智慧物聯網監測系統在當今日益數字化的環境中扮演著至關重要的角色。本研究旨在開發一種穩定的智慧物聯網監測系統架構，以應對不斷變化的監測需求，提高監測效率和自動化能力。本研究的目標是建立一個高度彈性、可擴展且安全可靠的系統，能夠整合先進的感測技術和數據處理算法，實現對山坡影像與水位監測資料的高效收集、儲存和分析。為了達到這一目標，本研究使用樹莓派作為伺服器，實現伺服器的輕量化，同時感測器端，研究ESP32減少電量消耗，，並且在系統繁忙時以Line Notify即時通報管理者，減少管理者平時用於監測系統消耗的時間成本。

本研究的研究方法根據不同案例需求分析選擇出的適合系統架構。在架構設計的基礎上，本研究將選擇和改善適用於不同場景的感測技術和數據處理算法，以實現系統的高效運作。同時，本研究還將實施完善的安全機制，確保系統的數據安全性和隱私性，並設計便利的應用，視覺化呈現資料給使用者。

預期的成果包括實作一種智慧物聯網監測系統架構，具有高度彈性、可擴展性和自動化能力，並實際應用於山坡影像與水位監測。這一研究期望能實現為智慧監測系統的發展和應用提供重要的技術支持，促進物聯網技術在各個領域的更廣泛應用和深度融合。

The Smart IoT Monitoring System plays a critical role in today's increasingly digitized environment. This study aims to develop a stable architecture for a Smart IoT Monitoring System to address ever-changing monitoring needs and enhance monitoring efficiency and automation capabilities. The objective of this research is to establish a highly flexible, scalable, secure, and reliable system capable of integrating advanced sensing technologies and data processing algorithms for the efficient collection, storage, and analysis of slope imagery and water level monitoring data. To achieve this goal, this study utilizes Raspberry Pi as a lightweight server and ESP32 on the sensor end to reduce power consumption. Additionally, during system overloads, the system employs Line Notify to instantly inform administrators, reducing the time cost typically spent on system monitoring.

The research methodology involves analyzing the requirements of different use cases to select a suitable system architecture. Based on the architectural design, this study will choose and optimize sensing technologies and data processing algorithms applicable to various scenarios to ensure efficient system operation. Furthermore, the study will implement robust security mechanisms to ensure data safety and privacy, along with designing user-friendly applications to visually present data to users.

The expected outcomes include implementing a Smart IoT Monitoring System architecture characterized by high flexibility, scalability, and automation. This system will be applied to slope imagery and water level monitoring, providing significant technical support for the development and application of smart monitoring systems. It is anticipated that this research will promote broader and deeper integration of IoT technologies across various domains.

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