There are lots of brands of iBeacon and Eddystone beacon. Each brand has its own management app. We have often been asked,
“Is it possible to have just one app to manage different brands of beacon?”
While it’s technically possible, there’s no incentive for anyone to create such an app. Creating just one app to manage one beacon brand, across iOS and Android is significant effort in itself.
Google identified this problem and created the Eddystone Configuration GATT Service. The idea is that if manufacturers used just this, apps and beacons would be inter-operable. However, people want to configure iBeacon as well as Eddystone. Manufacturers also want to allow users to configure and read sensor data. Also, using Eddystone Configuration GATT Service software in all future beacons does nothing to help manage the large number of beacons that are already out there.
As of writing this, in 10 years since Eddystone Configuration GATT Service was published, no apps have been published that work with the Eddystone Configuration GATT Service. However, the Nordic nRF Connect app does understand some of the Bluetooth Characteristics to better read these kinds of beacons. There hasn’t been a rush for manufacturers to use Eddystone Standard GATT.
Back to the question. It looks like there will be a separate app per manufacturer for the foreseeable future.
Distance is determined on the receiving device rather than by the beacon itself. Devices such as smartphones are able to detect the signals transmitted by nearby beacons. When a beacon emits its Bluetooth radio signal, the receiving device measures the received signal strength indicator (RSSI), which can then be used to estimate how far the device is from the beacon.
Within a range of a few metres, changes in RSSI are usually noticeable enough to allow the distance to be estimated with reasonable accuracy. As the distance grows, however, these changes become much smaller and less reliable. This means the system can usually identify whether a beacon is nearby or further away, but calculating the exact distance becomes much more difficult.
For instance, Apple’s iOS programming interface CoreBluetooth categorises detected beacon signals into broad proximity groups such as ‘immediate’, ‘near’, and ‘far’. Rather than providing a precise measurement in metres or feet, these categories simply indicate the general closeness of the beacon.
The maximum detection range varies depending on the beacon hardware, but signals can often be picked up from around 50 metres and sometimes up to 100 metres. At these longer distances, however, the RSSI value becomes much less useful for estimating exact distance and instead only suggests whether the beacon is relatively close or relatively distant.
The year-wise analysis shows a steady growth in research output over the decade. Publications increased from 4.60 per cent in 2015 to a peak of 14.27 per cent in 2024, indicating rising academic interest, with only a slight dip in 2023. Articles form the dominant document type, accounting for 89.53 per cent of the total, followed by review articles at 9.90 per cent, while other formats such as book chapters, editorials and data papers contribute only marginally.
Research is concentrated primarily in Engineering Electrical and Electronic (32.11 per cent), Telecommunications (23.02 per cent) and Computer Science Information Systems (21.29 per cent), highlighting the technology-driven and interdisciplinary nature of beacon research.
Country-wise distribution reveals that China leads with 28.31 per cent of publications, followed by the United States at 17.72 per cent and South Korea at 9.67 per cent. Other notable contributors include Spain, India, Italy and England. Among journals, Sensors publishes the highest number of papers, followed by IEEE Access and Electronics.
Overall, the study concludes that beacon technology research has expanded consistently over the past decade, is heavily concentrated in engineering and computer science disciplines, and is significantly driven by Chinese institutions, researchers and funding bodies, with strong international collaboration and growing global interest.
New research presents RELoc, a WiFi fingerprinting indoor localisation framework designed to work reliably in multi-floor buildings where conventional 2D approaches often struggle because they cannot properly resolve vertical (between-floor) ambiguity. The method combines Recursive Feature Elimination with Cross-Validation (RFECV) to select the most informative WiFi access point signals, and an Extremely Randomised Trees regressor to predict positions as either 2D coordinates or full 3D coordinates including floor information. The trees model is tuned using Bayesian hyperparameter optimisation via Optuna’s Tree-structured Parzen Estimator, with the aim of improving accuracy while keeping computation manageable for practical deployments.
The authors evaluate RELoc on two public datasets, SODIndoorLoc and UTSIndoorLoc, and report that the 2D version achieves mean absolute errors of 1.84 m (SODIndoorLoc) and 4.39 m (UTSIndoorLoc). When floor level is incorporated for 3D prediction, performance improves markedly compared with the corresponding 2D setup, reducing error by about a third on SODIndoorLoc and by just over a quarter on UTSIndoorLoc, which the paper attributes to 3D modelling’s ability to separate locations that look similar in WiFi signal strength in the horizontal plane but lie on different floors. Across both datasets, the reported results show RELoc outperforming a range of baseline machine learning, ensemble, and deep learning methods, while also training faster than heavier neural approaches in their experiments.
The paper concludes that combining cross-validated feature selection with an efficient tree ensemble and automated tuning produces a strong balance of accuracy and computational efficiency for multi-floor indoor positioning.
Beacons allow you to set the transmit power to levels such as -30dBm, -20dBm, -16dBm, -12dBm, -8dBm, -4dBm, 0dBm and +4dBm. The number of actual setting values depends on the beacon. 0dbm is the default power recommended for normal use. Our article on Choosing the Transmitted Power explains these values and how they relate to distance.
We are often asked ‘What are the Estimated Distance/s for Tx Powers?’. This depends on the beacon, the environment and the receiver. An analogy is someone shouting a word. How loud does someone have to shout to be heard a certain distance? It depends on how clear the person shouts, how much noise there is and how well the person listening can hear. With beacons it depends on the beacon (mainly antenna) design, how much radio frequency (RF) noise there is, the degree of RF reflections, the receiving ability of the device (smartphone or gateway) you are using and even the weather.
The only way to determine the relationship between distance and power is experimentally and it will likely change over time as the environment changes.
A new paper addresses a core limitation of beacon-based indoor localisation systems, unstable and noisy RSSI measurements that degrade distance estimation and, by extension, location accuracy. The work focuses on Bluetooth Low Energy beacon deployments as a primary use case, alongside Wi-Fi and Zigbee, within Indoor Spatial Temporal Systems that rely on trilateration from RSSI values .
In Bluetooth beacon systems, RSSI values fluctuate significantly indoors due to multipath effects, attenuation from walls and furniture, human movement and device interference. The paper shows that conventional approaches, where RSSI filtering is performed in the cloud using Kalman or adaptive Kalman filters, introduce unacceptable latency for real-time beacon applications such as indoor navigation, proximity detection and safety monitoring. Moving filtering closer to the beacons at the edge reduces transmission delay but introduces a new problem. Adaptive filters such as robust self-adaptive Kalman filters are computationally expensive and unsuitable for resource-constrained edge devices commonly used with Bluetooth beacons .
To address this, the authors propose a lightweight edge-Kalman Filter designed specifically for noisy RSSI streams like those produced by Bluetooth beacons. Instead of continuously updating noise parameters, the filter only updates when a statistically significant change is detected between consecutive RSSI windows using density ratio estimation. This reduces unnecessary computation while still responding to real environmental changes, making it well suited to beacon receivers such as Raspberry Pis or mobile devices operating at the edge .
Experimental results using multiple Bluetooth beacon datasets show that the proposed approach substantially reduces distance estimation error compared with raw RSSI, standard Kalman filtering and robust self-adaptive Kalman filtering. In beacon scenarios with real-world noise, the edge-based approach achieves lower mean squared error while requiring fewer computations, which directly improves responsiveness and quality of service for Bluetooth beacon applications. The paper also demonstrates that cleaner, filtered beacon RSSI significantly improves downstream machine learning models for indoor location prediction, increasing classification accuracy to near-perfect levels in tested scenarios .
This question comes up a lot. You might have read that beacons can be used to pop up notifications. Such a mechanism, called Google Nearby Notifications, existed prior to October 2018 after which it was discontinued.
Today, there are two ways to cause beacons to trigger notifications:
A new paper investigates how transmission power can be adaptively controlled in Bluetooth Low Energy communications to improve reliability while reducing energy consumption in dynamic Internet of Things environments. It focuses on the complex relationship between received signal strength, data throughput, transmission power and overall system power consumption, all of which are strongly influenced by environmental variability such as interference, distance and multipath effects.
The authors first provide an extensive experimental analysis using Nordic BLE hardware to characterise how changes in transmission power affect signal strength, throughput and energy use across clean, moderately complex and highly noisy environments. The results show that increasing transmission power generally improves both RSSI and throughput, but at a significant energy cost, particularly when a front-end power amplifier is used. They also demonstrate that RSSI can be measured reliably at high update rates, whereas throughput measurements become increasingly noisy at high calculation frequencies due to the packet-based nature of BLE.
Building on these observations, the paper proposes a closed-loop transmission power control framework based on proportional–integral–derivative controllers implemented on the central device. Three strategies are developed and evaluated. The RSSI-based controller responds quickly to signal fluctuations and works well in clean or fast-changing environments, but it does not directly guarantee a desired data rate. The throughput-based controller directly targets application-level performance and performs better in complex environments, but its slower response and higher variability make it vulnerable to sudden disturbances.
To overcome the limitations of both approaches, the authors introduce a hybrid dual-loop controller that combines throughput-based control with fast RSSI regulation. In this design, the throughput loop sets an appropriate RSSI target, while a faster RSSI loop adjusts transmission power to maintain that target. Experimental results show that this hybrid method achieves more stable throughput with lower variance, maintains connectivity during abrupt signal degradation and delivers substantial energy savings compared with fixed maximum transmission power.
The study concludes that closed-loop, PID-based transmission power control is an effective way to balance reliability and energy efficiency in BLE systems. Among the evaluated strategies, the hybrid RSSI–throughput approach provides the best overall performance in dynamic and complex environments. Future work is proposed on extending the framework to multi-node and mesh networks and on using adaptive or learning-based techniques to further improve controller performance under mobility and varying traffic conditions.
We now have the M52-A Plus in stock. This is the same as the M52-Plus except that it also includes an accelerometer for motion triggered broadcasting.
The two main advertising channels can be iBeacon, Eddystone UID, Eddystone URL or user defined. The accelerometer has an adjustable threshold (32mg to 2032mg).
