Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach
In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to constru...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/24/9903 |
| _version_ | 1797379375533916160 |
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| author | Muhammed Zahid Karakusak Hasan Kivrak Simon Watson Mehmet Kemal Ozdemir |
| author_facet | Muhammed Zahid Karakusak Hasan Kivrak Simon Watson Mehmet Kemal Ozdemir |
| author_sort | Muhammed Zahid Karakusak |
| collection | DOAJ |
| description | In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≤</mo><mn>2.16</mn></mrow></semantics></math></inline-formula> m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.55</mn></mrow></semantics></math></inline-formula> m and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.97</mn></mrow></semantics></math></inline-formula> m with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>83.33</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>81.05</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability. |
| first_indexed | 2024-03-08T20:22:16Z |
| format | Article |
| id | doaj.art-a9fe41a1d2f8471a958fb6300bbdf728 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:22:16Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a9fe41a1d2f8471a958fb6300bbdf7282023-12-22T14:41:38ZengMDPI AGSensors1424-82202023-12-012324990310.3390/s23249903Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin ApproachMuhammed Zahid Karakusak0Hasan Kivrak1Simon Watson2Mehmet Kemal Ozdemir3Graduate School of Engineering and Natural Sciences, Istanbul Medipol University, 34810 Istanbul, TurkeyDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKDepartment of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UKDepartment of Computer Engineering, Istanbul Medipol University, 34810 Istanbul, TurkeyIn recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≤</mo><mn>2.16</mn></mrow></semantics></math></inline-formula> m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.55</mn></mrow></semantics></math></inline-formula> m and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.97</mn></mrow></semantics></math></inline-formula> m with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>83.33</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>81.05</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.https://www.mdpi.com/1424-8220/23/24/9903Internet of things (IoT)digital twinscyber-physical systems (CPSs)smart spaceindoor localizationwireless LAN positioning |
| spellingShingle | Muhammed Zahid Karakusak Hasan Kivrak Simon Watson Mehmet Kemal Ozdemir Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach Sensors Internet of things (IoT) digital twins cyber-physical systems (CPSs) smart space indoor localization wireless LAN positioning |
| title | Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach |
| title_full | Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach |
| title_fullStr | Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach |
| title_full_unstemmed | Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach |
| title_short | Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach |
| title_sort | cyber wise a cyber physical deep wireless indoor positioning system and digital twin approach |
| topic | Internet of things (IoT) digital twins cyber-physical systems (CPSs) smart space indoor localization wireless LAN positioning |
| url | https://www.mdpi.com/1424-8220/23/24/9903 |
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