在Wireless Sensor Networks (WSNs)中，如何降低Sensor的電力消耗以達到延長使用時間是許多人想要解決的議題。一般來說Sensor在傳輸資料的時候所使用的電力是造成電力消耗的主要原因之一，然而由於Sensor計算能力的限制，我們希望能夠找到一個計算較簡單並且直觀的方法來解決這個問題。我們採取的方法分別為Adaptive Sampling With RD Model以及 Adaptive Sampling in Dynamic Mode。這兩種方法各適用於單顆Sensor資料波動小但與其它Sensor的趨勢大不相同的情況和單顆Sensor資料波動大但大部份的趨勢相似的情況。在Adaptive Sampling With RD Model中各Sensor Node會在固定的每一段時間中計算出自己目前資料波動的情況，而Base Station將會根據這個資訊將Target Distortion以最佳化的方式分配到各個Sensor Node，然後各Sensor會以查表的方式找出對應的Sampling Rate將這個區段的資料送出。而在Adaptive Sampling in Dynamic Mode中則是以送出一條代表電力走勢的線性方程式來代替Sensor所收到的資料，直到電力的走勢發生改變，使得這條方程式估計出來的值在Base Station造成的誤差超過能容忍的範圍才會再次計算出一條符合目前走勢的線性方程式。最後，我們將這兩個方法結合起來，在每次更新線性方程式時檢查前一段資料的波動情況來決定接下來將使用何種方法。由實驗結果可以看出我們的方法能夠根據不同的資料分佈情況挑選適合的模式來達到降低傳輸率的目的。

Wireless Sensor Networks (WSNs) have been widely applied to many different areas such as surveillance, healthcare, environmental and utility monitoring, etc. In WSNs, each sensor node has the characteristics of small size, limited power, and connected wirelessly. It is responsible for gathering and delivering sensing data over the network periodically. Thus, the energy consumption problem becomes a challenging issue to prolong the lifetime of WSNs. Several research works utilize data aggregation and/or data compression concept to reduce the quantity of necessary transmission, since it is the primary issue that consumes sensors’ power particularly. However, the implementation of these operations requires high computational power.
In this thesis, two approaches adapting to sensing data distribution to largely reduce the amount of required data transmission with limited computation are proposed. They are: Adaptive Sampling with RD Model and Adaptive Sampling in Dynamic Mode. In the first approach, the target distortion is near-optimally distributed (in the rate-distortion sense) to every sensor node corresponding to their relative fluctuation. In the latter one, the possible occurrence of rapid data change in the sensing period is concerned and deliberately manipulated. To combine these two methods, we verify the data trend of each sensor when the prediction function needs to be updated. Then according to the data trend we can decide whether to use Adaptive Sampling with RD Model or Adaptive Sampling in Dynamic Mode. Finally, several real sensed data were gathered and employed to demonstrate the performance of the proposed methods.

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