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  • 學位論文

邊緣計算於施工監控系統應用之研究

Research on Application of Edge Computing in Construction Monitoring System

指導教授 : 蔡明修
共同指導教授 : 王人牧

摘要


隨著物聯網(Internet of things,IoT) 的快速發展,施工過程通常被稱為非常複雜的過程,需要大量的自動化。引入物聯網設備,有利於現場施工團隊的工作時間得到有效的利用;有利於減少施工現場的事故,同時保障工人的安全;也有利於控制周遭環境,保障周圍居民的權益。即使物聯網有那麼多改善現場狀況的優點,但是卻一直難以大規模的部署,難以體現物聯網本身的價值。 邊緣計算(Edge computing)作為近兩年物聯網趨勢的榜首,可以有效地緩解雲計算平臺的資料處理的負擔,提高了資料處理的效率。同時,由於設備與邊緣更貼近邊緣側,極大的降低了資料傳輸的延時性,解決了資料延時性的問題。邊緣計算只是優化使用場景,為物聯網在施工現場的大規模部署增加更多的可能性。 針對上述提到的物聯網技術和邊緣計算技術,本研究認為,邊緣計算結合物聯網的方式去開發物聯網系統會有利於物聯網系統更好的在施工現場部署,所以本研究以施工現場的場景為例設計物聯網監控系統,並對其在現地部署的問題進行探討彙整,於此同時設計出邊緣計算物聯網框架設計物聯網監控系統,同樣對其在現地部署進行探討彙整。綜上所述,本研究以解決物聯網在施工現場部署出現的問題,探索邊緣計算是否有助於施工環境部署物聯網系統為目的,針對多個施工場景,藉此通過實驗來驗證。 為證明本開發想法之可行性與適用性,本研究將針對建立兩個系統,設計一個對比實驗,將資料完整性進行對比,從而更好的去分析用邊緣計算框架去設計一個物聯網系統,是否有利於促進物聯網在施工現場的部署,從統計學的角度去分析,通過符合施工現場的環境設計,從斷線率和信號強度兩個方面去評估對照組和實驗組的性能最後得出結論證明邊緣計算框架的運用相比于傳統物聯網模式更加適合於施工環境,邊緣計算的應用有利於物聯網系統更好地融入施工現場,發揮出最大的價值。

並列摘要


With the rapid development of the Internet of things (IoT), the construction process is often referred to as a very complex process that requires a great deal of automation. The introduction of IoT devices facilitates the effective use of the working time of the construction team on site, reducing accidents on the construction site while safeguarding the safety of workers. It also helps to control the surrounding environment and protect the rights of the surrounding residents. Even though IoT has so many advantages to improve the field conditions, it has been difficult to deploy on a large scale, and it is difficult to reflect the value of IoT itself. Edge computing, the top IoT trend in the past two years, can effectively relieve the burden of data processing in cloud computing platform and improve the efficiency of data processing. At the same time, as the device and edge are closer to the edge side, it greatly reduces the delay of data transmission and solves the problem of data delay. Edge computing just optimizes the usage scenario and adds more possibilities for the large-scale deployment of IoT at construction sites. For the above mentioned IoT technology and edge computing technology, this study believes that edge computing combined with IoT to develop IoT system will be beneficial for better deployment of IoT system in construction site. Therefore, this study takes the construction site scenario as an example to design the IoT monitoring system. It explores the convergence of the problems of deployment in the present site and also designs the edge computing IoT framework to design the IoT monitoring system. In summary, this study aims to solve the problems of IoT deployment in construction sites, For the purpose of exploring whether edge computing is helpful to deploy the Internet of things system in the construction environment, it is verified by experiments for multiple construction scenarios. To demonstrate the feasibility and applicability of this development idea, this study will aim to build two systems and design a comparison experiment. It compares the data integrity to better determine whether designing an IoT system with an edge computing framework is beneficial to facilitate the deployment of IoT in construction sites. It analyzes statistically the performance of the control and experimental groups in terms of both disconnection rate and signal strength by conforming to the construction site environment design. Finally, it is concluded to prove that the use of edge computing framework is more suitable for the construction environment compared to the traditional IoT model. The application of edge computing facilitates better integration of the IoT system into the construction site and maximizes its value. The final summary describes the research contributions of this thesis and suggests future research directions related to this study that can be referred to.

參考文獻


1. Bonomi FR,Zhu J,et alMilito. (2012). Fog computing and its role in internet of things. Proceedings of the First Edition of the MCC workshop on Mobile Cloud Computing, 页 13-16.
2. ChenKe. (2020). Enhancing construction safety management through edge computing: Framework and scenarios. ITcon, 页 438-451.
3. EdgeX官網. (2019). EdgeX foundry 官網. 检索来源: EdgeX foundry 官網: https://nexus.edgexfoundry.org/content/sites/docs/staging/master/docs/_build/html/
4. GeorgeWang, J., Bala, M., Eiszler, T., Pillai, P., and Satyanarayanan, M.S.,. (2019). Towards Drone-sourced live video analytics for the construction industry. Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications, 页 3-8.
5. Hu Y CM,Sabella D,et alPatel. (2015). Mobile edge computing—A key technology towards 5G. ETSI White Paper, 页 1-16.

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