Author: Support

Using Beacons to Improve Location of Mobile Robots

There’s new research from King Mongkut’s Institute of Technology Ladkrabang, Thailand on Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line (pdf).

Mobile robots are broadly divided into automated guided vehicles (AGVs) and autonomous intelligent vehicles (AIVs). AGVs are confined to predetermined paths while AIVs have the flexibility to move in any direction without any infrastructural alterations. Factories often face challenges when it comes to synchronising mobile robots with target machinery. The paper presents a solution to reduce errors in robot localisation and improve parking accuracy.

Adaptive Monte Carlo Localisation (AMCL), a probability-based localisation system which relies on LiDAR and odometry data often misjudges robot positions in environments where the factory production line and room shapes are alike. To mitigate this, a novel landmark-based localisation strategy using iBeacon, a Bluetooth Low Energy (BLE) device, is proposed. This approach aims to provide more accurate localisation of mobile robots, addressing the shortcomings of the AMCL system.

View iBeacons

Unsolicited Messages Can’t be Sent to iOS and Android

This is one of most popular enquiries so we have created a new blog post explaining the situation. Contrary to what some may believe, it’s not possible to send unsolicited messages from beacons to iOS and Android. The problem is that this used to be possible and there are now many web sites still promoting old information.

In the past, there was a way for beacons to broadcast a URL using a protocol known as Google’s Eddystone-URL. This protocol allowed a beacon to transmit a web address and a smartphone or web browser with the ‘Nearby’ feature could detect this broadcast without needing a specific application installed.

However, from December 2018, Google announced that it would discontinue the ‘Nearby’ feature due to a significant increase in irrelevant and spammy notifications that were leading to a poor user experience. This change meant that the Eddystone-URL, which was a potential avenue for unsolicited messages, could no longer be used in this way.

In the wake of Google’s decision, the beacon messaging landscape has changed. Beacons can no longer send unsolicited messages via the Eddystone-URL protocol and all notifications now require an app installed on the device that can listen for the beacons.

While this might seem like a limitation, it provides a level of protection for users, ensuring that they’re only receiving notifications that are relevant and wanted.

iBeacon-enabled Interactive Audio Walk

Lydspor is an immersive, interactive audio walk taking visitors through the history of Helsingør in Denmark. This project makes use of iBeacon technology, allowing it to precisely locate you as you meander down Hestemøllestræde. It is not just an auditory experience but a site-specific sensory journey, produced after one and a half years of intensive research.


The Lydspor team explored soma design methods, focusing on affective interaction design and the concept of bodies as interconnected and multisensory, transcending just human interaction. This exploration and the resultant understanding were then integrated into the design process to create a unique combination of somatic and affective design, aimed at providing site-specific sonic augmentations that vividly communicate the history of the space.

Scattered along Hestemøllestræde are beacons, each telling a distinct historical tale. The accompanying app has been designed to provide a seamless auditory experience that requires minimal device interaction, further enhancing the immersive nature of the walk.

There’s also a research paper with videos.

Using Beacons To Detect Human Movement

There’s an innovative use of beacons mentioned in the research paper on Developing a Human Motion Detector using Bluetooth. Beacons and its Applications (PDF).

Most motion sensing applications usually place a sensor beacon on the things that will move. The accelerometer in the beacon reports movement. The research paper describes an alternative method of detecting movement of a person, an elderly person in this case, based on the change in blocking of the beacon signal over time. This has the advantage that the beacon doesn’t need to be worn. Also, it doesn’t have to be a accelerometer beacon as any beacon can be used.

The problem with using the strength of the beacon signal (RSSI), is that it varies over time even when there’s no change of blocking in the room. This is due to radio frequency (RF) noise and reflection. The authors of the paper looked into smoothing of the data to filter out such variance in the data:

The report concludes that when averaging over three or more RSSI values, it’s possible to minimise the RF variance and reliably detect the variance caused by human movement in the room.

Another, more reliable, way of detecting movement is to use a beacon with built-in PIR such as the iBS02PIR, M52-PIR, IX32 or MSP01.

Small, Inexpensive Gateway

Every now and then, we come across a product that’s a bit different. In this case it’s a very small and reasonably priced gateway, the MG3.

It is designed to gather advertising data from iBeacon, Eddystone and other devices. It sends this data to your server in JSON format using either HTTP(S) or MQTT protocols. The device connected to a 2.4GHz WiFi. To indicate its status, there is an RGB LED integrated into its design.

While the marketing materials mention detecting Minew beacons, it can detect any kind of beacon and, more generally, any Bluetooth device that is advertising. It has the capability to process data from up to 70 devices per second. Although its optimal range is 70 meters in an open space, this range is dependent on the power of the beacons being detected.

The device conveniently uses a standard USB connection for power. It consumes approximately 340mA of power, which decreases to 290mA if the LEDs are turned off.

To change the device settings you use a smartphone app. Please note that currently, the app is only available for Android devices. The settings you can configure include the method of data upload (HTTP(S) or MQTT), server URL, upload interval, RSSI filter, MAC address filter (using Regular Expression), raw Bluetooth filter (using Regular Expression), and time zone settings.

View all gateways

Using Bluetooth Metadata to Infer Social Context

Researchers from Idiap Research Institute and EPFL, Switzerland have been looking into the use of smartphone data, include Bluetooth metadata to try to infer social context, for example whether someone is alone or not.

The paper Understanding Social Context from Smartphone Sensing: Generalization Across Countries and Daily Life Moments (pdf) attempts to better understand human behaviour and mental well-being. The paper focuses on the use of passive smartphone sensors, including Bluetooth, to track the social context of individuals over time. In the past, this field of research has been limited by the fact that most studies have only been conducted in one or two countries and often focused on specific contexts such as eating or drinking.

This paper aims to overcome these limitations by using a new, extensive and multimodal smartphone sensing dataset that includes over 216,000 self-reports from more than 580 participants across five different countries – Mongolia, Italy, Denmark, the UK and Paraguay. The goal is to explore the feasibility of using sensor data to infer whether a person is alone or not and to examine how behavioural and country-level diversity influences this inference.

The sensor data comes from 34 different sensors, divided into continuous and interaction sensing modalities. Continuous sensing includes types of activity, step count, Bluetooth, WiFi, location, cellular, and proximity data, while interaction sensing involves app usage, touch events, screen on/off episodes, and notifications. In terms of Bluetooth, the study used both normal and low-energy Bluetooth capturing data on the number of connected devices and received signal strength indicators (RSSI).

The study’s key findings suggest that sensor features can be used to infer the social context. The research also found that models partially personalised to multi-country and country-specific data achieved similar accuracy levels, typically ranging from 80% to 90%. However, the models did not generalise well to unseen countries regardless of geographic similarity.

View sensor beacons

Beacons Enable Ambient Intelligence

Ambient Intelligence (AmI) is a digital environment that subtly reacts, adapts, and responds to the presence of humans. This implies a setting that can foresee users’ requirements, recognising changes in context and providing services that are anticipatory, adaptive, and personalised. It seamlessly integrates into everyday activities and objects, employing a range of advanced technologies including Artificial Intelligence (AI), the Internet of Things (IoT), ubiquitous computing and context-aware computing.

Sensor beacons play a pivotal role in achieving ambient intelligence. Equipped with technologies such as Bluetooth Low Energy (BLE), sensor beacons gather environmental data and pinpoint the location or presence of objects or people. For instance, in a smart home environment, sensor beacons might identify when a person enters or leaves a room, and cause lighting to be adjusted automatically. In a retail setting, they can supply customers with personalised advertisements or detailed product information when they approach a specific product.

Ambient intelligence mixes several key technological and conceptual components, including Industry 4.0, the Physical Web, IoT and the smart home.

Industry 4.0 marks the fourth industrial revolution, characterised by heightened digitisation and interconnectivity of products, business models, and value chains. It incorporates elements like IoT, cloud computing, and cyber-physical systems, and ambient intelligence is an integral part of this transformation, facilitating intelligent, adaptive interactions within industrial settings.

The Physical Web concept involves the integration of interconnected smart objects into the World Wide Web. Objects equipped with sensor beacons can be viewed as part of the Physical Web, generating real-world interactions specific to certain locations via the internet. Moreover, ambient intelligence aligns closely with the broader concept of the Internet of Things (IoT), a network of physical devices embedded with sensors and software to connect, communicate and exchange data over the internet. Here, devices not only communicate and connect but also adapt and anticipate user needs.

The smart home concept involves leveraging IoT, AI, and other advanced technologies to automate numerous features, from lighting and temperature to security. Ambient intelligence, in this context, doesn’t just automate homes; it makes them sensitive and responsive to the inhabitants’ requirements and contexts. This makes ambient intelligence a pivotal part of smart homes, contributing to their adaptive, intelligent functioning.

View sensor beacons

Nordic Wireless Quarter Magazine

Nordic Semiconductor, the manufacturer of the System on a Chip (SoC) in many beacons, has published the latest online issue of Wireless Quarter Magazine. It showcases the many uses of Nordic SoCs.


The latest issue of the magazine highlights the use of Nordic SoCs in the following Bluetooth solutions:

  • The T1 Tomahawk smart tape measure
  • A beacon tag that enables a wireless manifest for helicopter crews
  • LocoTrack pallet tracking beacons
  • A pet tracker beacon that uses machine learning to detect animal health problems

The magazine also announces the new nRF54 Series of SoCs that have higher performance processing and much more on-board memory. We don’t expect these to end up in beacons because such performance isn’t required for beacons. Instead they will make their way into solutions such as Bluetooth gateways.

There’s an in-depth article on Retail that shows how wireless tech is improving the retail experience, maximising profits for the retailer and delivering value added convenience for customers. There’s mention of Stratosfy’s Tempgenie solution that uses temperature sensor beacons and a Bluetooth LE to Wi-Fi gateway to measure and alert on the ambient and surface temperature of front- and back-of-house
equipment.

Finally, there’s a useful article ‘Planet Bluetooth’ that charts the history of Bluetooth’s spread and evolution into areas and applications that were once unimagined.

Reverse Engineering iBeacon and Eddystone Bluetooth GATT Services

For some of our beacons such the manufacturers haven’t documented their Bluetooth Service Characteristics. This means that while they are ok for scanning/proximity type applications, you can’t write your own app to, for example, change programmatically the UUID, major and minor, transmit power, advertising period and must rely on the manufacturer’s configuration app. While this of no consequence for the majority of uses that set and forget settings, more ambitious scenarios might want directly access the Bluetooth GATT services to change settings.

Uri Shaked has a great article on Medium on how to Reverse Engineer a Bluetooth Lightbulb. His method uses the developer logging in Android 4.4 and later to allow inspection of the Bluetooth packets and hence the Bluetooth Services and Characteristics that are being used. This method can equally be used with iBeacon and Eddystone beacons to reverse engineer the Bluetooth GATT information.

Another method is to use a Bluetooth sniffer. This listens in on the Bluetooth communication between two devices. One way of doing this is with Nordic Semiconductor’s Sniffer software on a dongle. There’s a tutorial on JimmyIoT.

It’s usually ill-advised to reverse engineer interfaces to discover undocumented features because the manufacturer can change the implementation thus breaking your solution. However, it’s very rare that firmware is ever updated in beacons and when it is, it’s usually only to fix bugs rather than change the implementation.

Processing on Bluetooth Device or Smartphone?

There’s often a dilemma when creating Bluetooth systems whether to place the processing on the smartphone or on the Bluetooth device.

The efficient and accurate prediction of an individual’s heart rate using wearable devices is crucial for various personal care applications. A new study Energy-efficient Wearable-to-Mobile Offload of Machine Learning Inference for Photoplethysmogram-based Heart-Rate Estimation (pdf) from the Universita di Bologna, Italy, looks into the trade-offs between carrying out heart rate tracking on the device itself or delegating the work to a mobile device.

The research introduces CHRIS, an inference system that uses the interconnectedness between a smartwatch and a smartphone. This system assesses the balance between energy consumption and heart rate tracking error. Depending on the connection status, a user-specified error, energy constraints and an estimate of the input difficulty, CHRIS employs two heart rate prediction algorithms. These are executed on either the smartwatch or the phone.

CHRIS showed the potential to achieve up to 2.03 times energy reduction on the smartwatch by deferring processing off the smartwatch, without a reduction on the tracking accuracy.