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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳瑞北(Ruey-Beei Wu) | |
| dc.contributor.author | Poh Yuen Chan | en |
| dc.contributor.author | 陳保源 | zh_TW |
| dc.date.accessioned | 2023-03-20T00:02:03Z | - |
| dc.date.copyright | 2022-08-23 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-12 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86541 | - |
| dc.description.abstract | 本論文旨在實現Wi-Fi被動室內定位系統（IPS），無需待測物（DUT）安裝額外應用程序，也無需用戶主動協作。 Wi-Fi 偵測器 （Sniffers）部署在實驗區域，以掃描和收集 DUT 的 Wi-Fi 接收信號強度（RSSI）作為 Wi-Fi 指紋，應用加權k最近鄰域（WKNN）方法獲得DUT位置。為了每個RP可以接收的Wi-Fi RSSI最大化，本文使用具有增強信息熵特徵的目標函數，利用改進基因演算法（GA）優化Wi-Fi偵測器的部署，實驗上使用Wi-Fi測量機器人在每個 RP 上自動收集Wi-Fi 2.4 GHz和 5 GHz RSSI 數據。初步結果表明，僅使用 20 個 Wi-Fi Sniffer 作為模型訓練的特徵，離線定位精度可以達到可接受範圍為 2.2 m。此外，在 NTU 中實施了概念驗證的真實在線被動 IPS，以顯示檢測 DUT 在線存在並隨後獲得其 RSSI作為位置估計的在線指紋的可能性。 | zh_TW |
| dc.description.abstract | This thesis focused on the realisation of Wi-Fi based passive indoor positioning system (IPS) without any additional application installed on the device-under-target (DUT) and without active collaboration from the user. The Wi-Fi Sniffers are deployed in an area of interest to scan and collect DUT’s Wi-Fi received signal strength (RSIS) as Wi-Fi fingerprints for mapping vectors of RSSI to each reference point (RP) in the physical world by applying the weighted k-nearest neighbourhood (WKNN) method. To maximise the Wi-Fi RSSI that can be received in each RP, optimisation of the deployment of Wi-Fi Sniffers is considered using a modified Genetic Algorithm (GA) for an objective function with the enhanced feature of information entropy. Automate data collection of RSSI at each RP is done using a surveying robot for both Wi-Fi 2.4 GHz and 5 GHz. Preliminary result shows that the off-line positioning accuracy can achieve 2.2 m which is acceptable with only 20 Wi-Fi Sniffers as features for model training. In addition, a proof-of-concept real on-line passive IPS is implemented in NTU to show the possibility of detecting the on-line presence of DUT and subsequently obtain its RSSI as on-line fingerprints for position estimation. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-20T00:02:03Z (GMT). No. of bitstreams: 1 U0001-0908202201552400.pdf: 4417262 bytes, checksum: 61172b6b69f79be7e80dc8284490d922 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENTS i 摘要 ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii CHAPTER 1 : INTRODUCTION 1 1.1. Introduction 1 1.2. Wi-Fi Sensing for Indoor Positioning 2 1.3. Motivation 5 1.4. Main Contributions 6 1.5. Organisation of the Thesis 6 CHAPTER 2 : LITERATURE REVIEW 8 2.1 Wireless Sensing Modalities for Indoor Positioning 8 2.1.1 Device-free Passive (DfP) 8 2.1.2 Wi-Fi 10 2.1.3 Fusion System 11 2.2 Algorithms for Indoor Positioning 12 2.2.1 Ranging Technique 13 2.2.1.1 Trilateration 13 2.2.1.2 Triangulation 15 2.2.2 Scene Analysis 15 2.2.2.1 Deterministic 16 2.2.2.2 Probabilistic 17 2.2.2.3 Pattern Recognition 17 2.3 Passive Positioning System 17 CHAPTER 3 : RESEARCH METHODOLOGY 21 3.1 Project Overview 21 3.2 Simulation of Wi-Fi RSSI Using iBwave Wi-Fi Suite 22 3.3 Optimised Placement of Wi-Fi Sniffers Using GA with Entropy 25 3.4 Overview of Hardware Used in The Passive System 32 3.5 Design of Passive Wi-Fi MAC Address Extraction System 34 CHAPTER 4 : RESULTS AND DISCUSSION 38 4.1 Introduction 38 4.2 Automate Wi-Fi Fingerprints Training Data Extraction System 38 4.3 Positioning Algorithm 41 4.4 Positioning Accuracy with Simulated and Off-line Measured Data 43 4.5 On-line Passive IPS Demonstration 47 CHAPTER 5 : CONCLUSIONS AND FUTURE WORK 52 5.1 Conclusions 52 5.2 Future Work 52 REFERENCES 54 APPENDIX 58 | |
| dc.language.iso | en | |
| dc.subject | Wi-Fi被動室內定位系統 | zh_TW |
| dc.subject | 物聯網 | zh_TW |
| dc.subject | Wi-Fi 指紋 | zh_TW |
| dc.subject | Wi-Fi偵測器 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | Wi-Fi被動室內定位系統 | zh_TW |
| dc.subject | 物聯網 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | Wi-Fi 指紋 | zh_TW |
| dc.subject | 接收信號強度 | zh_TW |
| dc.subject | 接收信號強度 | zh_TW |
| dc.subject | Wi-Fi偵測器 | zh_TW |
| dc.subject | Wi-Fi-based passive indoor positioning system | en |
| dc.subject | Internet of Things | en |
| dc.subject | Wi-Fi Sniffer | en |
| dc.subject | received signal strength | en |
| dc.subject | Wi-Fi fingerprints | en |
| dc.subject | Genetic Algorithm | en |
| dc.subject | Internet of Things | en |
| dc.subject | Wi-Fi-based passive indoor positioning system | en |
| dc.subject | Wi-Fi Sniffer | en |
| dc.subject | received signal strength | en |
| dc.subject | Wi-Fi fingerprints | en |
| dc.subject | Genetic Algorithm | en |
| dc.title | 在 Wi-Fi Monitor模式下通過Sniffers使用 RSSI 指紋進行被動室內定位 | zh_TW |
| dc.title | Passive Indoor Positioning using RSSI Fingerprinting via Sniffers in Wi-Fi Monitor Mode | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張盛富(Sheng-Fuh Chang),王垂堂(Chuei-Tang Wang),曾祥峻(Jeans Tseng),賴怡吉(Alexander I-Chi Lai) | |
| dc.subject.keyword | 物聯網,Wi-Fi被動室內定位系統,Wi-Fi偵測器,接收信號強度,Wi-Fi 指紋,基因演算法, | zh_TW |
| dc.subject.keyword | Internet of Things,Wi-Fi-based passive indoor positioning system,Wi-Fi Sniffer,received signal strength,Wi-Fi fingerprints,Genetic Algorithm, | en |
| dc.relation.page | 59 | |
| dc.identifier.doi | 10.6342/NTU202202179 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-08-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-08-23 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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