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.
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.
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.
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.
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.
There’s a research paper by researchers from Taiwan on A practice of BLE RSSI measurement for indoor positioning. The paper looks into received signal strength (RSSI) to distance conversion, the significance of antenna plane (orientation) and measurements in two different situations, a low noise classroom and a more noisy manufacturing site workshop.
Techniques employed included developing a signal propagation model, trilateration, modification coefficients and Kalman filtering.
The hardware used included an Arduino Nano 33 (Bluetooth 5) and Linkit 7697 (Bluetooth 4.2). Over 1.6 million samples were collected generating over 13Mb of data.
“Multiple factors affected the RSSI, such as the device performance, antenna direction and radio wave refraction”
A positional accuracy of 10cm was achieved in ideal conditions dropping to meter level accuracy in more challenging setups and environments. The sensitivity of the (ceramic) antenna was found to fluctuate widely with orientation/topology. The researchers concluded that the key factor for reliable indoor positioning, based on RSSI, is maintaining good signal measurement quality.
The following graph shows the standard deviation of the RSSI @ 1m, for some of our beacons, measured over a 60 second time period:
Smaller bars are better and represent beacons whose RSSI varied the least over time.
We found that beacons belonged to one or two groups. Firstly those with very stable RSSI and secondly those with an RSSI that had a standard deviation between about 4 and 6 dBm.
Signal stability is more important when you are using the RSSI to infer distance, either directly from the RSSI itself or indirectly via, for example, the iOS immediate, near and far indicators. RSSI varying without a change of distance might cause more spurious triggering. However, you should keep in mind that environmental factors can often cause variation much larger than the 4 to 6 dBm found in this test. Moving obstacles, for example people, will cause significant variation in RSSI.
Bluetooth LE advertising moves pseudo-randomly between radio channels. The channels use different radio frequencies that, in turn, results in fading of the signal at different distances. We experienced and mitigated similar behaviour in our LocationEngine™. Different radio frequencies experience different constructive and destructive interference at different physical locations. Beacons that move more between channels can cause more rapidly varying received signal strength (RSSI).
While there has been lots of research into server-side processing to improve location accuracy, this research instead looks into improving accuracy locally, in terms of finding the nearest beacon. This kind of processing is often needed where smartphone apps provide users with contextual information based on their location, for example, in museums.
It’s not possible to use the raw received signal strength (RSSI) because it changes frequently due to changes in blocking and reflection in a room. Any errors in determining the correct transmitter can cause errors in displaying relevant information which, in turn, leads to a poor visitor user experience.
The study involved use of iBeacons detected by Android smartphones, both in a controlled room with three obstacles and a real-world setting Expo Museum.
The proposed algorithm stabilised the RSSI by considering previous measurements to filter out sudden fluctuation of the RSSI signal or the rapid movement of the mobile device. The smartphone’s accelerometer was also used dynamically change the scan interval based on the user’s movement.
In the controlled room, the proposed algorithm had a 14.29% better success rate than a standard algorithm using the raw RSSI values. It performed particularly (20%) better in spaces having medium or high density of physical obstacles. It also performed better in the real-world Expo environment with a success rate of 95% compared to 87% with a standard algorithm.
There’s new research by the Institute of Information Science and Technologies, Pisa, Italy on Detecting Proximity with Bluetooth Low Energy Beacons for Cultural Heritage. The paper starts by describing alternative technologies including Ultra-wideband (UWB), Near Field Communication (NFC) and vision.
Beacons are attached to exhibits and the paper compares two proximity detection algorithms, a ‘Distance-based Proximity Technique’ and a ‘Threshold-based Proximity Technique’. The paper describes stress, stability and calibration testing of the system.
The researchers found a strong variation of RSSI value for different tags that they say is caused by the varying channel (frequency) used by Bluetooth LE as well as environmental issues such as obstacles, fading and signal reflections.
The system was able to successfully detect the correct artwork with an accuracy up 95% using the Distance-based Proximity Technique.