Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus)
Asset tracking is an important technology as it allows businesses to track and manage valuable assets. A key aim of this paper is to explore the efficacy of Bluetooth Low Energy (BLE) based asset tracking in an indoor environment. This paper will examine how to convert the received signal strength i...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166068 |
| _version_ | 1824453651309002752 |
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| author | Li, Jefferson Zheng Jun |
| author2 | Oh Hong Lye |
| author_facet | Oh Hong Lye Li, Jefferson Zheng Jun |
| author_sort | Li, Jefferson Zheng Jun |
| collection | NTU |
| description | Asset tracking is an important technology as it allows businesses to track and manage valuable assets. A key aim of this paper is to explore the efficacy of Bluetooth Low Energy (BLE) based asset tracking in an indoor environment. This paper will examine how to convert the received signal strength indicator (RSSI) values from BLE devices to said devices’ position within a fixed arena. A neural network model will be built additionally to evaluate these RSSI values, which often fluctuate and therefore hard to make sense of. The experimental results show significant improvements over past works with the inclusion of neural network models. This project will end off with a live demonstration to demonstrate the useability of this paper’s findings under real-world conditions. |
| first_indexed | 2025-02-19T03:09:48Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166068 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:09:48Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1660682023-04-21T15:36:47Z Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus) Li, Jefferson Zheng Jun Oh Hong Lye School of Computer Science and Engineering hloh@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Asset tracking is an important technology as it allows businesses to track and manage valuable assets. A key aim of this paper is to explore the efficacy of Bluetooth Low Energy (BLE) based asset tracking in an indoor environment. This paper will examine how to convert the received signal strength indicator (RSSI) values from BLE devices to said devices’ position within a fixed arena. A neural network model will be built additionally to evaluate these RSSI values, which often fluctuate and therefore hard to make sense of. The experimental results show significant improvements over past works with the inclusion of neural network models. This project will end off with a live demonstration to demonstrate the useability of this paper’s findings under real-world conditions. Bachelor of Engineering (Computer Science) 2023-04-20T08:49:24Z 2023-04-20T08:49:24Z 2023 Final Year Project (FYP) Li, J. Z. J. (2023). Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166068 https://hdl.handle.net/10356/166068 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Li, Jefferson Zheng Jun Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus) |
| title | Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus) |
| title_full | Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus) |
| title_fullStr | Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus) |
| title_full_unstemmed | Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus) |
| title_short | Bluetooth low energy (BLE) based asset tagging system (BLE RSSI finger printing focus) |
| title_sort | bluetooth low energy ble based asset tagging system ble rssi finger printing focus |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/166068 |
| work_keys_str_mv | AT lijeffersonzhengjun bluetoothlowenergyblebasedassettaggingsystemblerssifingerprintingfocus |