iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
There’s recent research by Beakcheol Jang, Hyunjung Kim and Jong Wook Ki of Sangmyung University, S Korea on An Accurate Indoor Positioning Algorithm Using Sensors and Crowdsourced Landmarks.
The paper describes how to obtain more accurate heading direction and user coordinates by combining smartphone sensors and iBeacons as landmarks.
There’s recent research by Gabriele Salvatore de Blasi, José Carlos Rodríguez-Rodríguez, Carmelo R. García and Alexis Quesada-Arencibia of University of Las Palmas de Gran Canaria, Spain on Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems.
The paper starts by giving an overview of fingerprinting. It explains how fingerprinting is time-consuming and labour-intensive. Fingerprinting is affected by:
“Reflection, refraction, path loss, large fluctuations, multipath fading, non-line-of-sight (NLOS) conditions”
A problem is that some environment-related factors change over time, such as changes in hardware/furniture, the presence of people and ambient humidity conditions meaning that fingerprinting isn’t a one-off activity.
The researchers conclude that the highest transmission power (+4 dBm) produces the best location accuracy. However, this uses a lot of battery power. Use of the lowest power (−20 dBm) only worsened the accuracy by 11.8%. Similarly, lowering the density of the beacons by around 50%, the error increase was only about 9.2%. Increasing the advertising interval didn’t have a significant impact on the accuracy.
The affect of beacon orientation was assessed and vertical orientation was found to be best. As expected, introducing a slight electrical noise during the positioning phase did not significantly affect accuracy.
Read about Locating with Beacons
There’s a useful recent research paper by Basem AL-Madani, Farid Orujov, Rytis Maskeliūnas, Robertas Damaševičius,and Algimantas Venčkauskas on Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings.
The paper explores indoor location algorithms and implements a fingerprinting system using RSSI that achieves an average error of 0.43m.
The authors’ ‘fuzzy logic type-2’ system allows for complex environments such as buildings with glass/metal corridors. They comment that fingerprinting requires pre-configuration which is one of the main disadvantages of this method.
There’s new research by Umair Mujtaba Qureshi, Zuneera Umair and Gerhard Petrus Hancke of the Department of Computer Science, City University of Hong Kong on Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision. The paper takes a deep look into the relationship between transmitted power and signal stability. It also looks at ways of filtering received signal strength (RSSI) data to improve the location accuracy.
The main insight is that along with the expected difference in the RSSI attenuation there is a considerable difference in the BLE signal variation at all transmission power levels with respect to distance. The variation increases and the localisation accuracy decreases from high to low transmission power levels:
Another observation is that outliers in the data tend to affect the localisation accuracy. Applying filters to the data, they achieved a location accuracy of 2.2 meters with a precision of 95%.
One comment we have is that the researchers didn’t try different beacons. As we mentioned in 2016, the RSSI stability also varies across different beacon models.
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EgiGeoZone Geofence is a useful app for Android with over 10,000 users that allows you set up triggering based on location. There’s also a Bluetooth version that allows triggering in the vincinity of iBeacons.
The app is also open source on GitHub. Note that the app doesn’t yet work with the Android 8.0 background changes. The author is hoping someone else will fork the code and keep the app alive.
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
There’s a new solution to track cleaning staff that provides app and web source code to implement a cleaning staff tracking system using iBeacons:
Manage beacons, buildings, zones and broadcast messages. The web interface shows staff activity and allows staff to be assigned to tasks. Staff can update task status and provide notes from their smartphones.
This solution has been added to the BeaconZone Solutions Directory where you can find more solutions that work with generic beacons.
We sometimes get asked for location beacons or which beacons are best for determining location. All beacons can be used for locating. While there are physical aspects such as battery size/life and waterproofing that make some beacons more suitable for some scenarios, locating capability is determined more by the software used rather than the beacons themselves.
Our article on Determining Location Using Bluetooth Beacons gives an overview on locating while the article on Using Beacons, iBeacons for Real-time Locating Systems (RTLS) explains how RTLS work. If you wish to create your own locating software we have a large number of posts on RSSI.
If you have been attracted to Bluetooth by recent announcements on Bluetooth direction finding, be aware that no ready-made hardware or software solutions exist yet. It will take a while, perhaps years, before silicon vendors support Bluetooth 5.1 direction finding, silicon vendors create SDKs and hardware manufacturers create hardware.
Beacons are great for use with robots for use in determining extra contextual information. There’s recent research on Autonomous Navigation of an Indoor Mecanum-Wheeled Omnidirectional Robot Using Segnet (pdf) that uses iBeacons to determine a rough location of the robot.
The locating uses Kalman filtering and trilateration to get a fix for the robot.
If you want to learn more about using RSSI to determine robot location there’s also a presentation video Robot Localization using Bluetooth Low Energy Beacons RSSI Measures by David Obregón Castellanos.
Read about Using Beacons, iBeacons for Real-time Locating Systems (RTLS)
In our previous post on iBeacon Microlocation Accuracy we explained how distance can be inferred from the received signal strength indicator (RSSI). We also explained how techniques such as trilateration, calibration and angle of arrival (AoA) can be used to improve location accuracy.
There’s new research presented at The 17th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys ’19) by researchers from Nagoya University, Japan that looks into the use of AI machine learning to process Bluetooth RSSI to obtain location.
Their study was based on a large-scale exhibition where they placed scanning devices:
They implemented a LSTM neural network and experimented with the number of layers:
They obtained best results with the simplest machine learning model with only 1 LSTM:
As is often the case with machine learning, more complex models over-learn on the training data such that they don’t work with new, subsequent data. Simple models are more generic and work not just with the training data but with new scenarios.
The researchers managed to achieve an accuracy of 2.44m at 75 percentile – whatever that means – we guess in 75% of the cases. 2.44m is ok and compares well to accuracies of about 1.5m within a shorter range confined space and 5m at the longer distances achieved using conventional methods. As with all machine learning, further parameter tuning usually improves the accuracy further but can take along time and effort. It’s our experience that using other types of RNN in conjunction with LSTM can also improve accuracy.
If you want to view the research paper you need to download all the papers from the conference (zip) and extract p558-uranoA.pdf. Some of the other papers also make interesting, if not directly relevant, reading.
Read about AI Machine Learning with Beacons