iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
There’s an article at business of business on how Yasmine Mustafa has created a new business Roar For Good that supplies smart panic buttons for hotel and hospitality workers.
This is one of the first applications of Bluetooth Mesh outside of lighting. Workers push the panic button if they need help. A nearby beacon is used to identify their location and a notification is sent to security or the hotel manager.
Beacons allow you to set the transmit power to levels such as -30dBm, -20dBm, -16dBm, -12dBm, -8dBm, -4dBm, 0dBm and +4dBm. The number of actual setting values depends on the beacon. 0dbm is the default power recommended for normal use. Our article on Choosing the Transmitted Power explains these values and how they relate to distance.
We are often asked ‘What are the Estimated Distance/s for Tx Powers?’. This depends on the beacon, the environment and the receiver. An analogy is someone shouting a word. How loud does someone have to shout to be heard a certain distance? It depends on how clear the person shouts, how much noise there is and how well the person listening can hear. With beacons it depends on the beacon (mainly antenna) design, how much radio frequency (RF) noise there is, the degree of RF reflections, the receiving ability of the device (smartphone or gateway) you are using and even the weather.
The only way to determine the relationship between distance and power is experimentally and it will likely change over time as the environment changes.
If you want to try Bluetooth beacon advertising from your iOS or Android smartphone there’s a Flutter plugin called beacon_broadcast:
Simulating a beacon from a smartphone is a great way to get started and explore Bluetooth LE prior to buying dedicated hardware devices. However, it’s not something you should progress to regular use because advertising in this way uses a lot of battery power.
Beacon_broadcast is open source and the source code can be found on GitHub.
Hotel Management has an article mentioning how hotel panic button solutions are being used by Curator Hotel & Resort Collection.
Employees wear a cellular wireless panic button that can be pressed when help is needed. Bluetooth beacons are placed around the hotel that allow the worker to be located.
There are other ways to implement such systems without needing expensive, extra, cellular wireless. For example, it’s possible to piggy back on phones employees are already carrying, use beacons with 2-way radio or have gateways around the hotel to detect location.
James Bayliss, a final year industrial design student at Loughborough University, has designed a smart mobility aid that uses beacons. It’s allows people with dementia to live safely in their own home for longer.
The system, called ‘AIDE’, comprises of a walking stick that works with Bluetooth beacons situated around the home.
It tracks the person’s movement and uses machine learning software to detect behaviours and actions that are out of the ordinary. The system also provides reminders to the person to help re-orient them if they have a confused episode.
There’s a recent paper by researchers at DeustoTech Institute of Technology, Bilbao, Spain and Department of Engineering for Innovation, University of Salento, Lecce, Italy on Behavior Modeling for a Beacon-Based Indoor Location System.
The research compares two different approaches to track a person indoors using Bluetooth LE technology with a smartphone and a smartwatch used as monitoring devices.
The beacons were iB005N supplied by us and it’s the first time we have been referenced in a research paper.
The research is novel in that it uses AI machine learning to attempt location prediction.
The researchers were able to predict the user’s next location with 67% accuracy.
Location prediction has some interesting and useful applications. For example, you might stop a vulnerable person going outside a defined area or in an industrial setting stop a worker going into a dangerous area.
We have a new specialist sensor beacon INGICS iBS03R in stock. It uses a time of flight (TOF) sensor to accurately detect distance to ±25mm over a range 40mm to 3m and 27 degree field of view.
It’s suitable for applications such as waste can, toilet paper, sanitiser, inventory monitoring and industrial automation.
Bluetooth LE development is getting very popular with an increasing number of learning resources. Here are some that have appeared recently:
Erik Hellman, a developer, has two useful articles Bluetooth LE for modern Android Development part1 and part2. He covers Android versions, Bluetooth GATT, PHY, Android Device Manager, pairing and bonding.
Litum have a new article What is Bluetooth Low Energy (BLE)? How does BLE work? that’s higher level and less technical covering Bluetooth LE device discovery, differences to Classic Bluetooth, how positioning works, Bluetooth range , usecases and industries.
Zephyr, the embedded systems OS have also recently updated their Bluetooth documentation.
The University of North Carolina (UNC) recently piloted using beacons to track student-athlete class attendance.
SpotterEDU apps were used to detect beacons to implement automated attendance monitoring. This provided a replacement for previously ad-hoc and inconsistent manual checking.
There’s a recent paper by researchers at the Department of Management Science and Technology, Athens University of Economics and Business on An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons.
The paper starts with an overview of indoor positioning techniques including trilateration, fingerprinting, dead reckoning and AI machine learning. It also provides a synposis of different technologies such as RFID, WiFi and Bluetooth.
The paper explains that while fingerprinting is widely used, it faces limitations when used in dynamically changing environments. Fingerprinting requires ongoing maintenance and updating of the reference fingerprinting map that’s manually intensive and time-consuming. Fingerprinting also requires a large number of beacon reference points to perform accurate locating.
The researchers looked into positioning within a two floor (grocery) retail store. Retail stores are of of the more challenging environments as there are shoppers moving about that can affect indoor localisation
Several indoor positioning techniques were considered including fingerprinting and trilateration. The researchers implemented fingerprinting and compared it to seven established classifiers. The random forest algorithm worked the best and inspired the authors to build an ensemble classification filter with lower absolute mean and root mean squared errors.