無線感測網路廣泛應用於不同的環境，一個精確的連續監控對於評估區域內沒感測器的位置是極為重要的工作。在這篇論文，我們利用ɛ-SSVR回報環境的監控資訊，更進一步結合空間與時間的關聯性增強監控準確度。然而；假若感測器的分佈太稀疏，會造成覆蓋漏洞問題影響監控結果。因此，我們利用Uniform Design跟不同內插法來改善覆蓋漏洞的問題。在我們的實驗中，在不同的感測器分佈下比較了我們的方法與其他內插法，ɛ-SSVR比起其他方法來的準確且有較高的計算速度。除了連續監控外，我們也提出一個分散式異常偵測機制來回報異常資訊，提供一個可靠且即時的異常偵測系統。

Considering the diverse application scenarios involving wireless sensor networks (WSNs), accurate continuous monitoring requires a solution to the essential task of estimating unmeasured locations in the monitored space. In this thesis, we utilize ɛ-Smooth Support Vector Regression (ɛ-SSVR) to report monitoring information of environment, furthermore we combine spatial and temporal correlation to strengthen monitoring accuracy. However if our sensors are too sparsely deployed, the resulting coverage holes problem will adversely impact the monitoring result. Therefore, we utilize Uniform Design and different local interpolation methods to assist ɛ-SSVR to mitigate the coverage holes problem. In our experiment, we compare our method with different methods applied to different sensors deployments. ɛ-SSVR has better accuracy and computation speed than others. Besides continuous monitoring, we also propose a distributed anomaly detection mechanism to report anomaly information, in order to provide a reliable and real time anomaly monitoring system.

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