隨著網路的普及和感測器成本的降低，物聯網也逐漸興起。透過物聯網，可以監測不合預期的異常症狀。現有物聯網異常相關技術的研究，主要著重在異常事件的偵測，較少異常事件成因診斷的研究。導致異常事件的成因可能是觀測環境的異常或儀器設備本身的異常。針對異常成因的診斷，現有研究都集中在網路攻擊的異常事件。
本論文以環境物聯網為例，研究物聯網異常事件診斷的方法。我們歸納整理空汙環境物聯網的異常症狀、時空線索與成因，提出物聯網異常事件診斷的方法與流程。根據我們所提出的診斷流程，設計實作一個異常事件診斷系統，提供使用者透過聚焦、歸納、對比，逐步地由眾多可能的線索中，探索出最可能導致異常事件的成因。本論文並以真實案例，實證我們所提出的系統可以協助使用者方便有效地找出異常成因。

With the spread of the internet and the cost reduction of sensors, the Internet of Things (IoT) became more popular. People can monitor and detect unexpected anomaly symptoms using IoT. Most of the existing research focuses on anomaly event detection while little research has been paid to the anomaly event diagnosis. Anomaly event may come from the deviation in environment or malfunction of devices themselves. Most current work on anomaly event diagnosis aim at the malicious attacks in IoT network.
This thesis investigated the method of anomaly event diagnosis using environmental IoT as an example. We organized anomaly symptoms, temporal clues, spatial clues, and the root causes of anomaly events in environmental IoT for air pollution. This thesis also proposed the method and the procedure to diagnose anomaly events. According to the proposed procedure, this thesis designed and implemented an anomaly diagnosis system. The system provides the ability to focus, organize and compare the clues for anomaly diagnosis. It helps users to rule out unlikely root causes and explore possible root causes that triggered anomaly events. The proposed approach is demonstrated by real cases to show that the system could assist users to explore the root causes of anomaly events conveniently and effectively.

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