This system integrates a ‘blind’ device and multiple base stations using iBeacon components to form virtual digital electronic fences, effectively receiving signals from moving devices or tags in a targeted area. The proposed method divides the positioning area into rectangular or triangular subareas and establishes a loss value database for improved location estimation.
The system shows high accuracy, with an average error of less than 0.5 m in the worst-case scenario, making it suitable for various environments. The paper covers the architecture of the system, development phases and experimental results demonstrating the system’s effectiveness. The research offers significant insights into low-cost, high-precision indoor positioning methods suitable for diverse applications such as healthcare, smart cities, and industrial settings.
Recent research A Novel Optimized iBeacon Localization Algorithm Modeling by Jiajia Shi et al, addresses the challenges in achieving high accuracy in indoor object localisation or tracking using iBeacon systems. These systems, which use Bluetooth sensors, are appealing due to their low cost and ease of setup but there can be challenges with accuracy and they can sometimes be susceptible to interference and environmental noise.
To overcome these challenges, the study focuses on developing error modeling algorithms for signal calibration, uncertainty reduction and noise elimination. The novel approach is based on the Curve Fitted Kalman Filter (CFKF) algorithms. The research demonstrates that the CFKF algorithms significantly improve the accuracy and precision of iBeacon localisation.
The paper discusses the limitations of current indoor localisation technologies, including the Received Signal Strength Indicator (RSSI) method, which is affected by multipath fading in indoor environments.
The authors propose a novel CFKF error modelling approach to enhance the estimation accuracy of iBeacon systems in field experiments. This approach includes a developed Kalman Filter (KF) state estimate algorithm based on the modified Least Squares Algorithm (LSA), a system calibration process for the RSSI and estimated distance and the CFKF error modelling for improved accuracy.
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.
Researchers from Spain have recently developed a safety system that uses Bluetooth Low Energy (BLE) to ensure the correct use of Personal Protection Equipment (PPE) on construction sites. This innovative system is not only robust and reliable but also easily adaptable to various dangerous machines.
The system is built on RSSI (Received Signal Strength Indicator) information transmitted by BLE devices arranged in a particular rig combined with a Bayesian distance estimator. The aim is not merely to signal risky situations caused by the misuse of PPE but to intervene swiftly and robustly to eliminate the safety risk.
The researchers have built upon previous results on the statistically sound measurement of distances and closeness in construction sites. By collocating several BLE transmitters near orthogonally, they have managed to reduce interferences while avoiding the cost of more advanced technologies.
The practical contributions of this research include the design of the system, a working prototype and a thorough statistical analysis for finding the optimal parameters for both the software and the equipment. The research shows that using several orthogonally collocated BLE transmitters improves robustness and overall performance without requiring more complex and costly equipment.
The improvements are most significant as the number of transmitters increases. Using a diversity of devices is better when these devices are noisy and it also enhances the robustness of the solution. An arrangement of orthogonal BLE beacons allows for an increased rate of advertising messages, and an extended Kalman filter plus a discrete filter can benefit from that increased flow of data, providing a simple and efficient approximation to the problem of safety estimation.
The use of an additional beacon to notify the correct use of the PPE, implemented inside a wearable microcontroller, is a very flexible solution. It allows for different local implementations using various sensors and measurements without the need to modify the RSSI-only method in the receiver, and with any number of users. The system can be easily integrated into a wide variety of dangerous machines and tools such as angle grinders, concrete mixers and pneumatic drills.
We sometimes get asked whether a beacon is faulty because a customer is seeing a lot of fluctuation in the Received Signal Strength Indicator (RSSI) values, even in a seemingly stable environment and with no change in distance. The short answer is: this is normal. The reason for this lies in the complex nature of radio signals and how they interact with the environment.
Radio signals are susceptible to a variety of factors that can affect their received strength. When a beacon sends out a signal, it doesn’t just travel in a straight line to the receiver. Instead, it disperses in multiple directions and can bounce off walls, floors and other objects.
Reflections can cause the signal to take different paths before reaching the receiver. Each path can have a different length and, therefore, a different time delay. This results in a phenomenon known as multipath fading, where multiple copies of the signal arrive at the receiver at slightly different times. This can cause fluctuations in the RSSI values you observe.
While reflections are a primary cause of RSSI fluctuation, they are not the only one. Other physical changes in the environment can also contribute to this variability. For example, the presence of people moving around can affect the signal, as the human body is mostly water and can absorb radio frequencies. Similarly, other electronic devices emitting radio frequencies can interfere with the signal, causing further fluctuations.
To get a more accurate understanding of the signal strength, it’s advisable not to rely on a single RSSI value. Instead, you should look at many RSSI values over a period of time and calculate the average. This approach helps to mitigate the effects of temporary fluctuations and provides a more stable and reliable measure of signal strength.
Many people, particularly researchers, have looked into the intricacies of RSSI and its variability. Various algorithms and methods have been developed to improve the accuracy of RSSI-based distance estimation and location tracking. For those interested in a deeper understanding or potential solutions to this issue, we recommend looking at the articles tagged RSSI and RSSIStability on our blog.
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.
Instead, measuring distance happens on the receiving end. Devices such as smartphones are equipped to detect these beacon signals. When a beacon sends out its Bluetooth radio signal, the receiving device knows the received signal strength (RSSI). This RSSI can be used to infer the distance between the beacon and the device.
In the proximity of a few metres, the variation in RSSI is significant enough to deduce the distance with a reasonable degree of accuracy. However, as the distance increases, the variation in RSSI becomes less pronounced. This means that while you can determine if a beacon is close or far away, pinpointing an exact distance becomes challenging.
For example, the iOS programming API, CoreBluetooth, provides classifications for the detected beacon signals. These classifications are ‘immediate’, ‘near’, and ‘far’. They don’t give a precise measurement in metres or feet but rather a general idea of the beacon’s proximity.
In terms of maximum range, depending on the specific beacon, it can be detected from distances up to 50m or even 100m. However, as mentioned earlier, at these longer ranges, the RSSI doesn’t provide a clear indication of exact distance. Instead, it offers a more general sense of whether the beacon is nearer or farther away.
This paper introduces a new methodology called OBLEA, which aims to optimise BLE anchor configurations in indoor settings. It takes into account various BLE variables to enhance flexibility and applicability to different environments. The method uses a data-driven approach, aiming to obtain the best configuration with as few anchors as possible.
The OBLEA method offers a flexible framework for indoor spaces where the occupants are fitted with wrist activity bracelets (beacons) and BLE anchors are set up. The anchors then collect and aggregate data, sending it to a central point (fog node) via MQTT.
A dataset was generated with the maximum number of anchors in the indoor environment, and different configurations were then trained and tested based on this dataset. The best balance between fewer anchors and high accuracy was chosen as the optimal configuration.
This methodology was tested and optimised in a real-world scenario, in a Spanish nursing home in Alcaudete, Jaén. The experiment involved seven inhabitants in four shared double rooms. As a result of this optimisation, the inhabitants could be located in real time with an accuracy of 99.82%, using a method called the K-Nearest-Neighbour algorithm and collating the signal strength (RSSIs) in 30-second time windows.
A device was specifically designed for a study to assess the relationship between the Received Signal Strength Indicator (RSSI) of a BLE beacon and BLE reader and to develop a distance prediction model. This model was then applied in a static situation and on-sheep studies, using a multi-lateration approach to determine a beacon’s location within a field setting. A purpose-built Wearable Integrated Sensor Platform (WISP) was developed for the study, featuring a BLE reader and other sensors. It was designed to report the identity and RSSI of the 16 ‘closest’ beacons seen for each duty cycle.
The findings revealed that the height of the device had an impact, with fewer beacons reported at a shorter distance in WISPs at the lower height of 0.3 m. RSSI can vary greatly based on factors like transmission power, device orientation, enclosure and the operating environment.
Using the distance prediction and adjusted distance prediction, beacon locations could be estimated for most of the beacons. Not all beacons could be located due to issues such as being reported by too few WISPs or the resulting multi-lateration circles not intersecting.
The study suggests that BLE can potentially be used for sheep localisation in outdoor environments. The multi-lateration approach is dependent on receiving RSSI readings from multiple readers at a similar timepoint, it could offer more information about localisation and movement than simple proximity ranges or presence/absence. Locating a sheep to within about 30 m in a field environment represents a significant step forward.
Many people inquire about adjusting the transmission distance of a beacon. They often wish to either conserve battery or restrict the range at which a beacon is detectable.
While some third-party platforms and SDKs offer distance settings, it’s a misconception to think you can directly set the distance. What you’re actually adjusting is the transmission power, which in turn influences the transmission distance. But since this involves radio waves, which are prone to reflections and interference, it’s impossible to guarantee that a specific power will equate to a precise distance.
When using an app to detect beacons, you can employ the Received Signal Strength Indicator (RSSI) to focus on those within a desired range. However, it’s challenging to precisely correlate RSSI with the actual distance.
Some wonder if they can set the distance in terms of centimetres, similar to NFC. Typically, this isn’t feasible because even at their lowest power setting, most beacons transmit over a distance of about a metre.
Rather than asking if the transmitter’s distance can be minimised, it might be more practical to configure the receiver to disregard detections from further away. By using the RSSI value on the receiving app or another Bluetooth scanning device, you can filter out distant beacons. Specifically, you can dismiss detections with an RSSI below a certain threshold, allowing you to focus on detections within a centimetre range.