We previously mentioned Waze Beacons in Tunnels in New York City. Since then, Waze beacons have been installed in further cities such as Chicago, Paris, Rio, Brussels, Florence and Oslo. The latest installations are by Transurban who manage tunnels in Australia where they have installed over 930 beacons in 18Km of tunnels.
Waze beacons allow uninterrupted location services underground ensuring drivers never miss an in-tunnel exit. They provide navigation underground where GPS doesn’t work.
The beacons advertise Eddystone. The Waze app sees the beacons and uses the known beacon locations rather than GPS. Google is also a partner which allows Google Maps to also see Waze beacons when driving in tunnels.
Fielddrive provides machines to manage event visitor flows, providing fast checkin. They also supply the BEACONEX system where wearable beacons track the attendee journey throughout an event allowing show organisers to collect and analyse this data and learn about different aspects of the event.
The received signal strength (RSSI) of beacons is often used to infer location. However, the RSSI is subject to reflection and blocking from walls, people and other obstacles causing the derived locations from the raw data to be ‘jumpy’. There are many ways to process the raw data, such as Hidden Markov Models, k-nearest neighbors and Deep Neural Networks (DNN) to obtain smoother trajectories.
The researchers use movement constraints and sliding-window aggregation to extract invalid trajectories and provide real-time semantic trajectories.
The paper shows the proposed movement constraint-based approach extracts valid trajectories that are comparable to the unconstrained and non-machine language approaches. This new approach is particularly suited to dynamic indoor environments where the reflection and blocking changes over time.
The results show fingerprinting is better than distance-based schemes in industrial environments due to the presence of large moving metal objects that shadow and reflect wireless signals. The three methods were found to provide similar localisation accuracy. The authors say the machine learning method is best due to less complexity and better adaptability. The machine learning method does not need regular calibration as is the case with fingerprinting.
Bluetooth beacons are increasingly being used in the aviation industry to track pallets, unit load devices (ULDs) and audit temperature, humidity and shock levels.
Cargo Airports & Airline Service magazine has an article on the Bluetooth Revolution where it mentions ULD provider Unilode’s use of Bluetooth tags. Unilode is equipping its 125,000 ULDs with Bluetooth readers. This will take over two years but 80% should be fitted out within 18 months.
The most significant development recently in ULDs is the development of Bluetooth Low Energy tracking devices.
The article mentions how Unilode has been exploring the use of RFID over last 25-30 years. It says Bluetooth provides the solution to RFIDs limits of range, infrastructure cost and interference with aircraft systems. Bluetooth additionally allows monitoring of ambient shipment conditions, temperature sensitive cargo and shock sensitive cargo.
The key benefit of Bluetooth is knowing where units are, all the time, rather than relying on scanned updates. It provides for better utilisation of assets. This makes transport of freight easier, smoother and more efficient.
Real-time monitoring of assets allows the client to immediately know when assets are behind schedule, being routed inappropriately, or in poor conditions.
Bluetooth not only provides a scaleable and affordable way of tracking pallets and unit load devices but can also provide for tracking the status of smaller critical packages such as pharma and and cosmetics goods.
Here at BeaconZone, we have seen beacons used more for airline temperature sensing rather than tracking. For example, iB003N-SHT beacons are used by Qatar Airways to monitor the temperature of pre-flight cargo holding areas.
FIND is an open source indoor locating system for home automation, indoor local positioning and passive tracking. It uses your smartphone or laptop to pinpoint your position in your home or office with a location precision of below 10 sq ft.
FIND uses scanning of WiFi and Bluetooth:
FIND compiles these different signals can be compiled into a fingerprint which can be used to uniquely classify the current location of that device