RSSIStability – Page 2 – BeaconZone Blog

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.

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.

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.

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.

Improving on Beacon Immediate, Near and Far

We recently highlighted an article on Beacon Trajectory Smoothing. Faheem Zafari, Ioannis Papapanagiotou, Michael Devetsikiotis and Thomas Hacker have a new paper on An iBeacon based Proximity and Indoor Localization System (pdf) that also uses filtering.

They use a Server-Side Running Average (SRA) and Server-Side Kalman Filter (SKF) to improve the proximity detection accuracy compared to Apple’s immediate, near and far indicators.

The researchers found:

The current (Apple) approach achieved a proximity detection accuracy of 65.83% and 67.5% in environment 1 and environment 2 respectively. SRA achieved 92.5% and 96.6% proximity detection accuracy which is 26.7% and 29.1% improvement over the current approach in environment 1 and 2 respectively

What’s interesting here is that the researchers have quantified the accuracy of Apple’s implementation in two scenarios. The accuracy isn’t that good and as the researchers have shown, can be improved upon significantly.

Beacon Trajectory Smoothing

The problem with using RSSI for detecting location is that raw data contains lots of noise. Also, this noise becomes more prevalent in the viewed data when location samples are taken less often. There’s a useful new article at InfoQ on Processing Streaming Human Trajectories with WSO2 CEP.

The idea uses Kalman filtering to smooth noisy human trajectories.

Before and after filtering

This method is particularly useful for large realtime IoT rollouts because it uses a very small memory resource, is very fast and the calculation is recursive, so new values can be processed as they arrive.

Crowd Analysis Using Beacons

With so many uses of beacons centred around notifications to users, it’s interesting to see Queen Mary University of London doing something different. Research by Kleomenis Katevas, Laurissa Tokarchuk, Hamed Haddadi and Richard G. Clegg of the Department of Computing of Imperial College looks into detecting group (crowd) formations using iBeacon (pdf).

They used beacon RSSI and phone motion together with algorithms based on graph theory to predict interactions inside the crowd. They verified their finding using using video footage as ground truth.

The paper has some particularly interesting observations from testing RSSI in an EMC screened anechoic chamber and also has some information on distance estimation models.