iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
The quick answer is no.
The longer answer is that beacons don’t know if anyone is near because they are not doing the listening. They just send out information. It’s possible to have a smartphone app see the beacon and the phone make a sound.
We also have special tracker type beacons to which an app can connect rather than just detect. Once connected, the app can cause the beacon to ring. However, only one app user can connect at a time.
If you are an owner or manager considering implementing a RTLS you might need to justify the return on investment (ROI). In some cases this is easy but in others a quantitative assessment of ROI can be tricky.
The simpler usecases where RTLS is used to automate manually finding items can be easily assessed. For example, workers might be spending a lot of time finding the right pallet in a warehouse or a nurse might be spending too long looking for a shared piece of expensive equipment. Not knowing where things are is increasingly becoming unacceptable for businesses. Times and salaries can be measured, totalled and estimated RIOs calculated to prove ROI.
However, say for example, an RTLS is used to monitor hospital medicines to ensure they in refrigerators and don’t exceed a measured temperature. What are the costs of not doing this? Apart from the cost of the medicines themselves how do you assess the cost of someone dying because the medicines weren’t kept cold? Still in the hospital, how do you assess the gain in being able to find wheelchairs in a hospital? How do you put a price on customer satisfaction?
Things can also get more complicated when, as it usually the case, a RTLS system starts being used for more than one purpose. For example, a recent education client purchased a system for tracking room occupancy but subsequently extended it for lone worker SOS. It’s often the case that just initial usecase justifies the initial investment and follow-on uses are a bonus.
Follow on benefits usually come through reporting and subsequent process improvement. Questions typically revolve around ‘Where has my asset been?’ or ‘What’s happened at particular location?’. The answers, in the form of data, provide insights that drive improvements in processes that can’t always be easily measured or quantified.
Focussing on ROI on its own can be misleading and it’s instead necessary to take a wider view of the qualitative benefits and opportunities.
We sometimes get asked if it’s possible to use a smartphone as a gateway to scan for Bluetooth devices. The thinking is usually that workers or users already have devices so why not make use of them?
While it is possible, there are many reasons why you might not want to do this:
The best scenarios are those where you can dictate the phone type, it can be mains (PSU) powered and the phone isn’t owned by a user (i.e. it’s just used as a gateway). It’s almost always better to use a dedicated gateway.
There’s recent research on Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modelling, and Optimization.
The aim of the research was to provide suggestions to a museum’s curators to better manage visitors flows to increase visitor comfort and improve safety. The museum for the case study was Galleria Borghese museum in Rome, Italy that has no obligatory exhibition path and has frequent congestion in some rooms such that those containing Caravaggio’s paintings.
Beacons set to advertise iBeacon at +4dB power were carried by visitors. RaspberryPi 3B+ (RPi) were used in rooms to detect beacons. Data from the RPi was stored in a SQL database. The project captured over a million records for 900 visitors’ trajectories during 13 2 hour long visits.
The researchers used Lagrangian field measurements and statistical analyses to analyse the data. A sliding window-based statistical method and a MLP neural network were compared.
It was possible to accurately reconstruct visitor trajectories and analyse visitors’ paths to get behavioural insights.
The system was suitable for the museum being economically viable and accepted by visitors. An issue was Bluetooth signal noise that was mitigated using data processing. The sliding window approach was better at measuring room transitions while the machine learning approach performed better at estimating the time spent in rooms.
The researchers identified issues with the museum design and suggested rearrangement of the artworks and implementing of a new ticketing strategy to let 100 people enter every 30 minutes while eliminating a 2 hour time limit.
One mistake some projects make is to choose physically attractive beacons. Some manufacturers make their beacons look attractive to try to secure more sales. However, in use in some scenarios, the beacons can become attractive to thieves or children and become lost.
We once had a train transport customer ask “What’s your most unattractive beacon ?”
Brightly coloured beacons can invite thieves
Small black boxes remain anonymous
There’s new research by ITMO University, Russia on the Implementation of Indoor Positioning Methods: Virtual Hospital Case. The paper describes how positioning can be used to discover typical pathways, queues and bottlenecks in healthcare scenarios. The researchers implemented and compared two ways to mitigate noise in Bluetooth beacon RSSI data.
The probabilistic and neural network methods both use past recorded data to compare with new data. This is known as fingerprinting. The neural network method is less complex when there’s need to scale to locating many objects. The researchers tested the methods at the outpatient department of the cardio medical unit of Almazov National Medical Research Centre.
Comparison of the methods showed they give approximately the same error of between 0.96m and 2.11m. However, the neural network-based approach significantly increased performance.
There are many scenarios that require accurate tracking of assets and people. Logistics can ensure efficient use of equipment and improve workflows. Manufacturing can locate valuable plant tools, parts and sub-assemblies, improve safety and enable efficient asset allocation. Healthcare can track high value equipment, monitor the location of medicines, save time searching for equipment and monitor vulnerable patients. Facilities can track valuable assets, monitor lone workers, check occupancy levels and automatically locate people or students for safety and evacuation.
New AoA direction finding brings sub-metre tracking to Bluetooth where the main alternative was previously expensive, proprietary ultra-wide band (UWB). AoA direction finding uses receivers, called locators, that have multiple antenna. The differences in phase of the signal arriving from a beacon to each antenna are used to determine the direction.
One locator can be used to determine the location or multiple locators can be used to triangulate a more accurate beacon position.
You can’t use just any beacon. It needs to send a Constant Tone Extension (CTE) for a long enough time to enable the receiver to switch between all the antennas.
Martin Woolley’s excellent Bluetooth Direction Finding Technical Overview provides a deeper explanation of the theory.
The calculation of data from the antennas to angles is called radiogoniometry. This can be performed by the the same microcontroller hardware that’s receiving the radio data, by a gateway or by a separate location engine on a local server or in the cloud. The problem with using the same microcontroller is that it is slow and doesn’t scale well to larger numbers of beacons. Also, it doesn’t know about other locators and so can’t do triangulation when multiple locators see a beacon.
There are many ways to implement the location engine using different radiogoniometry algorithms of different accuracy and computational complexity. The location engine should also filter the incoming data to mitigate the affects of multi-path reception, polarization, signal spread delays, jitter, and noise. It also needs to be performant, ideally using compiled rather than interpreted code, to support the maximum throughput and hence the maximum number of beacons. It should also also provide a streaming rather than polling API to pass data onto system and applications such as real time locating systems (RTLS).
Read about PrecisionRTLS™
The new wearable, rechargeable Minew B10 beacon is now available.
This beacon works like a standard Minew beacon advertising up to 6 channels that can be iBeacon, Eddystone UID, Eddystone URL, Eddystone TLM, acceleration and device info. The button can be set to specific advertising for one, two of three presses. There’s a flashing led and vibration when pressed. There’s also a 6-axis accelerometer that can be used to analyse movement or for motion triggered broadcast.
A full charge lasts up 60 days per charge depending on settings.
We have the new Minew P1 Plus in stock. It’s a sensor beacon designed for rough environments and is IP68 waterproof, IK09 shockproof and has a wider than normal temperature rating due to use of the included industrial ER14250H lithium battery.
This beacon has temperature and accelerometer sensors. It’s turned on and off via a magnetic switch. As with other Minew beacons it advertises up to 6 channels that can be iBeacon, Eddystone UID, Eddystone URL, Eddystone TLM and device info.
Small beacons are sometimes needed so that they remain unobtrusive or need to be embedded into larger devices. The smallest, cased, beacons we supply are:
The compromise with small beacons is that they have CR2032 batteries that don’t last as long as larger battery beacons. If the beacons won’t be moving and you have access to USB power, consider using USB beacons that are also small.
