無線感測網路是由具有能量限制的微小裝置所組成的。一旦佈置後，感
測節點的位置通常是使用者難以到達地方並且也較困難去置換它的能量裝
置。因此，能量效能對於感測網路是一個重要的議題需要來解決以增進網路
的運作時間。LEACH 是感測網路中以叢集式架構結合資料融合的一種節省能
量且增進運作時間的資料收集方法。LEACH-C 則是中央控制的LEACH，而
在LEACH-C 中，它利用基地台知曉節點的位置及其能量來計算網路適合的
叢集分配，並且以此來增進LEACH 的運作時間效能。
在本論文，我們對於叢集架構的環境提出一套新的想法，稱作基地台控
制高能量移動節點之方法(BCM)。在BCM 中，我們主要加入了高能量的移動
感測節點在LEACH-C 的架構中，並且我們利用中央控制的方法來操控移動
節點對於需要支援的叢集來做支援，希望延長網路的運作時間。而我們在
BCM 中訂立了叢集需要支援的條件以及計算其分配給移動節點來做叢集頭
的位置，希望透過高能量移動節點的特性來支援需求支援的叢集，擔任這些
叢集的叢集頭。在最後，我們將透過模擬來驗證BCM 它的效能，並且對高能
量節點在移動與不可移動情況下對運作時間的影響結果上做比較與討論。

Wireless sensor networks consist of small battery powered devices with
limited energy resources. Once deployed, the sensor nodes are usually
inaccessible to the user, and are hard to replace their energy source. Hence,
energy efficiency is one of the important issues that need to be enhanced so
as to improve the lifetime of the wireless sensor networks. Low energy
adaptive clustering hierarchy (LEACH) is a protocol architecture for sensor
networks that combines the concept of integrating energy-efficient and
cluster-based routing together with data aggregation to increase the
performance in the system lifetime. LEACH-C is a centralized version of
LEACH. In LEACH-C, the base station utilizes its global knowledge of the
location and energy level of each node in the network for cluster formation
and can achieve better performance in the system lifetime.
In this thesis, we propose a new idea in the cluster based of sensor
networks called Base-station Controlled Mobile sensor node scheme (BCM).
In BCM, we adopt the high-power mobile sensor nodes in LEACH-C
architecture and these high-power mobile sensor nodes can move to a
suitable location to support the functions that the cluster headers shall
perform under central control. We propose the moving procedures and
location-aware decision rules. Thus, the decisions of movement of the
high-power mobile sensor nodes are made according to the status estimation
of each cluster and the location of these high-power mobile sensor nodes.
Finally, we illustrate the performance of proposed scheme through
experimental simulations. We also discuss and compare the performance of
the moving algorithm with location-aware .and without location-aware in
our simulation results.

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