Position mapping using BLE mesh networks
(2026) EITM01 20261Department of Electrical and Information Technology
- Abstract
- Device location is a practical problem in indoor wireless deployments. Without knowing where devices are positioned in a building, technicians struggle to locate failing equipment, understand coverage issues, and perform maintenance on devices. To facilitate technicians, they often get elevated access to the intranetwork, which not only gives them access to everything, but they still fail to know where devices are located physically in the building. To fully solve this problem, an indoor positioning system is helpful, as it not only displays the devices’ locations in a building but also displays the statuses of the devices. This thesis explores the viability of creating such a system using Bluetooth 6.0 with mesh technology and utilizes... (More)
- Device location is a practical problem in indoor wireless deployments. Without knowing where devices are positioned in a building, technicians struggle to locate failing equipment, understand coverage issues, and perform maintenance on devices. To facilitate technicians, they often get elevated access to the intranetwork, which not only gives them access to everything, but they still fail to know where devices are located physically in the building. To fully solve this problem, an indoor positioning system is helpful, as it not only displays the devices’ locations in a building but also displays the statuses of the devices. This thesis explores the viability of creating such a system using Bluetooth 6.0 with mesh technology and utilizes three different distance measuring techniques called Phase-Based Ranging (PBR), Round-Trip Time (RTT), and Inverse Fast Fourier Transform (IFFT).
For the intended room-level installer support use case, meter-level accuracy is sufficient to identify the correct area rather than exact sub-meter coordinates. Across both Line of Sight (LOS) and Non-Line of Sight (NLOS) conditions, Inverse Fast Fourier Transform (IFFT) performs best, with a mean error of 1.40 m in LOS and 2.40 m in NLOS. In contrast, Phase-Based Ranging (PBR) performs worst with a mean error above 5.00 m for both conditions, while Round-Trip Time (RTT) often performs worse than 1.70 m. The degradation from LOS to NLOS is approximately 40% for PBR, 71% for IFFT, and 102% for RTT.
Based on these ranging results, IFFT is chosen for the indoor positioning system. The position map is then reconstructed using two algorithms,Multidimensional Scaling (MDS) and Semidefinite Programming (SDP). The error from IFFT is low enough for reconstruction attempts. Although the IFFT ranging accuracy is higher, reconstruction introduces additional errors in our experiments, SDP performs better with a mean topology error of 3.37 m, compared to 4.07 m for MDS. This increase in error relative to the IFFT input precision is influenced by multiple factors, especially the network topology and the number of devices. Likewise, the performance of each reconstruction algorithm depends heavily on the deployment environment, the number of devices, and node connectivity. It is therefore difficult to draw a general conclusion about which method is superior across all scenarios. (Less) - Popular Abstract
- Finding devices in a large office environment can be a difficult task without a map. Using the new Bluetooth 6.0 distance measurement capabilities and Bluetooth Low Energy (BLE) mesh technology, we can create a map of the devices in the office and their relative positions to each other.
In this thesis, we tested three ways of estimating distance between devices: phase-based ranging (PBR), round-trip time (RTT), and inverse fast Fourier transform (IFFT). The tests were performed both in line-of-sight and in non-line-of-sight office environments where walls, glass, and furniture block or distort signals. This matters because devices in reality aren’t placed in ideal conditions.
After comparing the methods, IFFT gave the most reliable... (More) - Finding devices in a large office environment can be a difficult task without a map. Using the new Bluetooth 6.0 distance measurement capabilities and Bluetooth Low Energy (BLE) mesh technology, we can create a map of the devices in the office and their relative positions to each other.
In this thesis, we tested three ways of estimating distance between devices: phase-based ranging (PBR), round-trip time (RTT), and inverse fast Fourier transform (IFFT). The tests were performed both in line-of-sight and in non-line-of-sight office environments where walls, glass, and furniture block or distort signals. This matters because devices in reality aren’t placed in ideal conditions.
After comparing the methods, IFFT gave the most reliable distance estimates overall, especially when measurements were filtered over multiple samples. We then used these distances to build a map of where devices were located relative to each other. Two reconstruction methods were evaluated: Multidimensional Scaling (MDS) and Semidefinite Programming (SDP). In our multi-device test, SDP produced the best overall position accuracy, while MDS gave somewhat better consistency for individual pairwise links.
Using Bluetooth Mesh to coordinate the devices for creating indoor positioning to support installers during installation and maintenance, without relying on external positioning infrastructure is a promising approach. For example, an installer could use the system to locate defective devices, verify if a speaker is likely mounted in the wrong corridor or grouped correctly.
The results show clear difficulties in accuracy in challenging non-line-of-sight situations, and reconstruction quality depends on network geometry and connectivity between nodes. Overall the approach is a promising and cost-effective solution with interesting future work, but further development is needed before its full potential can be realized. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9236897
- author
- Padrón, Ernst LU and Persson, Jonathan LU
- supervisor
-
- Kaan Bür LU
- organization
- course
- EITM01 20261
- year
- 2026
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- BLE, LOS, NLOS, Multidimensional Scaling, Semidefinite Programming, Channel Sounding, IPS, BLE Mesh
- report number
- LU/LTH-EIT 2026-1158
- language
- English
- id
- 9236897
- date added to LUP
- 2026-06-16 16:23:29
- date last changed
- 2026-06-16 16:23:29
@misc{9236897,
abstract = {{Device location is a practical problem in indoor wireless deployments. Without knowing where devices are positioned in a building, technicians struggle to locate failing equipment, understand coverage issues, and perform maintenance on devices. To facilitate technicians, they often get elevated access to the intranetwork, which not only gives them access to everything, but they still fail to know where devices are located physically in the building. To fully solve this problem, an indoor positioning system is helpful, as it not only displays the devices’ locations in a building but also displays the statuses of the devices. This thesis explores the viability of creating such a system using Bluetooth 6.0 with mesh technology and utilizes three different distance measuring techniques called Phase-Based Ranging (PBR), Round-Trip Time (RTT), and Inverse Fast Fourier Transform (IFFT).
For the intended room-level installer support use case, meter-level accuracy is sufficient to identify the correct area rather than exact sub-meter coordinates. Across both Line of Sight (LOS) and Non-Line of Sight (NLOS) conditions, Inverse Fast Fourier Transform (IFFT) performs best, with a mean error of 1.40 m in LOS and 2.40 m in NLOS. In contrast, Phase-Based Ranging (PBR) performs worst with a mean error above 5.00 m for both conditions, while Round-Trip Time (RTT) often performs worse than 1.70 m. The degradation from LOS to NLOS is approximately 40% for PBR, 71% for IFFT, and 102% for RTT.
Based on these ranging results, IFFT is chosen for the indoor positioning system. The position map is then reconstructed using two algorithms,Multidimensional Scaling (MDS) and Semidefinite Programming (SDP). The error from IFFT is low enough for reconstruction attempts. Although the IFFT ranging accuracy is higher, reconstruction introduces additional errors in our experiments, SDP performs better with a mean topology error of 3.37 m, compared to 4.07 m for MDS. This increase in error relative to the IFFT input precision is influenced by multiple factors, especially the network topology and the number of devices. Likewise, the performance of each reconstruction algorithm depends heavily on the deployment environment, the number of devices, and node connectivity. It is therefore difficult to draw a general conclusion about which method is superior across all scenarios.}},
author = {{Padrón, Ernst and Persson, Jonathan}},
language = {{eng}},
note = {{Student Paper}},
title = {{Position mapping using BLE mesh networks}},
year = {{2026}},
}