無線感測網路具有低頻寬、有限電量、大量分散佈點等特性。當sink node 欲收集符合特定條件的感測器(sensor nodes)資料時，為避免在整個無線感測網路中進行泛流(flooding)，研究[2]提出Data-Centric Storage Architecture，當感測器感測到符合特定條件的資料時，便以雜湊(hashing)的方式計算出此資料儲存的感測器位置，而sink node亦可以雜湊(hashing)的方式計算出符合特定條件的感測器位置。然而，data-centric node在無線感測網路中所處的位置將會影響sink node對其查詢資料及感測資料在傳遞時所花費的成本，尤其是在多個sink node 的環境中，data-centric node 位置之選擇對通訊成本的影響更甚。本論文提出一個Dynamic Data-Centric Routing and Storage Mechanism (DDCRS)來決定data-centric node位置及解決sink node查詢及資料儲存時的通訊問題。我們發展的協定將根據sink node欲收集資料的頻率以及sink與data-centric node的位置關係來動態決定最佳的data-centric node，並且從data-centric node到多個sink之間自動建立共享的資料回傳路徑。另外，對於新的Sink 之查詢或回傳資料頻率的改變，我們也會動態的調整data-centric node的位置，以達到最好的省電效果。實驗數據顯示我們所開發的協定能有效地節省電量的消耗，並平衡整個網路的電量。

Wireless Sensor Networks (WSNs) are characterized by low bandwidth, limited energy, and largely distributed deployment. The authors in [2] proposed a data-centric storage architecture to temporarily store the event data to reduce the flooding overhead raised by transmitting query and data information. However, the locations of data-centric nodes significantly impact the power consumption and efficiency for data query and store, especially in a multi-sink environment. This thesis proposes a novel dissemination approach, namely Dynamic Data-Centric Routing and Storage Mechanism (DDCRS), to determine locations of data-centric nodes dynamically according to sinks’ location and data collecting rate and automatically construct shared paths from data-centric node to multiple sinks for data collection. To save the power consumption, the data-centric node will be changed as there are new sink nodes participated in the WSNs or some queries change frequencies. The simulation results reveal that the proposed protocol outperforms the existing protocols in power conservation and power balancing.

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