Supplementary open dataset for WiFi indoor localization based on received signal strength
Abstract Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-11-01
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| Series: | Satellite Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43020-022-00086-y |
| _version_ | 1828098585773735936 |
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| author | Jingxue Bi Yunjia Wang Baoguo Yu Hongji Cao Tongguang Shi Lu Huang |
| author_facet | Jingxue Bi Yunjia Wang Baoguo Yu Hongji Cao Tongguang Shi Lu Huang |
| author_sort | Jingxue Bi |
| collection | DOAJ |
| description | Abstract Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction. |
| first_indexed | 2024-04-11T08:04:37Z |
| format | Article |
| id | doaj.art-2eb71d19f85a4e4f88908f31007097d5 |
| institution | Directory Open Access Journal |
| issn | 2662-1363 |
| language | English |
| last_indexed | 2024-04-11T08:04:37Z |
| publishDate | 2022-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Satellite Navigation |
| spelling | doaj.art-2eb71d19f85a4e4f88908f31007097d52022-12-22T04:35:36ZengSpringerOpenSatellite Navigation2662-13632022-11-013111510.1186/s43020-022-00086-ySupplementary open dataset for WiFi indoor localization based on received signal strengthJingxue Bi0Yunjia Wang1Baoguo Yu2Hongji Cao3Tongguang Shi4Lu Huang5School of Surveying and Geo-Informatics, Shandong Jianzhu UniversitySchool of Environment and Spatial Informatics, China University of Mining and TechnologyKey Laboratory of Satellite Navigation System and Equipment Technology, The 54th Research Institute of China Electronics Technology Group CorporationSchool of Surveying and Geo-Informatics, Shandong Jianzhu UniversitySchool of Surveying and Geo-Informatics, Shandong Jianzhu UniversityKey Laboratory of Satellite Navigation System and Equipment Technology, The 54th Research Institute of China Electronics Technology Group CorporationAbstract Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction.https://doi.org/10.1186/s43020-022-00086-yWiFiIndoor localizationOpen datasetRSSAPMachine learning |
| spellingShingle | Jingxue Bi Yunjia Wang Baoguo Yu Hongji Cao Tongguang Shi Lu Huang Supplementary open dataset for WiFi indoor localization based on received signal strength Satellite Navigation WiFi Indoor localization Open dataset RSS AP Machine learning |
| title | Supplementary open dataset for WiFi indoor localization based on received signal strength |
| title_full | Supplementary open dataset for WiFi indoor localization based on received signal strength |
| title_fullStr | Supplementary open dataset for WiFi indoor localization based on received signal strength |
| title_full_unstemmed | Supplementary open dataset for WiFi indoor localization based on received signal strength |
| title_short | Supplementary open dataset for WiFi indoor localization based on received signal strength |
| title_sort | supplementary open dataset for wifi indoor localization based on received signal strength |
| topic | WiFi Indoor localization Open dataset RSS AP Machine learning |
| url | https://doi.org/10.1186/s43020-022-00086-y |
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