An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology
Access points (APs) are used to define coordinates in an indoor positioning system with Wi-Fi. These systems utilize existing infrastructure and Wi-Fi APs to find out the exact location of a device based on its RSSI and MAC address. The accuracy of these devices usually depends on the number of APs...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682301102X |
| _version_ | 1827370677874720768 |
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| author | Aparna Raj Sujala D. Shetty C.S. Rahul |
| author_facet | Aparna Raj Sujala D. Shetty C.S. Rahul |
| author_sort | Aparna Raj |
| collection | DOAJ |
| description | Access points (APs) are used to define coordinates in an indoor positioning system with Wi-Fi. These systems utilize existing infrastructure and Wi-Fi APs to find out the exact location of a device based on its RSSI and MAC address. The accuracy of these devices usually depends on the number of APs located nearby and the environment in which they are deployed. Therefore, the ideal selection of these points increases the discernibility of the localization technique. The rapid development of metaheuristic algorithms in recent years has demonstrated their effectiveness in resolving challenging optimization issues. The primary research goal is to investigate how to enhance indoor localization accuracy with metaheuristic algorithms and to assess the efficacy of positioning using these methods. In this paper, we propose a novel optimization algorithm called the Improved Pathfinder Algorithm (IPFA) using metaheuristic hybridization, where our contribution is twofold. The IPFA's superiority in optimization is used to choose the important APs. Subsequently, to maintain the generality of the localization performance, we created a feature-based classification model for the chosen AP subsets. Two prominent benchmark datasets, UJIIndoorLoc and JUIndoorLoc, were used to test the proposed framework. The proposed Indoor Localization framework attained an accuracy of 98.26% with a mean absolute error (MAE) of approximately 0.79 m. The results demonstrate that the IPFA method is capable of accurately locating the position with minimal positioning errors. |
| first_indexed | 2024-03-08T10:20:06Z |
| format | Article |
| id | doaj.art-be0095393cb44eb198c1cd78679d7e22 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-08T10:20:06Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-be0095393cb44eb198c1cd78679d7e222024-01-28T04:19:36ZengElsevierAlexandria Engineering Journal1110-01682024-01-01876376An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodologyAparna Raj0Sujala D. Shetty1C.S. Rahul2Department of Computer Science, Bits-Pilani, Dubai Campus, United Arab Emirates; Corresponding author.Department of Computer Science, Bits-Pilani, Dubai Campus, United Arab EmiratesSchool of Mathematics & Computer Science, IIT Goa, IndiaAccess points (APs) are used to define coordinates in an indoor positioning system with Wi-Fi. These systems utilize existing infrastructure and Wi-Fi APs to find out the exact location of a device based on its RSSI and MAC address. The accuracy of these devices usually depends on the number of APs located nearby and the environment in which they are deployed. Therefore, the ideal selection of these points increases the discernibility of the localization technique. The rapid development of metaheuristic algorithms in recent years has demonstrated their effectiveness in resolving challenging optimization issues. The primary research goal is to investigate how to enhance indoor localization accuracy with metaheuristic algorithms and to assess the efficacy of positioning using these methods. In this paper, we propose a novel optimization algorithm called the Improved Pathfinder Algorithm (IPFA) using metaheuristic hybridization, where our contribution is twofold. The IPFA's superiority in optimization is used to choose the important APs. Subsequently, to maintain the generality of the localization performance, we created a feature-based classification model for the chosen AP subsets. Two prominent benchmark datasets, UJIIndoorLoc and JUIndoorLoc, were used to test the proposed framework. The proposed Indoor Localization framework attained an accuracy of 98.26% with a mean absolute error (MAE) of approximately 0.79 m. The results demonstrate that the IPFA method is capable of accurately locating the position with minimal positioning errors.http://www.sciencedirect.com/science/article/pii/S111001682301102XIndoor localizationMetaheuristic hybridizationFeature selectionRSSISmart phone usersOptimization |
| spellingShingle | Aparna Raj Sujala D. Shetty C.S. Rahul An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology Alexandria Engineering Journal Indoor localization Metaheuristic hybridization Feature selection RSSI Smart phone users Optimization |
| title | An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology |
| title_full | An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology |
| title_fullStr | An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology |
| title_full_unstemmed | An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology |
| title_short | An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology |
| title_sort | efficient indoor localization for smartphone users hybrid metaheuristic optimization methodology |
| topic | Indoor localization Metaheuristic hybridization Feature selection RSSI Smart phone users Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S111001682301102X |
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