無線感測網路(wireless sensor network)是一群透過無線網路連結的感測點(sensor node)，近年來已經開發了許多相關的應用比如環境監測、產物管理、定位系統等。在無線感測網路中，由於因為硬體上的限制(裝置電池、體積小、重量輕)以及應用的需要，因此我們必須思考如何能夠透過改變網路運作的方式來有效地減少能量損耗藉以延長整個網路的壽命。在網路行為中，由於我們利用了無線傳輸，因此往往傳輸一單位的資料要比運算所要耗費的資源來的高，因此我們首重在如何有效的減少無線傳輸的次數以有效的提高能量的使用率。在典型的無線感測網路中，常常會利用大量感測點密集的佈放在我們想要監測的環境中，以進行資料的觀測，但是由於密度高的佈放會讓相鄰近的點所觀測的資料成為累贅的資料，如果我們能有效的減少這類資料的產生，將可以有效的節省整體能量消耗。
在這篇論文中，我們首先利用向量量化演算法(vector quantization)來選擇每個叢集的叢集首(cluster head)，藉以讓叢集首可以均勻分配在我們所要觀測的環境中，避免傳統隨機選擇叢集首因為分配不均導致浪費能量的情形發生，此外我們又提出了一個簡化的的準確度分配演算法(precision allocation)來指派每個感測點的誤差上限，我們所提出的準確度分配演算法具有低複雜度以及簡單的特性，而且所能節省的能量消耗幾乎與傳統的適應性準確度分配演算法(adaptive precision allocation)一致，最後，我們又在叢集首加入一個資料檢查的步驟，因為每個叢集的叢集首需要負責收集叢集成員的資料然後往上做更新的動作，因此叢集首的負載會比一般的感測點來得大，藉由加入這個機制我們可以有效的降低叢集首的負載，避免該個叢集因為叢集首能量耗盡而讓該個覆蓋範圍(coverage)失去作用，更進一步的延長整個網路的壽命。

Total energy consumption caused by data transmission and signal processing is always a major problem for the development of wireless sensor networks. In general, when sensor nodes are distributed densely, they will send all the sensing data to the processing center, and this may lead to packets contention or high loading for each relay node. However, some sensing data is not necessary for the precision of estima-tion made by the processing center; we call them the redundant data. If we can de-crease the redundant data appropriately, the total energy consumption can be effec-tively saved.
In this thesis, we propose an energy-efficient architecture for wireless sensor networks. We first reduce the total energy consumption by a clustering algorithm that chooses the cluster head by vector quantization (VQ). In the VQ-based clustering al-gorithm, each cluster head is uniformly specified such that the distances between its members and itself are nearly the same. Furthermore, we use the precision-constrained data aggregation method to decrease the redundant data. Hence, we need to assign different error bound for each sensor node for adjusting the updating frequency, which is so-called precision allocation. To further alleviate the energy consumption of the training node for error bound estimation, we assign the error bound for each sensor node according to the number of hops. The proposed precision allocation method is easily realized in sensor nodes and the total energy consumption will be efficiently decreased. By using this architecture, we can save a large amount of energy consumption. Moreover, we can add a check construction in cluster heads; we check the averaged data in order to release the loading in cluster heads. Hence, the lifetime of networks will be further extended. Although this step may decrease the precision of estimated data slightly, we can decide if the system will use the check in cluster heads based on the application in practice. Computer simulation results show that as compared to the traditional architecture, the proposed scheme has both low-complexity in sensor nodes and high energy efficiency.

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