Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning
This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting techn...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2023/6777759 |
| _version_ | 1797848257150320640 |
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| author | Doan Perdana I Made Arya Indra Tanaya Abdul Aziz Marwan Fityanul Akhyar |
| author_facet | Doan Perdana I Made Arya Indra Tanaya Abdul Aziz Marwan Fityanul Akhyar |
| author_sort | Doan Perdana |
| collection | DOAJ |
| description | This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used. |
| first_indexed | 2024-04-09T18:25:43Z |
| format | Article |
| id | doaj.art-a776629707cf438490a95ba1b256d960 |
| institution | Directory Open Access Journal |
| issn | 2090-715X |
| language | English |
| last_indexed | 2024-04-09T18:25:43Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj.art-a776629707cf438490a95ba1b256d9602023-04-12T00:00:03ZengHindawi LimitedJournal of Computer Networks and Communications2090-715X2023-01-01202310.1155/2023/6777759Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep LearningDoan Perdana0I Made Arya Indra Tanaya1Abdul Aziz Marwan2Fityanul Akhyar3Advanced Creative Networks Research Center in Telkom UniversityPT. Bale Teknologi BaliDepartment of Electrical EngineeringIntelligence System LaboratoryThis article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used.http://dx.doi.org/10.1155/2023/6777759 |
| spellingShingle | Doan Perdana I Made Arya Indra Tanaya Abdul Aziz Marwan Fityanul Akhyar Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning Journal of Computer Networks and Communications |
| title | Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning |
| title_full | Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning |
| title_fullStr | Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning |
| title_full_unstemmed | Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning |
| title_short | Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning |
| title_sort | evaluation of a high accuracy indoor positioning system with wi fi time of flight tof and deep learning |
| url | http://dx.doi.org/10.1155/2023/6777759 |
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