Indoor localization using Wi-Fi
Nowadays, the need for indoor localization is increasing as it has many possible implementations in many sectors, e.g. navigation, health care, etc. In order to obtain an accurate indoor location, fingerprinting is the most commonly used method. In this method, data is collected to build a fingerpri...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138070 |
| _version_ | 1824456407590633472 |
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| author | Siswanto, Hadi |
| author2 | Lee Bu Sung, Francis |
| author_facet | Lee Bu Sung, Francis Siswanto, Hadi |
| author_sort | Siswanto, Hadi |
| collection | NTU |
| description | Nowadays, the need for indoor localization is increasing as it has many possible implementations in many sectors, e.g. navigation, health care, etc. In order to obtain an accurate indoor location, fingerprinting is the most commonly used method. In this method, data is collected to build a fingerprint database during offline phase, and during online phase, unknown data is compared to the data in the database to estimate the location. The problem with fingerprinting approach is the high variation of RSSI values which could result in erroneous location estimation. Machine learning approach is a new alternative to fingerprinting approach that aims to solve this problem. This report provides a comparison of potential machine learning models for a classification problem of the grid-based indoor localization. All models are evaluated using data collected from several reference points on block N4, North Spine, Nanyang Technological University. A simple Android application is developed to assist data collection. Understanding the RSSI data, data preprocessing and feature selection techniques were applied to clean outliers and unimportant features to improve the accuracy of the prediction models. The results show that random forest classifier is the overall best choice for classification-based indoor localization, with up to 93% accuracy. An Android application is also developed to illustrate the usage of indoor localization. A map and the user’s estimated location will be shown in the application, and the user is able to get a navigation from their location to any point of interest inside the area of interest. |
| first_indexed | 2025-02-19T03:53:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138070 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:53:37Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1380702020-04-23T05:01:04Z Indoor localization using Wi-Fi Siswanto, Hadi Lee Bu Sung, Francis School of Computer Science and Engineering Singtel ebslee@ntu.edu.sg Engineering::Computer science and engineering Nowadays, the need for indoor localization is increasing as it has many possible implementations in many sectors, e.g. navigation, health care, etc. In order to obtain an accurate indoor location, fingerprinting is the most commonly used method. In this method, data is collected to build a fingerprint database during offline phase, and during online phase, unknown data is compared to the data in the database to estimate the location. The problem with fingerprinting approach is the high variation of RSSI values which could result in erroneous location estimation. Machine learning approach is a new alternative to fingerprinting approach that aims to solve this problem. This report provides a comparison of potential machine learning models for a classification problem of the grid-based indoor localization. All models are evaluated using data collected from several reference points on block N4, North Spine, Nanyang Technological University. A simple Android application is developed to assist data collection. Understanding the RSSI data, data preprocessing and feature selection techniques were applied to clean outliers and unimportant features to improve the accuracy of the prediction models. The results show that random forest classifier is the overall best choice for classification-based indoor localization, with up to 93% accuracy. An Android application is also developed to illustrate the usage of indoor localization. A map and the user’s estimated location will be shown in the application, and the user is able to get a navigation from their location to any point of interest inside the area of interest. Bachelor of Engineering (Computer Science) 2020-04-23T05:01:04Z 2020-04-23T05:01:04Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138070 en SCSE19-0372 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Siswanto, Hadi Indoor localization using Wi-Fi |
| title | Indoor localization using Wi-Fi |
| title_full | Indoor localization using Wi-Fi |
| title_fullStr | Indoor localization using Wi-Fi |
| title_full_unstemmed | Indoor localization using Wi-Fi |
| title_short | Indoor localization using Wi-Fi |
| title_sort | indoor localization using wi fi |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/138070 |
| work_keys_str_mv | AT siswantohadi indoorlocalizationusingwifi |