Using Bluetooth and WiFi RSSI for Locating

There’s a recent paper by Hongji Cao,Yunjia Wang,Jingxue Bi and Hongxia Qi of China University of Mining and Technology on An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance.

The paper looks into how to combine both Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP) to provide for an adaptive Bluetooth/Wi-Fi fingerprint positioning system based on Gaussian process regression (GPR).

The adapative feature is particularly useful because fingerprint acquisition requires a great deal of effort and requires subsequent update and maintenance.This new method provides a better positioning than Bluetooth and Wi-Fi positioning alone but at the cost of extra computation.

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Using iBeacons with Intelligent Displaying and Alerting Systems

There’s recent research into using iBeacons with intelligent displaying and alerting systems (SICIAD) typically found in public buildings and offices. The paper An Intelligent Low-Power Displaying System with Integrated Emergency Alerting Capability by Marius Vochin, Alexandru Vulpe, Laurentiu Boicescu, Serban Georgica Obreja and George Suciu of the University of Bucharest shows how beacons can be used to determine indoor position of mobile terminals or signalling points of interest.

An Android app uses the beacons to detect location and sends it to the SICIAD system. The researchers concluded that:

“By using an appropriate number of beacons and optimal positions, a relatively precise indoor localization can be obtained with iBeacon technology”

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.

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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.

IoT Priority and Asset Tracking

Gartner has a new report Hype Cycle for the Internet of Things 2019, in which they say:

“The Priority Matrix shows that many IoT technologies are 5 years from mainstream adoption. However only one innovation profile will reach maturity in 2 years, indoor location for assets.

So why is ‘indoor location for assets’ more likely to achieve mainstream adoption sooner than other technologies? It’s because there are clear benefits for most companies and off-the-shelf software such as our BeaconRTLS™ is already available.

Our work with companies shows they are nevertheless cautious. Companies are taking time to understand the competing asset tracking technologies and are performing, sometimes lengthy, trials to determine how new systems will integrate with existing systems. They are considering the implications of SAAS vs on-premise solutions, the availability of second-sourced beacon hardware and the compromises of accuracy vs system complexity and cost.

Location 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.

Reducing Asset Redundancy Using Beacons

There are many industries where the inability to find assets leads to the requirement to have many more of those assets. This is especially so in areas, such as hospitals, where not finding things can cost lives.

It also tends to be the case that such urgently required items are also expensive as they are critical pieces of equipment. When equipment is very expensive, lack of redundancy can end up causing key staff spending their time finding things rather than doing their main job.

Even when not finding things isn’t mission critical, a lot of time, human effort and hence cost can be wasted if assets aren’t available. Examples include vehicles in fleet management, tools in construction and equipment in manufacturing.

Beacons and locating systems allow you to reduce asset redundancy, save costs and make working processes more efficient.

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

Using iBeacons for Locating Robots

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.

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Using AI Machine Learning on Bluetooth RSSI to Obtain Location

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.

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