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
A problem is that some environment-related factors change over time, such as changes in hardware/furniture, the presence of people and ambient humidity conditions meaning that fingerprinting isn’t a one-off activity.
The researchers conclude that the highest transmission power (+4 dBm) produces the best location accuracy. However, this uses a lot of battery power. Use of the lowest power (−20 dBm) only worsened the accuracy by 11.8%. Similarly, lowering the density of the beacons by around 50%, the error increase was only about 9.2%. Increasing the advertising interval didn’t have a significant impact on the accuracy.
The affect of beacon orientation was assessed and vertical orientation was found to be best. Read our previous article on orientation. As expected, introducing a slight electrical noise during the positioning phase did not significantly affect accuracy.
The paper explores indoor location algorithms and implements a fingerprinting system using RSSI that achieves an average error of 0.43m.
The authors’ ‘fuzzy logic type-2’ system allows for complex environments such as buildings with glass/metal corridors. They comment that fingerprinting requires pre-configuration which is one of the main disadvantages of this method.
The main insight is that along with the expected difference in the RSSI attenuation there is a considerable difference in the BLE signal variation at all transmission power levels with respect to distance. The variation increases and the localisation accuracy decreases from high to low transmission power levels:
Another observation is that outliers in the data tend to affect the localisation accuracy. Applying filters to the data, they achieved a location accuracy of 2.2 meters with a precision of 95%.
One comment we have is that the researchers didn’t try different beacons. As we mentioned in 2016, the RSSI stability also varies across different beacon models.