Beacon Signal Stability Observations

As previously mentioned, we perform signal strength and stability tests across beacons. The data feeds into our consultancy work. Here are some high level observations.

The following graph shows the standard deviation of the RSSI @ 1m, for some of our beacons, measured over a 60 second time period:


Smaller bars are better and represent beacons
whose RSSI varied the least over time.

We found that beacons belonged to one or two groups. Firstly those with very stable RSSI and secondly those with an RSSI that had a standard deviation between about 4 and 6 dBm.

Signal stability is more important when you are using the RSSI to infer distance, either directly from the RSSI itself or indirectly via, for example, the iOS immediate, near and far indicators. RSSI varying without a change of distance might cause more spurious triggering. However, you should keep in mind that environmental factors can often cause variation much larger than the 4 to 6 dBm found in this test. Moving obstacles, for example people, will cause significant variation in RSSI.

Bluetooth LE advertising moves pseudo-randomly between radio channels. The channels use different radio frequencies that, in turn, results in fading of the signal at different distances. We experienced and mitigated similar behaviour in our BluetoothLocationEngine™. Different radio frequencies experience different constructive and destructive interference at different physical locations. Beacons that move more between channels can cause more rapidly varying received signal strength (RSSI).

Finding the Nearest Beacon

There’s new research from Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia on Improved Bluetooth Low Energy Sensor Detection for Indoor Localization Services.

While there has been lots of research into server-side processing to improve location accuracy, this research instead looks into improving accuracy locally, in terms of finding the nearest beacon. This kind of processing is often needed where smartphone apps provide users with contextual information based on their location, for example, in museums.

It’s not possible to use the raw received signal strength (RSSI) because it changes frequently due to changes in blocking and reflection in a room. Any errors in determining the correct transmitter can cause errors in displaying relevant information which, in turn, leads to a poor visitor user experience.

The study involved use of iBeacons detected by Android smartphones, both in a controlled room with three obstacles and a real-world setting Expo Museum.

The proposed algorithm stabilised the RSSI by considering previous measurements to filter out sudden fluctuation of the RSSI signal or the rapid movement of the mobile device. The smartphone’s accelerometer was also used dynamically change the scan interval based on the user’s movement.

In the controlled room, the proposed algorithm had a 14.29% better success rate than a standard algorithm using the raw RSSI values. It performed particularly (20%) better in spaces having medium or high density of physical obstacles. It also performed better in the real-world Expo environment with a success rate of 95% compared to 87% with a standard algorithm.

Detecting Proximity Using Bluetooth Beacons in Museums

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.

Read about Determining Location Using Bluetooth Beacons

What Can Block Beacon Signals?

We often get questions asking what kinds of things can block Bluetooth signals and enquiries about the relative blocking of different materials.

Metal obstructions or metal-based surfaces such as metal-reinforced concrete cause the most blocking followed by other dense building materials such as plaster and concrete. Next comes water that you might not think would be a problem but, as people are made up of 60% water, bodies blocking Bluetooth signals can be a significant factor. Least blocking are glass (but not bulletproof), wood and plastics.

Blocking can be caused by wireless noise as well and physical obstructions. This includes electrical noise from other electrical equipment as well as interference from devices using the same 2.4GHz frequency. WiFi on 2.4GHz causes negligible interference.

In extreme cases, a very large number of Bluetooth devices can cause interference with each other because only one can advertise at a time without there being collisions and hence lost data. The maximum number of Bluetooth devices depends on how long and how often the Bluetooth devices transmit. It also depends on whether devices are just advertising or additionally using GATT connections. Bluetooth also has adaptive frequency hopping that helps reduce packet interference.

We have a deeper analysis of interference in the post on Bluetooth LE on the Factory Floor.

Improving iBeacon Location Accuracy

There are lots of ways of processing Bluetooth signal strength (RSSI) to determine location. Being based on radio, RSSI suffers from fluctuations, over time, even when the sender and receiver don’t move.

The College of Surveying and GeoInformatics, Tongji University, Shanghai , China has new research on iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations.

Trilateration and fingerprinting are common techniques to improve location accuracy based on RSSI. The paper improves on these by using analysis based on Kalman filtering of segments delimited by turns. This is used to derive locations based on pedestrian dead reckoning.

The researchers achieved a positioning accuracy of 2.75m.

Read about Determining Location Using Bluetooth Beacons

Read about Using Beacons, iBeacons for Real-time Locating Systems (RTLS)

Remote Team Management Using iOS as an iBeacon

S Sindhumol of Cochin University of Science and Technology, Kochi, India presents recent research into Implementation and Analysis of a Smart Team Management System using iOS Devices as iBeacon (pdf).

The key thing about this research is that it uses iOS rather than a beacon to advertise iBeacon. The system allows the entire team to determine the location of other members, perform location based tasks, receive announcements and communicate via instant chat.

iBeacon Team Management Screens

The paper contains some useful analysis of accuracy of distance measurement on distance, interference, measured power and obstructions:

Effect of iBeacon distance accuracy with obstructions
Effect of iBeacon distance accuracy with presence of another iBeacon
Effect of measured power variation on proximity and accuracy
Effect of obstructing objects on RSSI and Accuracy

On iOS it’s only possible to advertise iBeacon if the app is in foreground:

The major limitation of the proposed app is battery drainage while keeping the app active all the time in the foreground

A more practical system would have been implemented by having the users carry a separate wearable beacon. This would have allowed presence to be detected when the app isn’t in foreground and there wouldn’t have been a problem with excessive iOS battery use.

England’s Contact Tracing App Now Trying the Apple/Google API

After wasting a lot of money and more importantly time, England has abandoned using the public mobile OS Bluetooth APIs and is now using Apple/Google’s new contact tracing APIs. However, all has not been wasted because the home grown solution included advanced processing of Bluetooth RSSI values that provided for more precise measuring of close contacts.

The problem now is that the Google/Apple solution doesn’t provide access to RSSI and instead makes its own determination of close contact. Developers are forced down the path of a closed solution that can’t be improved upon. The new app is worse at determining distance than the original NHS Covid-19 app:

Engineers are still trying to reduce how often the Bluetooth-based tech wrongly flags people as being within 2m (6.6ft) of each other

RSSI is very noisy due to radio multi-path distortion, reflection, shadowing and fading. It also varies due to differences across devices in transmit and receive capabilities.

Variation of RSSI in devices and due to the environment

We have previously mentioned the use of Kalman filtering to improve the processing of RSSI. A newer research paper by The Alan Turing Institute of University of Oxford Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers describes the processing of RSSI to infer close contacts. The BBC believes this is what was used in the older NHS app.

The paper shows show how good prediction of proximity and risk can be obtained by using RSSI sequences rather than applying thresholds to single values. This correlates with our findings in that our Bluetooth contact tracing solution uses sequences rather than value thresholds. The paper also mentions that the duration of the risk also makes some close contacts more important to classify correctly. Again, we concur in that our solution has the ability to contact trace based on contact duration.

England’s contact tracing is now heading in a better direction and in the direction we previously advocated. They now need to persuade Apple and Google to improve their solution.

Bluetooth Positioning Using Separate Bluetooth Channels

While we wait for commercial Bluetooth 5.1 direction finding solutions to become available, people are trying to refine traditional locating methods to gain more accuracy. Baichuan Huang, Jingbin Liu, Wei Sun and Fan Yang have a research paper on A Robust Indoor Positioning Method based on Bluetooth Low Energy with Separate Channel Information.

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.

Read about Determining Location Using Bluetooth Beacons

Obtaining Distance from RSSI

RSSI is the signal strength at the Bluetooth receiver. The signal type, for example, iBeacon, Eddystone or sensor beacon is irrelevant. The value of the RSSI can be used to infer distance.

The accuracy of the distance measurement depends on many factors such as the type of sending device used, the output power, the capability of the receiving device, obstacles and importantly the distance of the beacon from the receiving device.

The output power isn’t known to the receiver so it’s sometimes added to the advertising data in the form of the ‘measured power’ which is the power at 1m from the sender.

The closer the beacon is to the receiver, the more accurate the derived distance. As our article mentions, projects that get more detailed location derived from RSSI, usually via trilateration and weighted averages, usually achieve accuracies of about 5m within the full range of the beacon or 1.5m within a shorter range confined space.

There’s some Android Java code on GitHub if you want to experiment with extracting distance from RSSI. There’s an equation for iOS on GitHub.

Need more help? Consider a Feasibility Study.

Beacons that flash/vibrate at a given distance.

Research Paper on Using Bluetooth for Indoor Locating

There’s a paper by Mariusz Kaczmarek, Jacek Ruminski and Adam Bujnowski of Gdansk University of Technology on the Accuracy analysis of the RSSI BLE SensorTag signal for indoor localization purposes (pdf).

They studied the radio signal from multiple Texas Instruments SensorTag CC2650 devices in order to determine if it could be used to determine location.

They concluded:

“Given the large number of factors governing the received RSSI, calibration is unlikely to be able to compensate for all of
them, leading us to conclude that there is an inherent limit to the accuracy of a BLE positioning system especially when multiple devices are used.”

They suggest:

…that instead of using a single RSSI measurement to estimate distance, try using the average or median value of N measurements collected on the same spot (at least N>20) so that you can reduce the effect of small scale fading.