FIND Framework for Internal Navigation and Discovery

FIND is an open source indoor locating system for home automation, indoor local positioning and passive tracking. It uses your smartphone or laptop to pinpoint your position in your home or office with a location precision of below 10 sq ft.

FIND uses scanning of WiFi and Bluetooth:

FIND compiles these different signals can be compiled into a fingerprint which can be used to uniquely classify the current location of that device

Read the documentation, the FAQ and source code on GitHub.

Using Bluetooth Beacons for Jungle Vehicle Poacher Tracking

There’s new research by Karan Juj and Charith Perera of Cardiff University on Exploring the Suitability of BLE Beacons to Track Poacher Vehicles in Harsh Jungle Terrains (pdf). The paper describes a real world study conducted in a Malaysian jungle that tracks poacher vehicles.

Deep jungle terrain has challenges because GPS doesn’t work, there’s no cellular connection and 100% humidity can hinder wireless signal.

The study mounted Bluetooth beacons beside a road and placed a concealed receiver inside a vehicle:

The researchers tested various types of obstructions that would be faced in deployment and measured the reliability of detecting beacons from under bonnet:

After extensive evaluation, the researchers found that Bluetooth LE beacons can be successfully used in jungle terrains to a track vehicle.

The Affect of Transmission Power, Advertising Interval and Beacon Placement Density on Location Accuracy

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. Read our previous article on orientation. As expected, introducing a slight electrical noise during the positioning phase did not significantly affect accuracy.

Read about Locating with Beacons

Beacon Based Navigation for the Visually Impaired

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.

The Affect of Power Levels on Wireless Indoor Localisation Accuracy

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.

Need more help? Consider a Feasibility Study.

EgiGeoZone Geofence for Android

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.

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

RTLS Locating Using Mesh

You-Wei Lin and Chi-Yi Lin of Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan have a paper An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network.

Although the implementation is similar to SensorMesh™ and BeaconRTLS™ used together, their solution uses a proprietary mesh implementation and a proprietary data protocol. Consequently, their implementation suffers longer response time when used over longer physical distances. Their maximum inter-hop distance of 8 to 10 m also isn’t good due to non-optimal devices and non-optimal device positioning.

Cleaner Staff Tracking with iBeacons

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:

Android screens
Web interface

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.