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研究生:李慶祥
研究生(外文):Ching-Shiang Lee
論文名稱:應用行動邊界運算技術於低延遲物聯網閘道器之設計
論文名稱(外文):Mobile Edge Computing for Low LatencyIoT Gateway
指導教授:陳俊良陳俊良引用關係
指導教授(外文):Jiann-Liang Chen
口試委員:陳俊良
口試委員(外文):Jiann-Liang Chen
口試日期:2016-07-26
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:63
中文關鍵詞:物聯網第五代行動通訊行動邊界運算軟體定義網路網路功能虛擬化
外文關鍵詞:Internet of Things5th generation of mobile networksMobile Edge ComputingSoftware-Defined NetworkingNetwork Function Virtualization
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隨著物聯網(Internet of Things, IoT)技術與應用服務的崛起,許多設備將訊息資料透過網路傳送至雲端,例如:感測器、穿戴裝置等。還有各種類型的無線感知網路(Wireless Sensor Network, WSN),例如:橋梁監控、土石流監控、海嘯監控、地震警報、車聯網、自動駕駛和智慧車等應用,這都驗證了低延遲和即時性服務需求的重要性。促使物聯網環境的狀況及裝置設備需更容易被掌控與管理,並藉由網路連線至雲端進行大數據分析,提供更智能的生活應用服務。
根據愛立信研究預測,到了2021年全世界會有超過280億的裝置與雲端資料中心互聯,其中有100億是電腦、手機、平板電腦等,還有其他150億裝置是物聯網終端物件。感測器傳送資料給物聯網應用服務器,將產生大量的資料,當這些資料透過物聯網相結合時,可預期在物聯網中所流通的資料量勢必非常龐大,且必定會耗費且占用非常大量的網路運輸資源。因此當物聯網進行資訊傳送時,若發生急迫性的服務需求時,必須具備能即時性傳遞資訊的傳輸服務品質(Quality of Service, QoS),並且必須針對不同的使用者或不同的需求,提供相對應的服務。
第五代行動通訊(Fifth generation of mobile networks, 5G)網路架構導入軟體定義網路(Software-Defined Networking, SDN)與網路功能虛擬化(Network Function Virtualization, NFV)技術,建立一個具有靈活彈性的網路和巨量物聯網終端環境。5G網路提供更高的資料傳輸速度,更聰明資源調度方法,將可滿足不同客群用戶的應用需求。隨著物聯網應用多元發展,整合5G網路技術和巨量物聯網終端裝置進行服務聯結過程,透過閘道器(Gateway)進行流量排程(Scheduling)與解析機制,將有效地提升服務品質。閘道器也扮演負責收集物聯網資料裝置,透過有線和無線不同通訊介面,讓物聯網感測器連接至雲端網路,進行整合資料分析功能。閘道器扮演的角色將更多元,使其能夠擴展到各種不同的應用環境裡。
本研究中,採用Linux開發板建立物聯網閘道器,利用Open source軟體與硬體平台快速建立網路虛擬化平台,並根據行動邊界運算技術(Mobile edge computing, MEC)的概念,建立分流控制(Traffic Offload Function, TOF)和無線網路信息服務(Radio Network Information Service, RNIS)兩種應用程式的功能建立在嵌入式開發板上,藉由即時偵測網路流量與無線環境,達到改善物聯網用戶體驗(Quality of Experience, QoE)及減少網路壅塞現象。虛擬化平台使用以應用程式為中心的輕量虛擬化技術,稱為容器技術(Container),其容器映像檔與虛擬機器映像檔相比,容器映像檔佔用空間較小,更容易透過網路傳輸到任何環境上進行部署,並增強應用服務平台的配置管理,更快速將物聯網應用服務建立於此虛擬化平台上,有效降低物聯網提供服務平均約30 %的Latency,使得物聯網應用有更好的服務品質。
Internet of Things (IoT) grows up rapidly with technology and applications, such as sensors or wearable devices from network to cloud computing platform. The IoT applications involve Wireless Sensor Network (WSN), bridges monitoring, landslides monitoring, earthquake monitoring and tsunami alerts, telematics, autonomous car, intelligent vehicles, etc, demonstrating the importance of low latency and real-time demand for services, providing more services that are intelligent.
According to Ericsson’s research forecast, there will be over 28 billion devices worldwide interconnecting with the cloud data center by 2021, of which 10 billion devices are expected to be computers, smartphones, tablets, etc, and the other huge amount of 15 billion devices will be seen in IoT objects. When data is being transmitted in an IoT platform, there should be a real-time mechanism with different Quality of Service (QoS) support for the urgent request for services.
The next generation of 5G network, implementing Software-Defined Networking (SDN) and Network Function Virtualization (NFV) technologies, establishes a flexible and resilient network in line with various IoT devices. With the diverse development of networking applications, integrating 5G network technologies with IoT devices for application process scheduling and analysis methods via gateways, will effectively enhance the QoS, Gateway devices are also responsible for collecting information on the IoT platform and can connect sensors to cloud computing platform for integrating data analysis.
In this paper, we propose the Mobile Edge Computing (MEC) with networking gateways that integrate with open source software and hardware platform to build up NFV. Two applications of MEC for Traffic Offload Function (TOF) and Radio Network Information Service (RNIS) establish in the Linux embedded system, with real-time detection of network traffic and wireless environments. IoT applications will improve the user Quality of Experience (QoE) and reduce network congestion phenomenon significantly. The NFV platform that drives lightweight application centric with virtualization technology is called Container technology. It can enhance configuration management in IoT application platform, and effectively reducing about 30 % of latency of IoT services. It indeed can provide a better QoS on IoT applications.
摘要 I
Abstract IV
目錄 VI
圖目錄 VIII
表目錄 X
第一章 緒論 1
1.1 研究動機 1
1.2 研究貢獻 2
1.3 研究架構 4
第二章 知識背景 5
2.1 物聯網 5
2.2 5G網路 7
2.3 容器技術 11
2.4 行動邊界運算技術 12
第三章 系統架構 17
3.1 系統架構 17
3.2 系統操作與架構 18
3.3 流量控制機制 19
第四章 系統設計與效能分析 25
4.1 情境環境 25
4.2 系統設計 26
4.3 效能分析 41
第五章 結論與未來研究方向 48
5.1 結論 48
5.2 未來研究方向 49
參考文獻 50
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