New Algorithms Optimize BLE Beacon Planning in Buildings
Researchers at the Brandenburg University of Technology Cottbus-Senftenberg (BTU) have developed new methods for the automatic planning of Bluetooth Low Energy (BLE) beacon networks. The algorithms take building structure and radio signal attenuation into account and are designed to enable reliable indoor localization using fewer beacons.
Where Should BLE Beacons Be Installed?
BLE beacons can serve as fixed reference points for indoor localization within buildings. A mobile device receives signals from multiple beacons and uses them to estimate its position. In the architecture studied, distance is determined using RSSI, and position is calculated via trilateration. For this to work, each relevant point must be within range of at least three beacons simultaneously.
The real challenge, therefore, arises even before the system goes live: Where should the beacons be installed to ensure there are no coverage gaps, yet also avoid deploying an unnecessary amount of hardware?
To address this, the BTU researchers incorporate building characteristics such as walls, ceilings, glass surfaces, material types, and material thicknesses into their modeling. These factors influence signal attenuation and thus the expected range of the BLE signals.
Planning Software Replaces Manual Placement
In practice, the placement of beacons is often planned manually. This is time-consuming and cannot guarantee either complete triple coverage or the smallest possible number of beacons.
The team led by Sven Löffler, Viktoria Abbenhaus, George Assaf, and Petra Hofstedt therefore combines three optimization approaches:
Constraint Programming,
Large Neighborhood Search, and
Evolutionary Algorithms.
This new work builds on previous research by incorporating faster calculations of radio coverage, optimized parameters, and hybrid methods.
A key advancement concerns the calculation of beacon coverage. As soon as the maximum range derived from the RSSI model is exceeded, the calculation is terminated. In Large Neighborhood Search, existing coverage data is also updated incrementally rather than being completely recalculated. For parts of the method, this reduced the computational effort by about 80 percent.
All 29 Building Models Successfully Solved
The researchers tested the methods on 29 synthetically generated, multi-story building models. Depending on the approach, earlier constraint-based variants found solutions for only 27.6 to 93.1 percent of the buildings.
With the improved algorithms, the solution rate rose to 100 percent. The combination of constraint-based optimization and Large Neighborhood Search proved particularly successful. It reduced the average number of beacons required from 167 to 161 and achieved the best solution for 22 of the 29 test buildings.
Real Building Test at BTU
In addition, the method was applied to real floor plans of a three-story BTU building in Cottbus. For the modeled triple coverage, a total of 39 beacons were calculated, distributed across 13, 11, and 15 units per floor. BLE signals can, in some cases, pass through ceilings and also contribute to coverage of adjacent floors.
The authors emphasize that this does not yet constitute large-scale real-world validation. Together with the synthetic tests, however, the experiment provides initial evidence that the approach can also be applied to complex real-world buildings.
From Emergency Localization to Asset Tracking
Potential applications range from indoor navigation, personal safety, ticketing, and emergency response to asset tracking.
The architecture examined here is key: The fixed BLE beacons serve as reference points. BLE-enabled mobile devices such as smartphones, wearables, or other devices receive their signals and use them to determine their position. A classic RTLS architecture, in which a BLE tag transmits and fixed locators calculate the position, is not the subject of this research.
NFC & BLE Micro-Location Mobile Ticketing Solution
Integrating NFC and BLE technologies into a micro-location mobile ticketing system effectively streamlines fare management and enhances passenger experience in complex public transport networks.
For system integrators, the practical benefit therefore lies primarily in the automated planning of the wireless infrastructure. Even before installation, it is possible to model how many reference beacons are needed and where they should be installed to achieve the required three-dimensional coverage.
The open-access study “Advanced Algorithms for the Three-Dimensional Beacon Placement Problem Based on Constraint Programming, Large Neighborhood Search, and Evolutionary Methods” was published in SN Computer Science by Springer Nature.
Read the study on Springer Nature: https://link.springer.com/article/10.1007/s42979-026-05267-z