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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 蔡志宏(Zse-hong Tsai) | |
dc.contributor.advisor | 蔡志宏(Zse-hong Tsai | ztsai@ntu.edu.tw | ), | |
dc.contributor.author | Chen-Han Chung | en |
dc.contributor.author | 鍾承翰 | zh_TW |
dc.date.accessioned | 2023-03-19T22:18:17Z | - |
dc.date.copyright | 2022-09-26 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-09-19 | |
dc.identifier.citation | V. A. Q Nguyen, “Study on Study on Realtime Control System in IoT Based Smart Factory Interference Awareness Architectural Elements and Its Application,” Seventh International Conference on Information Science and Technology, April 16-19, 2017 R. C. Rodrigues, G. R. Mateus, and A. A. F. Loureiro, “On the design and capacity planning of a wireless local area network,” proc. IEEE/IFIP Network Operations abd Management Symp. 2000, pp.335-348 Apr.2000. Y. Lee, K. Kim, and Y. Choi, “Optimization of AP Placement and Channel Assignment in Wireless LANs,” in Proceedings of the 27th Annual IEEE Conference on Local Computer Networks, pp.831-836, Nov. 2002. M. Kamenetsky, M. Unbehaun, “Coverage Planning for Outdoor Wireless LAN Systems,” proc. International Zurich Seminar on Broadband Communications, pp.1-6, Feb. 2002. J. A. Park et al., “Analysis of Spectrum Channel Assignment for IEEE 802.11b Wireless LAN,” proc. 5th International Symposium on Wireless Personal Multimedia Communications, vol.3, pp.1073-1077, Oct. 2002 K. Sui et al., “Understanding the Impact of AP Density on WiFi Performance Through Real-World Deployment,” proc. 2016 IEEE International Symposium on Local and Metropolitan Area Networks, pp. 1-6, 2016 J. Pérez-Romero et al., “On Modeling Channel Selection in LTE-U as a Repeated Game,” IEEE Wireless Commun. Network. Conf. Doha, Qatar, Apr. 2016 T. Vanhatupa, M. Hännikäinen, T. D. Hämäläinen. “Genetic Algorithm to Optimize Node Placement and Configuration for WLAN Planning,” proc. 2007 4th International Symposium on Wireless Communication Systems, pp. 612-616, IEEE, 2007 S. Qiu et al., “Joint Access Point Placement and Power-Channel-Resource-Unit Assignment for IEEE 802.11ax-Based Dense WiFi Network with QoS Requirements,” IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 06-09 Jul, 2020 IEEE, “IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications,” IEEE Std 802.11-1997, 1997 V. Maglogiannis et al., “Cooperation Techniques between LTE in Unlicensed Spectrum and Wi-Fi towards Fair Spectral Efficiency,” Sensors, vol. 17, no. 9, p.1994, 2017 H. A. Omar, K. Abboud, N. Cheng, “A Survey on High Efficiency Wireless Local Area Networks: Next Generation WiFi,” IEEE Communication Survey & Tutorials, Vol. 18, No. 4, Fourth Quarter, 2016 A. M. Sweedy et al., “The Effect of Frame Length, Fragmentation and RTS/CTS mechanism on IEEE 802.11 MAC performance,” in Proc. 10th International Systems Design and Applications (ISDA), Cairo, Egypt, pp. 1338-1344, 2010. 中華民國交通部, “頻率供應計畫” 5 月,2015 Available at: http://www.motc.gov.tw/uploaddowndoc?file=bulletin/201505041349391.pdf&filedisplay=%E9%A0%BB%E7%8E%87%E4%BE%9B%E6%87%89%E8%A8%88%E7%95%AB.pdf&flag=doc Wi-Fi 6E Whitepaper Available at: https://www.litepoint.com/wp-content/uploads/2020/06/Wi-Fi-6E-Whitepaper-060220-web.pdf Tp-link Available at: https://www.tp-link.com/tw/home-networking/cloud-camera/ TECHDesign Available at: https://blog.techdesign.com/automated-guided-vehicles-agvs-types-definition-solutions/?utm_source=Twitter&utm_medium=daily+post P. Nain et al, “Properties of Random Direction Models,” Proceedings of IEEE 24th Annual Joint Conference of the IEEE Computer of Communications Societys. Mar, 2005 B. Sklar, F. J. Harris, Digital communications: fundamentals and applications, vol 2001. Prentice-hall Englewood Cliffs, NJ 1988 J. Zhang, X. Jia, Z. Zheng, and Y. Zhou, “Minimizing cost of placement of multi-radio and multi-power-level access points with rate aDRAOtation in indoor environment,” IEEE Transactions on wireless communications, vol. 10, no. 7, pp. 2186-2195, 2011 Wi-Fi Location-Based Services 4.1 Design Guide, Available at: https: //www.cisco.com/c/en/us/td/docs/solutions/Enterprise/Mobility/WiFi LBS-DG/wifich5.html, 2014. C. F. Lin, “Optimizing the Number of Connected Devices in the Industrial WiFi Environment,” 碩士論文,國立台灣大學圖書資訊學研究所,2017。https://hdl.handle.net/11296/juks4v D. J. Deng, S. Y. Lien, J. Lee, and K. C. Chen, “On quality-of-service provisioning in IEEE 802.11 ax WLANs,” IEEE Access, vol. 4, pp. 6086- 6104, 2016. R. Jain et al., “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems,” Technical Report Digital Equipment Corporation, DEC-TR-301 (1984) M. Schulz et al., “Simulation Based Decision Support for Future 300mm Automated Material Handling,” Proceedings of the Winter Simulation Conference, Jan 2001 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84630 | - |
dc.description.abstract | 最近幾年來,興建智慧工廠成為自動化生產的一個重要方向。為了使得資料傳輸與控制信號可以有效且可靠,很多網路問題的解決使利用IEEE 802.11ax (WiFi 6)的技術來實現。在802.11ax的網路架構下可以解決許多關於資訊傳遞以及兩台或多台裝置之間的溝通的網路問題。 在此篇論文中,我們提出了一個分散式的演算法,此演算法之設計是為了解決資源分配以及試著最佳化系統效能。在分散式演算法的前提下,每一台AP都有自己運行的演算法來達成系統的最佳化而非受到中央系統下指令控制。每台AP要分配給底下裝置的資源分別有頻道與傳輸能量。另外,我們設定了兩種模式: 一為資源分配最佳化模式,另一為公平性模式。此兩種模式在我們回合制的運行過程中會依據整體效能來評估下一回合應該使用何種模式。當系統效能有過門檻時會選擇資源分配最佳化模式,而當系統效能不佳時會選擇公平性模式。資源分配模式會週期性地改善整體資源分配,而公平性模式會把整體系統的公平性提升。 最後,我們所提出的演算法會透過模擬來呈現及分析。我們設定了四種環境,沒有障礙物、有對稱性的障礙物、有不對稱性的障礙物以及最後會依據智慧工廠的一些特性來模擬真實的環境。本篇所提出的演算法會拿來與隨機佈建法與循序漸進佈建法比較,並在模擬結束時分析各個環境下各個演算法的各種表現狀況。 | zh_TW |
dc.description.abstract | In recent years, the smart factory has become an important direction for manufacturing automation. In order to transmit data and control signals reliably within a smart factory, many network solutions have emerged and IEEE 802.11ax based WiFi has been found to be a promising information and communication technology for such Cyber-Physical System (CPS). This thesis proposed a distributed auto-configuration and resource allocation algorithm in which each AP can operate on its own, instead of relying on a control center giving all instructions to APs. The main contribution of this algorithm is its capability to optimally allocate key resources of APs, including frequency channel, transmitting power, AP placement location. Furthermore, it allows two different modes to cope with two types of network conditions: one case with QoS qualified for most APs, and the case in which QoS levels are not satisfied for certain APs. For the first mode, the algorithm can periodically improve network throughput and reducing interference; while for the second mode, QoS fairness can be adjusted to a better level. In the final result, the proposed system is demonstrated via simulation and it is shown that cross AP interferences are greatly reduced and the total throughput is improved under a dense WiFi network, comparing to legacy network designs. The resource allocation approach of the proposed system is also found promising for various types of smart factory environments. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T22:18:17Z (GMT). No. of bitstreams: 1 U0001-0709202220252400.pdf: 3882523 bytes, checksum: 4c3a2fc199966c9aa69177065db3b2c6 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Background 1 1.2 Literature Review 2 1.3 Motivation and Problem Statement 6 1.4 Thesis Organization 7 Chapter 2 System Architecture 8 2.1 WiFi Operating Mechanism 8 2.2 WiFi Frequency Channel 10 2.3 Types of Smart Factory Devices 12 2.3.1 The QoS compatibility problem for difference types of devices 13 2.4 Traffic Environment in Dense WiFi 15 2.5 Mathematical Model 16 2.5.1 Shannon capacity 16 2.5.2 Interference model 17 2.5.3 Channel Conflict Indicator (CCI) 19 2.5.4 Mobility model 21 Chapter 3 Distributed Resource Allocation and Optimization Algorithm 23 3.1 Operation Principle 23 3.2 State Machine in DRAO Implementation 25 3.2.1 AP State 25 3.2.2 Device State 27 3.2.3 Definition of Operation Conditions 29 3.2.4 Actions of AP 30 3.2.5 Actions of Device 31 3.2.6 Example of State Transitions 33 3.3 DRAO Algorithm 34 3.3.1 Overall Framework 34 3.3.2 Initialization 35 3.3.3 Resource optimization mode 39 3.3.4 Fairness mode 40 3.4 Performance Measurement 42 3.4.1 Number of Active APs 42 3.4.2 Predicted Maximum Total Throughput 42 3.4.3 Fairness Index 43 Chapter 4 Simulation and Performance Evaluation 45 4.1 Simulation Parameter 45 4.2 Simulation Environment 46 4.3 Random Walk with Mobility Model 50 4.4 Simulation Flow Chart 54 4.5 Number of Lost Devices versus Timer 56 4.6 Random Allocation and Sequential Allocation 57 4.7 Simulation Results 60 4.7.1 Simulations in No Obstacle Environment 64 4.7.2 Simulations in Symmetric Obstacle Environment 67 4.7.3 Simulations in Asymmetric Obstacle Environment 70 4.7.4 Simulations in Real Factory Layout Environment 73 4.7.5 QoS Analysis 76 Chapter 5 Conclusion and Future Work 78 5.1 Conclusion 78 5.2 Future Work 78 REFERENCE 80 | |
dc.language.iso | en | |
dc.title | 智慧工廠802.11ax高密度網路之資源分配與存取點設置 | zh_TW |
dc.title | On Access Point Deployment and Resource Allocation for 802.11ax-based Dense WiFi in Smart Factories | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林風(Phone Lin),鍾耀梁(Yao-Liang Chung) | |
dc.subject.keyword | IEEE 802.11ax,無限存取點,服務品質,資源分配,擁擠網路環境,分散式系統,智慧工廠, | zh_TW |
dc.subject.keyword | IEEE 802.11ax,access point,quality of service,resource allocation,dense WiFi,distributed system,smart factory, | en |
dc.relation.page | 82 | |
dc.identifier.doi | 10.6342/NTU202203234 | |
dc.rights.note | 同意授權(限校園內公開) | |
dc.date.accepted | 2022-09-19 | |
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 | 2022-09-26 | - |
顯示於系所單位: | 電信工程學研究所 |
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