Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation
In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute (MDPI)
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114670/ http://psasir.upm.edu.my/id/eprint/114670/1/114670.pdf |
| _version_ | 1848866561609170944 |
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| author | Scavino, Edgar Abd Rahman, Mohd Amiruddin Farid, Zahid Ahmad, Sadique Asim, Muhammad |
| author_facet | Scavino, Edgar Abd Rahman, Mohd Amiruddin Farid, Zahid Ahmad, Sadique Asim, Muhammad |
| author_sort | Scavino, Edgar |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and safety reasons. Moreover, satellite-based Global Positioning System (GPS) signals are likely to be unusable in deep indoor spaces, and technologies like WiFi and Bluetooth are susceptible to signal noise and fading effects. For these reasons, a hybrid approach that employs at least two different signal typologies proved to be more effective, resilient, robust, and accurate in determining localization in indoor environments. This paper proposes an improved hybrid technique that implements fingerprinting-based indoor positioning using Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points and Wireless Sensor Network (WSN) technology. Six signals were recorded on a regular grid of anchor points covering the research surface. For optimization purposes, appropriate raw signal weighing was applied in accordance with previous research on the same data. The novel approach in this work consisted of performing a virtual tessellation of the considered indoor surface with a regular set of tiles encompassing the whole area. The optimization process was focused on varying the size of the tiles as well as their relative position concerning the signal acquisition grid, with the goal of minimizing the average distance error based on tile identification accuracy. The optimization process was conducted using a standard Quantum Particle Swarm Optimization (QPSO), while the position error estimate for each tile configuration was performed using a 3-layer Multilayer Perceptron (MLP) neural network. These experimental results showed a 16% reduction in the positioning error when a suitable tile configuration was calculated in the optimization process. Our final achieved value of 0.611 m of location incertitude shows a sensible improvement compared to our previous results. |
| first_indexed | 2025-11-15T14:22:34Z |
| format | Article |
| id | upm-114670 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:22:34Z |
| publishDate | 2024 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1146702025-01-22T08:10:59Z http://psasir.upm.edu.my/id/eprint/114670/ Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation Scavino, Edgar Abd Rahman, Mohd Amiruddin Farid, Zahid Ahmad, Sadique Asim, Muhammad In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and safety reasons. Moreover, satellite-based Global Positioning System (GPS) signals are likely to be unusable in deep indoor spaces, and technologies like WiFi and Bluetooth are susceptible to signal noise and fading effects. For these reasons, a hybrid approach that employs at least two different signal typologies proved to be more effective, resilient, robust, and accurate in determining localization in indoor environments. This paper proposes an improved hybrid technique that implements fingerprinting-based indoor positioning using Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points and Wireless Sensor Network (WSN) technology. Six signals were recorded on a regular grid of anchor points covering the research surface. For optimization purposes, appropriate raw signal weighing was applied in accordance with previous research on the same data. The novel approach in this work consisted of performing a virtual tessellation of the considered indoor surface with a regular set of tiles encompassing the whole area. The optimization process was focused on varying the size of the tiles as well as their relative position concerning the signal acquisition grid, with the goal of minimizing the average distance error based on tile identification accuracy. The optimization process was conducted using a standard Quantum Particle Swarm Optimization (QPSO), while the position error estimate for each tile configuration was performed using a 3-layer Multilayer Perceptron (MLP) neural network. These experimental results showed a 16% reduction in the positioning error when a suitable tile configuration was calculated in the optimization process. Our final achieved value of 0.611 m of location incertitude shows a sensible improvement compared to our previous results. Multidisciplinary Digital Publishing Institute (MDPI) 2024-07-25 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114670/1/114670.pdf Scavino, Edgar and Abd Rahman, Mohd Amiruddin and Farid, Zahid and Ahmad, Sadique and Asim, Muhammad (2024) Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation. Algorithms, 17 (8). art. no. 326. pp. 1-15. ISSN 1999-4893; eISSN: 1999-4893 (Submitted) https://www.mdpi.com/1999-4893/17/8/326 10.3390/a17080326 |
| spellingShingle | Scavino, Edgar Abd Rahman, Mohd Amiruddin Farid, Zahid Ahmad, Sadique Asim, Muhammad Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation |
| title | Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation |
| title_full | Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation |
| title_fullStr | Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation |
| title_full_unstemmed | Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation |
| title_short | Enhancing indoor positioning accuracy with WLAN and WSN: a QPSO hybrid algorithm with Surface tessellation |
| title_sort | enhancing indoor positioning accuracy with wlan and wsn: a qpso hybrid algorithm with surface tessellation |
| url | http://psasir.upm.edu.my/id/eprint/114670/ http://psasir.upm.edu.my/id/eprint/114670/ http://psasir.upm.edu.my/id/eprint/114670/ http://psasir.upm.edu.my/id/eprint/114670/1/114670.pdf |