Beacon Signal Stability Observations

As previously mentioned, we perform signal strength and stability tests across beacons. The data feeds into our consultancy work. Here are some high level observations.

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 BluetoothLocationEngine™. 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).

Processing iBeacon RSSI Using AI Machine Learning

There’s new research from China on Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network.

The project used extended Gaussian filtering to delete and filter significant abnormal data values caused by multipath radio noise indoors. A deep neural network was also used to fingerprint data.

The system resulted in a maximum error positional error of only 1.02m.

Using iBeacon for Team Management

There’s useful research by Sindhumol S of Cochin University of Science and Technology, India on Implementation and Analysis of a Smart Team Management System using iOS Devices as iBeacon.

The system provides location-based task monitoring and presence detection. Task details and announcements are available when a team member enters the range of an iBeacon broadcast. The system also provides typical project management facilities such as task allocation, notification, instant chat, status reports and employee logs.

From a technical perspective, the paper describes setting the beacon measure power, the affect of distance change on accuracy and the change in accuracy/RSSI depending on obstacle blocking.

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Finding the Nearest Beacon

There’s new research from Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia on Improved Bluetooth Low Energy Sensor Detection for Indoor Localization Services.

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.

Using Bluetooth RSSI for Visually Impaired Navigation

There’s new research from the University of Bristol on Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections.

The idea is to use Bluetooth received signal strength (RSSI) to enable the blind and visually impaired (BVI) to safely to cross intersections on foot. Audible systems already exist but users find them confusing when crossing complex road intersections. The researchers developed a system called CAS (Crossing Assistance System) that provides pedestrian positioning.

The system uses k-nearest neighbors (kNN) method Support Vector Machine (SVM) with various RSSI features for classification, including a moving average filter, that was able to localise people with 97.7% accuracy.

RSSI vs AoA Bluetooth Asset Tracking

In a previous post on iBeacon Microlocation Accuracy we explained how assets can be tracked using Bluetooth Received Signal Strength (RSSI) or Angle of Arrival (AoA). We advised working out what accuracy is needed prior to seeking out an appropriate solution. However, accuracy isn’t always the only consideration and here is a more complete list of factors.


RSSI asset tracking can achieve accuracies of about 1.5m within a shorter range confined space and 5m at the longer distances. RSSI zone-based systems where beacons are found to the nearest gateway, are accurate to the inter-gateway distance that can be of the order of cm. However having such as large gateway density is usually only practical for very small areas.

AoA asset tracking achieves sub-metre accuracy. The accuracy depends most on the distance between the locator and beacon but is also affected by the locator hardware quality, radio signal noise, surfaces causing radio reflections, the accuracy of locator placement and beacon orientation.

Maximum number of beacons

AoA-based asset tracking produces and requires much more data which means the locators and software systems have to deal with more data. The data throughput for both types of system depends on the required minimum latency that in-turn depends on how often the beacons advertise. RSSI-based systems support up to high tens of thousands of beacons while AoA supports thousands of beacons.

Beacon variety and IoT

RSSI-based systems can use any beacon and hence support a large range of sensor beacons that can detect movement (accelerometer), movement (started/stopped moving), button press, temperature, humidity, air pressure, light level, open/closed (magnetic hall effect), proximity (PIR), proximity (cm range), fall detection, smoke, natural gas and water leak.

AoA beacons are more specialised and currently only support limited IoT sensing such as movement (accelerometer) and button press.


AoA locators, gateways and beacons are more complex and are therefore more costly. AoA also needs more locators/gateways per sq area. Hence, AoA systems are x3 to x4 more expensive than RSSI systems.

Setup effort

The accuracy of AoA requires that locators be more carefully positioned than for RSSI, in particular the site and AoA locator positions need to be carefully measured.

Beaconzone supplies both RSSI and AoA systems. Contact us to determine the best type of system for your needs.

Using Bluetooth Mesh and Space, Time, Frequency Diversity to Improve Locating Accuracy

There’s new research from the Department of Electrical Engineering, University of North Texas, USA on Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications. The paper explains how received signal strength (RSSI) based solutions have accuracy limitations in radio multipath (radio reflective) environments. It describes a solution that improves accuracy using Bluetooth mesh and Bluetooth channel-based processing.

Bluetooth and WiFi Channels

A system was created that exploits the space, time, and frequency diversities in measurements. Different Bluetooth channels have different fading effects.

Advertising was modified to make it Bluetooth channel-aware to be able to differentiate the fading effects. It was possible to reduce the residual fitting errors in the path loss models by using a space-time-frequency diversity combining scheme.

The system was demonstrated using ESP32 BLE modules.

The system significantly reduced the residual linear regression fitting errors in path loss models. It was able to more accurately use RSSI to measure the distance between the transmitter and receiver. The researchers demonstrated it’s possible to implement the proposed multi-receiver configuration and the diversity combining scheme using commercial off-the-shelf standard BLE devices.

Probabilistic vs Neural Network iBeacon Positioning

There’s new research by ITMO University, Russia on the Implementation of Indoor Positioning Methods: Virtual Hospital Case. The paper describes how positioning can be used to discover typical pathways, queues and bottlenecks in healthcare scenarios. The researchers implemented and compared two ways to mitigate noise in Bluetooth beacon RSSI data.

The probabilistic and neural network methods both use past recorded data to compare with new data. This is known as fingerprinting. The neural network method is less complex when there’s need to scale to locating many objects. The researchers tested the methods at the outpatient department of the cardio medical unit of Almazov National Medical Research Centre.

Comparison of the methods showed they give approximately the same error of between 0.96m and 2.11m. However, the neural network-based approach significantly increased performance.

Detecting Proximity Using Bluetooth Beacons in Museums

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.

The RE.S.I.STO project allows media on the medieval town of Pisa to be accessible via smartphones and tablets. The system is implemented using the React Native Javascript Framework to allow cross-platform aps to be created on iOS and Android.

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.

RSSI time series of 5 tags

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.

Read about Determining Location Using Bluetooth Beacons

Bluetooth (BLE) vs Ultra-Wideband (UWB) for Locating

We previously mentioned how cost, battery life and second sourcing are the main advantages of Bluetooth over Ultra-Wideband (UWB). An additional, rarely mentioned, advantage is scalability.

Servers that process Bluetooth or Ultra-Wideband support a particular maximum throughout. The rate at which updates reach systems depends on the number of assets, how often they report and the area covered (number of gateways/locators). Each update needs to be processed and compared with very recent updates from other gateways/locators to determine an asset’s position.

For Bluetooth, updates tend to be of the order of 2 to 10 seconds but in some scenarios can be 30 seconds or more for stock checking where assets rarely move. Motion triggered beacons can be used to provide variable update periods depending on an asset’s movement patterns. This allows Bluetooth to support high 10s of thousands of assets without overloading the server.

For Ultra-Wideband, refresh rates tend to be of the order of hundreds of milliseconds (ms) thus stressing the system with more updates/sec. This is why most Ultra-Wideband systems support of the order of single digit thousands of assets and/or smaller areas. More frequent advertising is also the reason why the tags use a lot of battery power.

How does all this change with the new Bluetooth 5.1 direction finding standard? The standard was published in January 2019 but solutions have been slow to come to the market. The products that have so far appeared all have shortcomings that mean we can’t yet recommend them to our customers. Aside from this, in evaluating these products we are seeing compromises compared to traditional Bluetooth locating using received signal strength (RSSI).

Bluetooth 5.1 direction finding needs more complex hardware that, at least in current implementations, are reporting much more often. The server has to do complex processing to convert phase differences to angles and angles to positions thus supporting fewer updates/sec. Bluetooth direction finding is looking more like UWB in that cost, scalability and battery life are sacrificed for increased accuracy. Direction finding locators are currently x6 to x10 more costly than existing Bluetooth/WiFi gateways. Beacon battery life is reduced due to the more frequent and longer advertising. We are seeing Bluetooth 5.1 direction finding being somewhere between traditional Bluetooth RSSI-based locating and Ultra-Wideband in terms of flexibility vs accuracy.

Despite these intrinsic compromises, Bluetooth direction finding is set to provide strong competition to UWB for high accuracy applications. We are already seeing UWB providers seeking to diversify into Bluetooth to provide lower cost, longer battery life and greater scalability.