning, personal tags rely on less accurate signal strength readings from fixed reference points. This discrepancy can lead to safety issues, especially when personnel are inside vehicles but their tags report separate positions.

To resolve this, the study investigates a method for personal tags to inherit the vehicle’s position by first determining if they are actually inside the same vehicle. The approach uses Bluetooth Low Energy (BLE) to detect nearby devices, then samples and compares accelerometer data from both the personal and vehicle tags. Using dynamic time warping (DTW) and variance analysis of the motion data, the system classifies whether a personal tag is inside the vehicle.

The system was implemented as an Android application and tested under various conditions. BLE proved effective for detecting proximity, while combining DTW and variance analysis yielded a classification accuracy of about 90%. Power consumption increased by roughly 22% with full functionality.