Category: rtls

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.

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

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.

Read about AI Machine Learning with Beacons

Information Display and Alerting System Based on iBeacon

There’s new research on a An Intelligent Low-Power Displaying System with Integrated Emergency Alerting Capability. The authors have implemented a wireless ePaper system showing static and dynamic information together with indoor locating based on Bluetooth beacons .


Unfortunately, the locating system is based on an empty room and the technique would be liable to fluctuations in the physical environment.

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

Trust Range Method of Improving Location Accuracy

A mentioned in our post on location accuracy, two methods of improving accuracy are calibration and trilateration. There’s a recent research paper on iBeacon indoor localization using trusted-ranges model, that explores an alternative ‘trusted-ranges’ method. The method is still based on the RSSI measurements between the beacon and detector. It builds up a trusted-range model to describe how the RSSI varies over time and distance.

The model supplies reliable ranges of received signal strength values from nearest neighbours classifying received signal strength values into various levels of range. It performs better than calibration, especially at shorter ranges, while having a low complexity and hence computationally fast speed.

Bluetooth vs UWB vs RFID for RTLS

In the past, before Bluetooth Low Energy (LE) was introduced in 2010, real time locating systems (RTLS) used costly Ultra Wide Band (UWB) devices or radio frequency id (RFID).

Bluetooth LE is increasing being used for RTLS due to:

  • the availability of much lower cost Bluetooth devices, up to 1/4 the price of UWB.
  • a much longer battery life than UWB, 5+ years for some devices.
  • a much longer range than RFID, up to 300m vs cm.
  • the ease of detecting beacons via apps and single board computers using existing operating system Bluetooth APIs
  • the availability of low cost Bluetooth gateways
  • being based on standard Bluetooth rather than proprietary UWB protocols, custom devices and specialist skills

Read more about Beacons and Real Time Locating Systems (RTLS)

New Bluetooth Direction Finding Feature

A new direction finding feature has been released for Bluetooth 5.1 (pdf). Using more than one antenna, as used by Quuppa, allows for direction finding.

The paper on Enhancing Bluetooth Location Services with Direction Finding explains how location services currently use RSSI to estimate the distance. Direction finding introduces more advanced Angle of arrival (AoA) and angle of departure (AoD) techniques into Bluetooth 5.

“Should smartphone vendors choose to include Bluetooth direction finding with AoA support in their products, item finding solutions could be enhanced to provide directional information.”

As with the move from Bluetooth 4 to Bluetooth 5 it’s going to be while before we see (non Quuppa) products with direction finding. This feature requires specific hardware and software. Before that, it needs SDKs from the SoC vendors. Existing smartphones, beacons and gateways won’t be able to be upgraded.

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