無線感測網路近年來被廣泛地應用於安全、環境監控與目標追蹤及偵測，進而去獲取週遭環境資訊與執行數據計算。針對入侵者或目標物偵測此一應用，如何有效率地進行即時性偵測，以及妥善利用感測節點之能量，對於目標物偵測應用是個很重要的議題。在過去許多偵測研究，大多是使用單一感測節點進行目標物偵測，並且僅使用單一位元的偵測結果作為最後判斷依據，雖然該方法簡單容易實作，然而，容易受到環境中的雜訊干擾，使得判斷錯誤的機率提高，降低目標物偵測的效能。因此，本篇論文利用多個感測節點來進行合作連續偵測，這樣的偵測機制適應於雜訊干擾存在，以及訊號會隨著距離衰減之情況，提供了比單一感測節點更加可靠的偵測機制。在本篇論文中，我們提出了三個基於資料融合，且採用網路內處理之合作連續偵測方法，分別為加總運算之數值融合偵測（Value fusion by summation）、概似比值累乘運算之數值融合偵測（Value fusion by likelihood ratio product）以及概似比值累乘運算之決策融合偵測（Decision fusion by likelihood ratio product）。資料融合主要將各個感測節點偵測到的資料智能合成，提供比單一感測節點更加準確的判斷依據，提升偵測之效能。其中，將各偵測資料智能化合成之計算，分別於各個感測節點進行之，即網路內處理，如同資料聚集技術，藉此降低能源之消耗。我們的實驗結果顯示基於數值融合可以達到較短的偵測延遲時間，且使用概似比值累乘運算之網路內處理之偵測效能較佳。除此之外，採用網路內處理比未採用大幅地降低了感測節點傳輸上之能源消耗。

Recently, wireless sensor networks (WSNs) are extensively utilized in security surveillance, environmental monitoring and target tracking/detection. WSNs are used to monitor the physical environment and perform tasks for a variety of purposes. In target detection, two important issues are to perform detection efficiently and reduce energy consumption in transmission. Most of past studies adopted a disc model to target detection and use only 1-bit to represent the detection made by the system. Although using the trivial detection strategy is easy to implement, the detection performance may be decreased due to high false alarm probability caused by noise. In this paper, we propose a collaborative sequential detection with multiple sensors. The detection strategy is applied to the environment where noise and signal attenuation coexists. Three collaborative sequential detection methods are presented. These methods are based on value fusion by summation, value fusion by likelihood ratio product, and decision fusion by likelihood ratio product. In particular, data fusion can integrate multiple detection data, and provide an accurate and useful decision result to ensure the overall performance. By using the technology of in-network processing, sensor measurements are processed along the route to the sink and successfully reduces energy consumption of network. Simulation result shows that the integration of value fusion and in-network processing with likelihood ratio product achieves better performance. Furthermore, using in-network processing, our research reduces the energy consumption in transmission among sensors.

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