Location – Page 4 – BeaconZone Blog

Using Beacons To Detect Human Movement

There’s an innovative use of beacons mentioned in the research paper on Developing a Human Motion Detector using Bluetooth. Beacons and its Applications (PDF).

Most motion sensing applications usually place a sensor beacon on the things that will move. The accelerometer in the beacon reports movement. The research paper describes an alternative method of detecting movement of a person, an elderly person in this case, based on the change in blocking of the beacon signal over time. This has the advantage that the beacon doesn’t need to be worn. Also, it doesn’t have to be a accelerometer beacon as any beacon can be used.

The problem with using the strength of the beacon signal (RSSI), is that it varies over time even when there’s no change of blocking in the room. This is due to radio frequency (RF) noise and reflection. The authors of the paper looked into smoothing of the data to filter out such variance in the data:

The report concludes that when averaging over three or more RSSI values, it’s possible to minimise the RF variance and reliably detect the variance caused by human movement in the room.

Another, more reliable, way of detecting movement is to use a beacon with built-in PIR such as the iBS02PIR, M52-PIR, IX32 or MSP01.

Using Packet Loss to Infer Location

There’s new research from the University of Illinois titled Packet Reception Probability: Packets That You Can’t Decode Can Help Keep You Safe (pdf). Many existing systems estimate distance using the Receiver Signal Strength Indicator (RSSI) which is negatively impacted by sampling bias and multipath effects. As an alternative, the study uses Packet Reception Probability (PRP) that utilises packet loss to estimate distance.

Localisation is achieved through a Bayesian-PRP approach that also includes an explicit model of multipath. To facilitate straightforward deployment, there’s no need for any modifications to hardware, firmware, or driver-level on standard devices and only minimal training is required.

A variety of devices were used including Bluvision iBeeks, BluFi, a Texas Instrument Packet Sniffer, a laptop, and Android smartphones (Nexus5x). 60 iBeacons were deployed in a library and 38 in a retail store. The Texas Instrument Packet Sniffer, connected to a Windows laptop was used for packet reception from beacons. Android phones were equipped with a purpose-built Android app.

PRP was found to provide metre-level accuracy with just six devices in known locations and 12 training locations. Combining PRP with RSSI was found to be beneficial at short distances up to 2m. Beyond distances of 2m, fusing the two is less effective than using PRP alone because RSSI becomes de-correlated with distance.

New Bluetooth Location Market Research

Bluetooth SIG, the organisation responsible for Bluetooth standards, has a new Bluetooth® Market Update in collaboration with ABI Research. Bluetooth covers a large range of device types and application areas. Here are some insights related to location services.

Bluetooth location services device growth will trend significantly upward and return to pre-pandemic forecasts due to heightened awareness of the benefits of Bluetooth location services. There will be 2.46x growth in annual Bluetooth location services device shipments from 2023 to 2027.

Bluetooth real time location systems (RTLS) are set for rapid growth. New regulatory and safety requirements in manufacturing, stricter compliance procedures and sustainable operation requirements are making RTLS solutions more attractive. There will be 178,000 Bluetooth® RTLS implementations by the end of 2023. Many commercial and industrial facilities are now relying on asset tracking solutions to optimise resource and inventory control. The commoditisation of off-the-shelf Bluetooth asset tracking gateways and beacons are major drivers behind continued growth. 112 million Bluetooth asset tracking devices will ship in 2023.

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More Accurate Beacon Locating Using AI Machine Learning

There’s new research in the Bulletin of Electrical Engineering and Informatics on Bluetooth beacons based indoor positioning in a shopping malls using machine learning. Researchers from Algeria and Italy improved the accuracy of RSSI locating by using AI machine learning techniques. They used extra-trees classifier (ETC) and a k-neighbours classifier to achieve greater than 90% accuracy.

A smartphone app was used to receive beacon RSSI and send it to an indoor positioning system’s data collection module. RSSI data was also filtered by a data processing module to limit the error range. KNN, RFC, extra trees classifiers (ETC), SVM, gradient boosting classifiers (GBC) and decision trees (DT) algorithms were evaluated.

The ETC model gave the best accuracy. ETC is an algorithm that uses a group of decision trees to classify data. It is similar to a random forest classifier but uses a different method to construct the decision trees. ETC fits a number of randomised decision trees on sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. ETC is a good choice for applications where accuracy is important but the data is noisy and where computational efficiency is important.

Using Beacons for Intelligent In-Room Presence Detection

Most Beacon usecases involve putting beacons on things or in places and triggering notifications on users’ phones. There’s a paper by Yang Yang, Zhouchi Li and Kaveh Pahlavan of Worcester Polytechnic Institute (WPI), Worcester, MA that instead proposes Using iBeacon for Intelligent In-Room Presence Detection.

Their system records users in a room for applications such as graduate seminar check-in, security and in and out counting. It recognises in room presence by analysing path loss and door motion readings to decide whether a person is inside the room. Their custom app receives the beacon data and sends it to a server for analysis. They experimented using two iBeacons, one attached to the outside of the door with another mirroring at the inside and also as single iBeacon implementation that still performed well.

The paper also a useful chart showing the variation of RSSI with how a phone is held:

Advantages of Real Time Location Systems (RTLS)

RTLS systems are used to track the location of objects or people, tagged with Bluetooth beacons, in real time. Some of the advantages of using a RTLS include:

Improved efficiency: RTLS systems allow organisations to track the location of assets or personnel in real time, which can help improve the efficiency of operations. For example, a RTLS system can be used to track the location of equipment in a warehouse, allowing workers to quickly locate and retrieve items when needed.
Enhanced safety: RTLS systems can also be used to improve safety in a variety of settings. For example, a RTLS system could be used to track the location of workers in a construction site, allowing supervisors to quickly respond to any safety incidents.
Increased visibility: RTLS systems provide organisations with real-time visibility into the location of assets or personnel, which can help with decision making and resource allocation. For example, a RTLS system can be used to track the location of vehicles on a site, allowing managers to optimise routes and reduce fuel consumption.
Improved asset utilisation: RTLS systems can help organisations to better utilise their assets, by providing real-time information about their location and availability. For example, a RTLS system could be used to track the location of equipment in a hospital, allowing better matching of demand with supply.

Overall, the main advantage of using a RTLS system is that it provides organisations with real-time information about the location of assets or personnel, which can help them to improve efficiency, enhance safety, and better utilise their resources.

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Integrating Beacons into Existing Systems

There are three main ways beacons can be integrated into existing systems:

1. Using Smartphone Apps

Beacons are usually stationary. Apps on users’ smartphone use the standard Bluetooth iOS and Android APIs to detect beacons and send information to your cloud or servers, typically via HTTP(S).

2. Using Ethernet/WiFi Gateways

Beacons are using moving. Gateways in fixed positions detect beacons and send information to your cloud or servers, typically via HTTP(S) or MQTT.

3. Using an Intermediate Platform Such as a Real Time Location System (RTLS)

This is a variant on #2 in that gateways send information to a system such as BeaconRTLS™ or PrecisionRTLS™. These systems have HTTP(S) APIs that can be used by your cloud or servers.

More information:
What are beacons?
Beacons for the Internet of Things (IoT)

If you need more project specific help we also offer consultancy and feasibility studies.

Indoor Positioning Using iBeacon and ESP32

Bluetooth beacons advertising iBeacon can be used to perform indoor locating using trilateration. Trilateration is where three receivers are used to measure signal strength (RSSI) to calculate the position.

It’s possible to use ESP32 single board computers as Bluetooth receivers. The GitHub project iBeacon-indoor-positioning-demo has an example open source implementation. There’s also an accompanying blog post.

The implementation uses MQTT to send the data to a React app on a server where it’s displayed on a floorplan.

In practice, you might want to consider creating a more robust solution that uses Bluetooth gateways rather than ESP32 devices. There’s also the Bluetooth AoA Direction Finding standard that’s more accurate than using RSSI.

New Bluetooth Location Services Infographic

The Bluetooth SIG, who manage the Bluetooth standards, have a new infographic on location services based on figures from ABI Research.

Some insights:

The leading location services category is Retail and Services at 62%.
Smartphones are helping drive adoption.
There will be 35% compound annual growth in Bluetooth location devices from 2022 to 2026.
There will be 547,000 Bluetooth RTLS implementations by 2026.

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Bluetooth AoA Direction Finding in the Cloud

We have had many enquiries from ISVs regarding the possibility of using AoA in the cloud. The idea is to use a location engine instance to allow their multiple customers to access AoA direction finding as a service.

Bluetooth AoA Direction finding works by having multiple locators that communicate with an on-site gateway that connects to the location engine. This is radio data so there’s lots of information sent very often. For large sites, there are multiple edge gateways. In most systems with more than a few assets, the gateway throughput becomes limited by the gateway hardware and the location engine processing input is limited mainly by the CPU capability.

The location engine has to do a lot of work. It implements computationally intensive radiogoniometry and anti-interference algorithms using data from multiple gateways.

In most cases, with large numbers of assets, the gateways and location engine are working near full capacity with the latency of the whole system being balanced against the number of assets.

While such a system can work in the cloud, the bandwidth and latency of the connection to the cloud means that it usually isn’t technically and financially viable. Sharing such a system across customers is even less viable. Instead, standalone systems have to be set up on-site to provide optimum performance.

Be aware that some ‘toy’ evaluation, as opposed to production, AoA systems perform the radiogoniometry and anti-interference algorithms at the gateway. While might work for a few assets, the gateway usually doesn’t have the processing power to scale to a production environment. Also, the gateway is only processing the radiogoniometry and anti-interference algorithms using data it has seen. Production grade radiogoniometry and anti-interference algorithms need to consider data from multiple gateways.

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