如果能利用定位機制為感測節點自動取得定位資訊，則在無線感測網路的應用上會更俱有意義。目前大多數的定位機制只有使用到的定位資訊來自於定位機制執行時期，而未使用到一些佈署時所已知的資訊。在這篇論文中我們利用一些已知佈署知識，提出一定位模型是建立在貝式網路基礎上，此模型包含是否接收到訊號、佈署資訊(例如：格網長度、格網大小等)、佈署知識(例如：某一感測節點鄰近的感測節點的資訊、感測節點之通訊可能性等)元素，利用這些元素去描述感測節點在佈署成格網形狀時的位置關係，以一機率模型的方式來呈現其因果關係。基於此定位模型，我們提出一相互定位機制利用預先知識使用貝式網路之演算法(CLPKBN)，去達到自動定位之功能可應用在無線感測網路中。為了評估提出之機制，我們實作相互定位機制利用預先知識使用貝式網路之演算法、機率格網定位機制(Probability Grid)在一模擬器上。我們評估不同感測節點數量在感測環境中、不同比例之已知位置節點在感測環境中、不同無線電遮蔽效果在感測環境中、不同大小之傳輸訊號功率在感測節點中等參數。評估的標準是去計算此兩個演算法之定位精準度。實驗結果顯示相互定位機制利用預先知識使用貝式網路之演算法優於機率格網定位機制在大多數的測試數據下。

Obtaining location information by localization schemes for sensor nodes makes applications of wireless sensor networks (WSNs) more meaningful. Most of localization schemes only use the information gathered during the execution of the localization scheme. In this thesis, we proposed a location model based on Bayesian Network [18] with proximity measurement, the deployment information (such as grid distance, grid size, etc.), and the deployment knowledge (such as neighbors of a sensor node, the connectivity betweens two sensor nodes, etc.) to describe the relations of the locations of sensor nodes deployed in a grid topology with the probabilistic graphical model. Based on the location model, we present a cooperative localization algorithm, the CLPKBN scheme, to do the localization for a WSN. To evaluate the proposed scheme, we implement the CLPKBN scheme and the Probability Grid scheme on a simulator. Several parameters, including different deployment sizes, different ratio of anchor nodes, different deployment for anchor nodes, different shadowing effects, and different transmission signal power, are used as measurement metrics. The experimental results show that the CLPKBN scheme outperforms the Probability Grid scheme in most of test cases.

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