There’s new research by the Institute of Information Science and Technologies, Pisa, Italy on Detecting Proximity with Bluetooth Low Energy Beacons for Cultural Heritage. The paper starts by describing alternative technologies including Ultra-wideband (UWB), Near Field Communication (NFC) and vision.
The RE.S.I.STO project allows media on the medieval town of Pisa to be accessible via smartphones and tablets. The system is implemented using the React Native Javascript Framework to allow cross-platform aps to be created on iOS and Android.
Beacons are attached to exhibits and the paper compares two proximity detection algorithms, a ‘Distance-based Proximity Technique’ and a ‘Threshold-based Proximity Technique’. The paper describes stress, stability and calibration testing of the system.
RSSI time series of 5 tags
The researchers found a strong variation of RSSI value for different tags that they say is caused by the varying channel (frequency) used by Bluetooth LE as well as environmental issues such as obstacles, fading and signal reflections.
The system was able to successfully detect the correct artwork with an accuracy up 95% using the Distance-based Proximity Technique.
We previously mentioned how cost, battery life and second sourcing are the main advantages of Bluetooth over Ultra-Wideband (UWB). An additional, rarely mentioned, advantage is scalability.
Servers that process Bluetooth or Ultra-Wideband support a particular maximum throughout. The rate at which updates reach systems depends on the number of assets, how often they report and the area covered (number of gateways/locators). Each update needs to be processed and compared with very recent updates from other gateways/locators to determine an asset’s position.
For Bluetooth, updates tend to be of the order of 2 to 10 seconds but in some scenarios can be 30 seconds or more for stock checking where assets rarely move. Motion triggered beacons can be used to provide variable update periods depending on an asset’s movement patterns. This allows Bluetooth to support high 10s of thousands of assets without overloading the server.
For Ultra-Wideband, refresh rates tend to be of the order of hundreds of milliseconds (ms) thus stressing the system with more updates/sec. This is why most Ultra-Wideband systems support of the order of single digit thousands of assets and/or smaller areas. More frequent advertising is also the reason why the tags use a lot of battery power.
How does all this change with the new Bluetooth 5.1 direction finding standard? The standard was published in January 2019 but solutions have been slow to come to the market. The products that have so far appeared all have shortcomings that mean we can’t yet recommend them to our customers. Aside from this, in evaluating these products we are seeing compromises compared to traditional Bluetooth locating using received signal strength (RSSI).
Bluetooth 5.1 direction finding needs more complex hardware that, at least in current implementations, are reporting much more often. The server has to do complex processing to convert phase differences to angles and angles to positions thus supporting fewer updates/sec. Bluetooth direction finding is looking more like UWB in that cost, scalability and battery life are sacrificed for increased accuracy. Direction finding locators are currently x6 to x10 more costly than existing Bluetooth/WiFi gateways. Beacon battery life is reduced due to the more frequent and longer advertising. We are seeing Bluetooth 5.1 direction finding being somewhere between traditional Bluetooth RSSI-based locating and Ultra-Wideband in terms of flexibility vs accuracy.
Despite these intrinsic compromises, Bluetooth direction finding is set to provide strong competition to UWB for high accuracy applications. We are already seeing UWB providers seeking to diversify into Bluetooth to provide lower cost, longer battery life and greater scalability.
The paper starts by describing trilateration and the author voices the opinion that another method, fingerprinting, requires a lot of effort and isn’t feasible for practical implementation.
The new method makes use of the fact that accuracy is usually good when the received signal strength (RSSI) is -70 dBm or better. The use of more beacons and basing calculations on ‘reliable circles’ of higher signal strength, when available, provides for more accuracy.
The data is also filtered using a Kalman filter to reduce signal noise by about 37%.
Due to the pandemic, hospitals and care facilities have been experiencing greater patient numbers leading to pressures to accelerate digital transformation to increase efficiency. At BeaconZone, these are the main reasons customers have been using locating systems:
To save time searching for equipment, particularly highly mobile equipment such as wheelchairs
To monitor the location and temperature of medicines
To monitor the location of hospital porters
To track the location of vulnerable patients
To audit the visiting of care givers to patients
However, there are many more areas suitable for increasing efficiency and safety:
Tracking expensive assets such as beds and medical devices
Tracking rental/borrowed equipment to ensure they are returned on time to avoid unintended costs
Staff distress SOS for increased safety
Hygiene management, for example, on hand washing stations
Inventory counts and stock checks
Analysis of workflows to detect choke points and streamline processes
Production of key metrics such as time being spent with patients, patient throughput and wait times
Time saved improving the above activities leads to more time being spent with patients and hence potentially saved lives.
Here are some considerations if you are comparing solutions:
Tag costs – Prefer commodity rather than proprietary hardware to reduce costs and allow 2nd sourcing to reduce future risk
Real time – Prefer systems that detect continuously over those that rely on error-prone manual scanning
Scalable – Prefer software systems that will scale financially, particularly in large hospitals
Ongoing costs – Prefer systems that have known future system costs – ideally with a one-off licence rather than varying subscription.
One final tip. It’s our experience that healthcare providers under-estimate the human element in attempting to implement new systems. There are often internal problems as to who will be responsible for a) purchasing, b) installing and c) running new systems. Work these out and agree up-front before embarking on these transformative changes so as to prevent your project becoming blocked.
The signal level and interference vary change depending on the position of the sender and receiver. They also vary depending on the surrounding environment. The signal level (RSS) is affected by reflection, shielding, and diffraction by surrounding objects, walls and the ground. Instead, testing requires known signal level and interference values.
The paper describes a software-implemented BLE controller, BluMoon, that calculates the received signal strength for each frame and imitates radio interference. The emulator replaces the controller with the HCI as the boundary.
BluMoon performs BLE communication emulation frame by frame and is implemented on Linux using the BlueZ Bluetooth stack.
GoPorter is an ibeacon-based solution for hospitals, shopping malls and hotels that provides automatic job allocation, real time indoor tracking, business analytics and reporting.
Systems such as GoPorter allow facilities management to learn about service request trends thus allowing the deployment of the optimum number of staff to improve efficiency.
The researchers explain how standard beacon advertising works and documents the existing iBeacon and Eddystone protocols.
New protocols, LP4S-6 (for resource-constraint beacons), LP4S-X (for more powerful beacons) and LP4S-J (for beacons able to run complex firmware) are proposed that can be used to allow IoT telemetry systems to discover new nodes and to describe and auto-register the sensors and actuators connected to a beacon.
The paper describes the resultant JSON, shows how a new protocol can be added to an Eddystone beacon and proves how the new latency and power consumption remain low.
Note that updating the firmware of a beacon is non-trivial because it requires the implementation of what’s already on the beacon without access to the original source code.
We recently came across Time Guard, an app that logs working time, holiday and flexi time and provides notifications when you go over a set daily and maximum allowed working time.
The app can use iBeacons to automatically detect when you enter or leave locations. Data can be viewed in Excel (.csv) or as monthly time sheets.
The Fourth Industrial Revolution, also known as 4IR and Industry 4.0, improves manufacturing through the use of technology. The end-aims are to significantly improve productivity, reduce production delays and, for example, avoid penalties or future lost orders due to delayed work.
A key part of The Fourth Industrial Revolution is asset tracking that provides faster and more accurate stock control, item picking, job tracking, capacity measurement, demand analysis and product protection through sensing and automatic auditing.
It’s important that asset tracking is continuous because merely scanning things in/out using barcodes is open to human error and location is otherwise only as good as the last scan. Historical data is also important because it identifies blockages allowing processes to be refined.
When evaluating asset tracking systems consider:
Scalability and Performance – How many things do you need to track today and into the future?
Flexibility – Many of our customers initially buy an RTLS for one urgent purpose but later end up use the system system for additional needs.
Security – Where is your data stored and where does it go?
Look for a stand-alone solution rather than SAAS for greater performance, flexibility and longevity. While SAAS based systems can be a quick way into RTLS, they soon become limiting because you are sharing a platform with other customers. SAAS platforms usually don’t scale well technically and financially and don’t have efficient, direct access to the data for efficient ad-hoc reporting. They also pose potential security and reliability risks as you don’t own your data. The ultimate limitation comes when the SAAS provider, usually a startup, eventually increases costs, get’s bought out by its largest customer or goes out of business.
