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Obtaining Distance from RSSI

RSSI is the signal strength at the Bluetooth receiver. The signal type, for example, iBeacon, Eddystone or sensor beacon is irrelevant. The value of the RSSI can be used to infer distance.

The accuracy of the distance measurement depends on many factors such as the type of sending device used, the output power, the capability of the receiving device, obstacles and importantly the distance of the beacon from the receiving device.

The output power isn’t known to the receiver so it’s sometimes added to the advertising data in the form of the ‘measured power’ which is the power at 1m from the sender.

The closer the beacon is to the receiver, the more accurate the derived distance. As our article mentions, projects that get more detailed location derived from RSSI, usually via trilateration and weighted averages, usually achieve accuracies of about 5m within the full range of the beacon or 1.5m within a shorter range confined space.

Some beacons, mainly those with output RF amplifiers, transmit more than 50m and in these cases, while the beacon can be detected, using RSSI to infer distance isn’t usually reliable due to noise (variation) in the RSSI value.

There’s some Android Java code on GitHub if you want to experiment with extracting distance from RSSI. There’s an equation for iOS on GitHub.

Need more help? Consider a Feasibility Study.

Beacons that flash/vibrate at a given distance.

Passive Indoor People Counting Using Bluetooth LE

The new paper Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning, proposes a method for counting people in indoor spaces using Bluetooth Low Energy (BLE) signals and deep learning techniques. The goal is to offer a privacy-preserving, device-free, and non-intrusive solution for occupancy monitoring in environments where camera use is inappropriate, such as hospitals and laboratories.

The method relies on analysing how human presence distorts BLE signals, particularly their Received Signal Strength Indicator (RSSI). Unlike traditional camera-based or wearable solutions, this approach does not require people to carry any devices. BLE beacons emit signals that, when passing through or reflecting off human bodies, become altered in predictable ways. These signal deformations are then analysed using deep neural networks to estimate the number of occupants.

Five deep learning models were evaluated: Dense Neural Network (DenseNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid CNN+LSTM model, and a Transformer-based model. Both classification and regression approaches were tested. The hybrid CNN+LSTM model consistently outperformed the others in terms of accuracy and mean absolute error.

A key strength of the method is its flexibility and efficiency in new environments. The model is pre-trained on a large, varied dataset, and only requires a brief fine-tuning session with a small sample of data from the new location. In some cases, the model could even interpolate occupancy values it was not explicitly trained on. This means that with minimal setup time, the system can be deployed effectively in a range of environments, achieving accuracies of over 96%, and in some configurations even exceeding 99%.

The authors also developed a comprehensive data preprocessing and filtering strategy to account for signal noise and variability caused by human movement and the BLE protocol’s frequency hopping. They configured BLE beacons to transmit on fixed channels to maintain consistency in RSSI measurements.

In conclusion, the proposed BLE-based passive people counting system demonstrates high adaptability, accuracy, and practicality for real-time occupancy monitoring, with notable advantages over existing BLE and even some WiFi-based solutions. However, it still requires some calibration in each new environment due to limitations in generalising across different room geometries. Future work aims to develop a model that can generalise without this fine-tuning step.

Using Bluetooth Scans as a Proxy for Social Presence

The paper titled Bluetooth-sensed social presence is associated with immediate vigor and delayed fatigue: A multi-method time series analysis investigates the emotional impact of social presence as detected through Bluetooth on individuals in real-world settings. The research used experience sampling over two weeks with 80 participants and more than 123,000 Bluetooth scans.

The researchers employed Bluetooth scanning via the ESMira mobile app to passively detect nearby devices, using this as a proxy for measuring social presence. Participants initiated a Bluetooth scan lasting 60 seconds during each prompt, typically four times daily. The average number of Bluetooth devices detected per scan was approximately 34, with a large range and a median of 10. This method provided an unobtrusive and continuous means to quantify social proximity without disrupting natural behaviour.


The Bluetooth-derived device count (log-transformed as logNBT) was shown to correlate strongly with participants’ self-reported number of nearby people, validating its use as a reliable proxy for social presence. This was reinforced by a linear mixed-effects model which showed Bluetooth counts were a significant predictor of the number of people perceived within a 2-metre radius.

In analysing emotional responses, the study found that increased Bluetooth-detected social presence was associated with immediate rises in reported vigour and drops in dejection, but also predicted increased fatigue several hours later. This delayed fatigue effect is interpreted through the lens of social allostasis theory, suggesting that social exposure, while initially invigorating, incurs a delayed emotional cost.

Bluetooth device count was also found to influence state transitions: higher social presence increased the likelihood of transitioning from a vigorous to a fatigued state, and reduced perceived situational control. This reinforces the notion of a regulatory cost tied to prolonged social exposure.

Limitations of the Bluetooth method were acknowledged. While the device count strongly correlates with reported social proximity, it may be affected by technical artefacts, such as signal detection through walls or individuals carrying multiple devices. Moreover, it cannot differentiate the quality or type of social interaction, nor the interpersonal distance, which may have different emotional implications. The authors suggest that future research might integrate Bluetooth with more precise sensors or sociometric badges to address these limitations.

In conclusion, Bluetooth scanning proved to be an effective, low-burden method for capturing social presence in natural settings.

Continuous Transmission in Asset Tracking

Continuous transmission is a fundamental distinction between Bluetooth-based systems and traditional RFID or barcode scanning systems in asset tracking solutions. Unlike RFID or barcode methods, which rely on individual scans that can easily be missed, especially when performed manually, Bluetooth beacons emit signals at regular intervals. This regularity means that updates on an asset’s presence and location are ongoing, rather than sporadic. It also enables more accurate reporting, as the absence of a Bluetooth signal can reliably indicate that an asset has genuinely gone missing.

In reality, beacons do not transmit continuously in the literal sense. Instead, they emit signals periodically, with intervals ranging from a few hundred milliseconds to several seconds or even minutes. The frequency of these transmissions affects both power consumption and the immediacy of data in the reporting system. More frequent transmissions provide more up-to-date information but consume more power, which can be a critical consideration in battery-operated devices.

To balance power usage and data accuracy, some simpler systems opt for a transmission interval of around ten seconds. Additionally, certain advanced beacons are equipped with motion sensors that trigger extra updates when movement is detected, thereby providing more dynamic and context-aware tracking.

Mobile Device Power Saving Proximity Beacons

New research (pdf) introduces a method to reduce battery consumption in mobile devices, particularly small, wearable computing devices like smartwatches, rings, or glasses, by using proximity beacons instead of traditional satellite-based location tracking.

Typically, these devices use GNSS (Global Navigation Satellite Systems) such as GPS, which consume significant power due to the effort required to detect and process weak satellite signals. This is especially problematic for small devices with limited battery capacity. The proposed method embeds low-power proximity beacons, such as Bluetooth trackers (e.g., AirTags), into wearable components like watch bands. These beacons emit Bluetooth signals detected by nearby devices, which can then use their own GNSS services to triangulate the location and report it via existing networks like Apple’s Find My or Android’s equivalent.

The system allows dynamic association of the mobile device with new beacons. If a user switches out a watch band, for instance, the device can recognise which beacons are moving with it and prompt the user to reassign the new beacons for location tracking. This flexibility helps maintain tracking without user intervention in most cases.

In scenarios where beacons or networks are unavailable, such as remote locations, the device may still fall back on traditional GNSS. However, in populated areas with ample nearby devices, the reliance on GNSS can be minimised, significantly conserving battery life.

The document also outlines user control features, allowing manual pairing or unpairing of beacons, enabling/disabling location tracking based on time, place, or battery level, and even letting a device query a location network for its own location. This approach not only reduces power consumption but also extends tracking functionality even when the device’s battery is depleted, as the beacons can continue to transmit signals.

The technique provides an efficient and user-friendly alternative to power-intensive GNSS location services, enhancing practicality and longevity for small wearable devices while retaining robust location tracking capabilities.

Do You Have an Estimote Alternative?

Several companies have informed us that Estimote is no longer responding to enquiries about purchasing their beacons. It appears they are now more focused on promoting and selling their UWB (Ultra Wideband) tags instead.

This shift is unfortunate for companies that have integrated their solutions with Estimote’s beacons, SDK, and platform. Estimote’s approach, which involves manufacturing their own custom hardware and restricting their platform to only work with their own beacons, means there is no direct replacement for their older products.

However, if your application doesn’t rely on the Estimote SDK and simply detects iBeacon advertisements using the standard Bluetooth libraries available in iOS and Android, then you are not tied to Estimote’s hardware. In such cases, you are free to use any iBeacon. For more details, please refer to our post regarding beacon compatibility.

Read about the advantages of generic beacons

Small, Long Battery Life, Long Range Beacon

It’s surprising how many enquires we get asking for small beacons that also have a long battery life and long range. Such things don’t exist. Long range and long battery life need more battery power and hence the battery size needs to be larger. This implies a larger beacon.

You need to choose what’s most important for your project. If you need a very small device then the E8 is only 5mm thick but has a small CR2032 battery. If battery life and range are more important then look at the larger battery beacons and long range beacons. If you have the possibility of mains (USB) power then you might consider a USB beacon that can be small.

Browser Support for Web Bluetooth

Web Bluetooth is an API that enables websites to communicate with nearby Bluetooth devices via the Generic Attribute Profile (GATT). Its support varies significantly across browsers and platforms with Apple support particularly non-existent.

Supported Browsers

BrowserSupported Versions/PlatformsNotes
Google Chrome56+ (Desktop, Android, Chrome OS)Fully supported on Chrome for Android; requires Android 6.0+ and Windows 10 1703+.
Microsoft Edge79+Fully supported.
Opera43+ (Desktop, Android)Not working on latest desktop versions; works on Android since version 46.
Android Browser97+Fully supported.
Samsung Internet6.2+Fully supported.
Opera Mobile64+Fully supported.

Not Supported

BrowserNotes
Mozilla FirefoxNo supported on any version, including Firefox for Android.
Apple SafariNo supported on any version, including iOS and macOS.
Internet ExplorerNo supported on any version.

iOS Workarounds

  • Bluefy Web BLE Browser: A third-party app for iOS that enables Web Bluetooth functionality by acting as a browser supporting the API.

Matching Industrial Assets and Their Operators

Recent research looks into a new method for linking industrial assets, like power tools, with their operators using low-power Internet of Things (IoT) devices based on Bluetooth Low Energy (BLE). Despite the wide adoption of asset-tracking technologies, there’s still no widely used solution for dynamically identifying which worker is using which tool. This is important for improving safety, maintenance, and asset utilisation, especially in complex and fast-paced environments like construction sites.

The proposed system includes wearable devices for workers and beacons tags attached to tools. These beacons broadcast data about their usage status, which is picked up by the wearables. The system uses signal strength (RSSI) to estimate the proximity between workers and tools. Since RSSI data is noisy and imprecise, an Extended Kalman Filter (EKF) is employed to improve distance estimation accuracy. A cloud-based algorithm then analyses this data to identify the most probable asset-user matches.

The researchers implemented and tested this system using prototypes in both indoor and outdoor construction settings. The system achieved a median distance estimation error of 0.49 metres and up to 98.6% accuracy in matching tools to their users. The devices were optimised for low energy consumption: wearable devices could run for nearly a month on a single charge, and tags could last for years on small batteries.

The study concludes that the proposed system is a viable and scalable solution for enhancing digitalisation in industrial environments, particularly construction.

Filtering Beacons at Bluetooth Gateways

When using Bluetooth gateways, it is usual to reduce the number of Bluetooth devices that are sent to the server. This is because gateways do not just pick up the beacons you intend to monitor. They can also detect smartphones, vehicles, fitness trackers and a range of other Bluetooth devices. Although it is possible to ignore unwanted beacons at the server side, filtering them out at the gateway itself is a much more efficient approach. Doing this helps to reduce network bandwidth usage and places less strain on your server, allowing it to provide greater throughput, from multiple gateways, and just handle the meaningful data.

Another key advantage of filtering at the gateway is that it simplifies debugging and ongoing support. When there are fewer devices being reported, there is less data to sift through, making it quicker and easier to identify and solve any issues that may arise.

Most Bluetooth gateways offer the option to configure a whitelist based on advertising patterns. This allows you to instruct the gateway to block other devices before the data ever reaches your server. The challenging part lies in identifying exactly what a beacon is advertising. To tackle this, we recommend scanning for your Bluetooth beacons using a smartphone app that captures the raw advertising packets. Our blog post goes into greater detail about scanning for beacons and explains some of the nuances.

Once you have captured and analysed the advertising data, you can use the findings to set up an appropriate whitelist pattern on your gateway.