現今無線感測網路逐漸被使用於不同的應用中，其中，重要應用之一為偵測目標物是否存在於監控區域內，然而，在過去大多的目標物偵測研究中，採用不符合現實情況之圓盤感測模型做為偵測目標物模型，且依靠單一感測節點的決策結果視為整體網路的回報結果，容易導致高誤警機率並降低偵測效能，為了改善偵測效能，資料融合(Data fusion)被廣泛地使用於目標物偵測機制中，能減緩環境雜訊對最後決定所造成的影響，進而提升效能；此外，散佈在感測區域中的感測節點數量多寡會影響偵測效能，為了要達到使用者所要求的偵測效能，一般而言會散佈眾多的感測節點在區域中做偵測目標物偵測，如此做法將會導致成本提高，因此，本篇論文提出一個以決策融合為基礎之連續偵測機制，稱為決策融合連續偵測(Decision Fusion Sequential Detection, DFSD)，在感測區域中散佈固定的節點數量，感測節點為了要減少通訊的電量消耗，會先各自做出1位元的本地決定資訊傳給決策中心做融合，若決策中心蒐集到足夠的資訊能滿足使用者所預設的偵測效能，則做出最後全域決定，否則將進一步繼續做偵測，直到滿足所預設的預警機率與遺漏機率條件為止，保證最後決定的可靠性。我們所提出的機制不僅能提供簡單的偵測規則做出最後決定來確保誤警機率和遺漏機率，且因感測節點只傳送1位元資訊，還能降低電量消耗，延長網路壽命。此外，在本篇論文中，我們以偵測延遲時間(Detection latency)做為評估網路效能好壞之準則，因此，本論文以決策融合連續偵測機制為基礎，分析推導出目標物存在或不存在情況下之偵測延遲時間。
我們在模擬實驗中證實所分析而得的偵測延遲時間與模擬實際情況所得的偵測延遲時間非常趨近，並以數值融合連續偵測機制(Value Fusion Sequential Detection, VFSD)作為比較對象，證實所分析之偵測延遲時間正確無誤；而在電量消耗的評估實驗中，我們分別以VFSD與DFSD方法進行模擬，證明DFSD所消耗之電量會低於VFSD，延長網路存活時間。

Wireless sensor networks have been used in many applications. One of the most important applications is to detect a target or an event in the monitored region. However, most of previous target detection studies are based on the unrealistic disk model, and use a single sensor’s decision as the decision of the overall network. This simple detection scheme may result in high false alarm probability and deteriorate the detection performance. Therefore, data fusion is required to improve the performance of detection. It has been broadly exploited to alleviate the impact of noises on detection decisions made by the system. In general, more sensors deployed in the region of interest can results in higher detection probability. For fulfilling a pre-defined requirement of detection performance, a naive way is to increase the number of sensor in the region till the detection performance is reached. However, deploying more sensors can incur problem of higher cost. In this thesis, we adopt sequential detection to guarantee the reliability of the detection results, namely Decision Fusion Sequential Detection (DFSD) scheme. We deploy the fixed number of sensor node to detect the region about whether a target presence or not. To reduce communication energy consumptions, each sensor node first makes 1-bit local decision based on its local observation and then the local decision result is sent to a fusion center. It makes a global decision when it has sufficient data to guarantee that the pre-defined constraints of false alarm and the missing probability are satisfied. If the fusion center cannot make a global decision, it will collect data again in the next period and repeat the detection operations. The DFSD scheme provides simple rules derived for both local and global decision making based on likelihood ratio test. Using the procedure above, DFSD scheme can ensure false alarm probability and missing probability. It can also prolong the network lifetime. Moreover, this thesis also defines detection latency as an important metric for the performance of sensor networks and analytically derives the detection latency.
Simulations are conducted to compare the performance of DFSD scheme with Value Fusion Sequential Detection (VFSD) scheme. The experimental results verify the analytical solution of the detection latency is valid. In addition, the results show the proposed scheme (DFSD) is superior to the VFSD scheme in terms of energy consumption.

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