Governments are increasingly mandating workplace indoor occupancy limits due to the Coronavirus pandemic. This is especially so in education where the risk of reduced social distancing is being mitigated with occupancy limits.
Occupancy is the number of people that are currently inside a building, room or zone. Measuring occupancy manually requires significant effort, additional staff, is error prone and is difficult to achieve, especially when there are multiple entrances and exits.
It’s for this reason, we are seeing organisations starting to use automated approaches. Real time locating systems (RTLS) such as our BeaconRTLS™ use Bluetooth beacons on people and gateways in rooms/zones to track who is where. The resultant data provides for accurate current and historical occupancy.
Once you have a system in place it has lots of other uses:
Locating staff for safety and evacuation
Finding expensive assets shared amongst staff
Providing alerts if things move when they shouldn’t
Detecting when collisions occur between vehicles/racking
Tracing of parts, sub-assemblies and physical orders
Supporting IoT sensing including light, temperature, humidity, water leak, gas
Creating big data for use with AI to provide insights using patterns the data
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.
The paper looks into how to combine both Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP) to provide for an adaptive Bluetooth/Wi-Fi fingerprint positioning system based on Gaussian process regression (GPR).
The adapative feature is particularly useful because fingerprint acquisition requires a great deal of effort and requires subsequent update and maintenance.This new method provides a better positioning than Bluetooth and Wi-Fi positioning alone but at the cost of extra computation.
There’s recent research into using iBeacons with intelligent displaying and alerting systems (SICIAD) typically found in public buildings and offices. The paper An Intelligent Low-Power Displaying System with Integrated Emergency Alerting Capability by Marius Vochin, Alexandru Vulpe, Laurentiu Boicescu, Serban Georgica Obreja and George Suciu of the University of Bucharest shows how beacons can be used to determine indoor position of mobile terminals or signalling points of interest.
An Android app uses the beacons to detect location and sends it to the SICIAD system. The researchers concluded that:
“By using an appropriate number of beacons and optimal positions, a relatively precise indoor localization can be obtained with iBeacon technology”
They have observed that the stability of the received Bluetooth signal strength RSSI depends on which Channel 37, 38 or 39 the signal is being received on. This is because the channels slightly overlap the WiFi channels and there can be other Bluetooth devices also using the same channels.
The method analyses the channels over time and chooses those it thinks has least interference and most stable RSSI. This reduces the positioning error by 0.2m, to 2.2m, at a distance of 3.6m.
Although the implementation is similar to SensorMesh™ and BeaconRTLS™ used together, their solution uses a proprietary mesh implementation and a proprietary data protocol. Consequently, their implementation suffers longer response time when used over longer physical distances. Their maximum inter-hop distance of 8 to 10 m also isn’t good due to non-optimal devices and non-optimal device positioning.