Location – Page 3 – BeaconZone Blog

An Enhanced Triangulation Technique

Researchers from universities in Taiwan have developed a simple Bluetooth low-energy indoor positioning method using iBeacon components. The system aims to be lightweight, low-cost, and highly precise. The paper, Using iBeacon Components to Design and Fabricate Low-energy and Simple Indoor Positioning Method (PDF), introduces an enhanced triangulation technique using strength signatures of transmitted signals to improve positioning precision in planar locations.

The physical system consists of an observation (they call blind) device and multiple base stations using iBeacon components. These base stations can form virtual digital electronic fences and receive signals from blind devices, such as wearable devices or equipment tags. The positioning area is divided into rectangular or triangular subareas and the location of a blind device can be accurately located in real time using the measured strength of received signals and topology analysis.

The proposed method has an average error of less than 0.5 meters in the worst scenario and can be easily used in various environments. It integrates an STSS database and a triangulation method by evaluating the power values of received directional signals. Compared to traditional triangulation technologies, this method offers better positioning accuracy with simpler implementation procedures, reducing the overall cost of deployment.

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BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory

There’s new research into BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory. In life science automation laboratories, monitoring and managing the position of samples is crucial. One emerging solution for sample position estimation in these settings is the use of Bluetooth Low-Energy (BLE) beacons.

Historically, many fingerprinting models that harness received signal strength (RSS) data have been proposed for indoor positioning. However, a large number of these methods require an extensive installation of beacons. In contrast, proximity estimation, which relies solely on a single beacon, emerges as a more apt solution, especially for vast automated laboratories.

The intricacies of the life science automation laboratory environment present hurdles for the conventional path loss model (PLM), a prevalent method of proximity estimation based on radio wave propagation. Addressing this challenge, the paper introduces BLE sensing devices crafted specifically for sample position estimation. The proximity estimation rooted in BLE beacon technology is explored within a machine learning framework. Here, support vector regression (SVR) is employed to capture the nonlinear correlation between RSS data and distance. Concurrently, the Kalman filter is applied to reduce deviations in the RSS data.

Experimental outcomes spanning diverse settings underline the superiority of SVR over PLM. Remarkably, SVR achieved 1m absolute errors for an impressive 95% of test samples. The addition of the Kalman filter augments stable distance predictions, effectively smoothed the raw data and mitigated extreme value impacts.

When estimating positions between parallel workbenches, the framework achieved an average mean absolute error (MAE) of just 0.752m across 12 test positions. And for position estimation on workstations, identification accuracies beyond 99.93%.

In conclusion, for labs aiming to enhance sample position estimation, the BLE beacon paired with an IoT node presents a flexible sensing solution. By integrating machine learning, particularly SVR, and the Kalman filter, this framework offers increased accuracy in both corridors and labs.

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.