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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏宏宇 | |
| dc.contributor.author | Che-Wei Hsu | en |
| dc.contributor.author | 許哲瑋 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:10:05Z | - |
| dc.date.available | 2021-01-21 | |
| dc.date.copyright | 2020-01-21 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2020-01-16 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66876 | - |
| dc.description.abstract | 在5G裡的極可靠且低延遲通訊(URLLC)和大規模機器通訊(mMTC)被視為能夠支援未來智慧工廠(FoF)的重要技術；而行動邊緣運算(MEC)則是另一個實現智慧工廠自動化的必備系統。在未來的智慧工廠裡，各種生產輔助機具以及環境監測裝置都將具備連網的能力，而由於這些裝置本身硬體上的限制，有些工作必須依靠邊緣運算甚至是雲端運算的輔助藉以完成。在頻譜資源以及運算資源有限的情況下，能否最佳化資源分配帶來更大的增益是一個重要的問題。在我們這篇論文裡，不同於以往單純研究資源分配和工作的分發問題，我們把智慧工廠物聯網內異質網路的傳輸特性也一併納入考慮。我們提出一個兩層的邊緣-雲端運算網路架構(MEC-cloud)，而身處在工廠內的智慧裝置可以有效率的分配自己的工作量，而適當的透過可靠的網路傳輸把部分的工作分發給邊緣及雲端運算系統。在此篇論文裡，我們提出一個兩步驟的演算法:基於機會成本的工作分發演算法(Opportunity-Cost Based Offloading Algorithm, OCBOA)，在工作分發的同時最佳化運算及通訊資源的使用，並且達到節省耗電和降低工作分發失敗的機率。實驗結果顯示我們的低運算複雜度的演算法能夠勝過其他基準對照演算法，並且能夠滿足智慧裝置的服務質量(QoS)。 | zh_TW |
| dc.description.abstract | The ultra-reliable and low latency communication(URLLC) and massive machine type communication (mMTC)in 5G are envisioned to support intelligent automation in the Factories-of-the-Future (FoF) environment; Mobile-edge computing (MEC) is thought of as a promising system for realization. In the future factory, production machines and environmental monitoring devices will be endowed with the capability to connect to Internet. Due to limited capability caused by hardware limitation, some works should be completed with the help of edge computing or even cloud computing. Under limited spectrum and computation resources, it is important to get gains from the resource optimization. In this work, rather than simply investigating task offloading problem, the radio transmission properties are jointly considered under heterogeneous industrial IoT networks. A 2-tier MEC-cloud framework is provided, wherein the IoT mobile devices (MDs) are able to partition tasks and offload them to the MEC and the cloud server through the reliable transmission. A two-step algorithm named opportunity-cost based offloading algorithm (OCBOA) is proposed to jointly optimize the allocation of communication and computation resources for task offloading with the minimum energy consumption and offloading failure probability. The experiments show that our low-complexity algorithm outperforms the other benchmark algorithms on resource allocation while satisfying the QoS requirements. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:10:05Z (GMT). No. of bitstreams: 1 ntu-108-R06921037-1.pdf: 3439927 bytes, checksum: d93da70328e9d6ca2f97838d8202a58b (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 摘要 iii
Abstract v 1 Introduction 1 1.1 Background 1 1.2 Related Works 3 1.3 Our Contributions 4 2 System Model 7 2.1 System Architecture 7 2.2 Offloading Paradigm 8 2.3 Modeling of Mobile Devices 9 2.4 Throughput Model of 5G Licensed Offloading 9 2.5 Throughput Model of 5G NRU on the Coexistence of WLAN Users 10 2.5.1 Introduction to 5G NRU 10 2.5.2 LAA and WiFi MAC Protocol Mechanisms 10 2.5.3 Coexistence Throughput Analysis of 5G NRU and WiFi Using Modified Bianchi Model 14 2.6 Energy Model of 5G Licensed and 5G NRU Offloading 18 2.6.1 Energy Consumption on 5G Licensed Offloading 18 2.6.2 Energy Consumption on 5G NRU Offloading 19 2.7 Latency Analysis of 5G Licensed and 5G NRU Offloading 20 2.7.1 Latency on 5G Licensed Offloading 20 2.7.2 Latency on 5G NRU Offloading 21 2.8 Utility Definition 21 3 Mathematical Problem Formulation 23 4 Proposed Solution 25 4.1 Resource Allocation under Heterogeneous Cellular Networks 26 4.1.1 OpportunityCostBased Offloading Algorithm (OCBOA) 26 4.1.2 Analysis and Discussion 28 4.2 Resource Allocation under Heterogeneous Networks with NRU coexisted with WLAN users 30 4.2.1 Enhanced OpportunityCostBased Offloading Algorithm (eOCBOA) 30 4.2.2 Analysis and Discussion 32 4.3 Implementation of the Proposed Solution 33 5 Performance Evaluation 35 5.1 System Setting 36 5.2 Simulation Results 40 5.2.1 Blocking Situations 40 5.2.2 Utility 42 5.2.3 Energy Consumption 43 5.2.4 Mean Average Precision 44 5.2.5 Performance under different α 47 6 Conclusion 49 | |
| dc.language.iso | en | |
| dc.subject | 未來工廠 | zh_TW |
| dc.subject | 工業物聯網 | zh_TW |
| dc.subject | 邊緣運算 | zh_TW |
| dc.subject | factories of the future | en |
| dc.subject | mobile--edge computing | en |
| dc.subject | industrial IoT network | en |
| dc.title | 低功耗且可靠的異質工業物聯網邊緣運算 | zh_TW |
| dc.title | Energy-Efficient and Reliable MEC Offloading for Heterogeneous Industrial IoT Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭瑞光,施美如,謝宏昀,周敬淳 | |
| dc.subject.keyword | 未來工廠,邊緣運算,工業物聯網, | zh_TW |
| dc.subject.keyword | factories of the future,mobile--edge computing,industrial IoT network, | en |
| dc.relation.page | 54 | |
| dc.identifier.doi | 10.6342/NTU202000154 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-01-16 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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