群集路由協定是一種有效的拓撲結構，可以增加無線感測網路的擴展性，且可以有效的發揮感測器有限的能源。但是，每個感測器在網路中能源消耗程度有所不同，所以網路中的某些感測器可能會較快死亡。在這種情形下，感測器可能不能將它們所感測到的資料傳送回給資料匯集點。以往許多的研究都著重在群集路由協定的節能對網路壽命的影響，而沒 考慮到傳輸範圍、資料壓縮或融合、與多樣化的感測器設備等等…對網路的影響。感測器在網路中的位置不同，可能會有不同的通訊責任，例如:一些需要將資料傳回給資料匯集點的感測器。在本文中，我們提出一個量子化的模型來模擬集群網路，以評估網路的能源消耗與感測器的耗電分佈。此外，也發展出選擇群集頭領的演算法。隨著感測器能源消耗分佈的資訊增加，繞送協定將會更發達且更有效的延長網路的壽命和增加網路的利用率。分析的結果，可以得知能源消耗和網路中的能源負擔。在論文中，我們將我們的方法與LEACH做比較。

Clustering routing protocols are effective topology approaches which can increase the scalability of wireless sensor networks and efficiently utilize the limited energy resources of the sensors. However, the loading or energy consumption of sensors in networks is heterogeneous such that some sensors might die earlier than the others. In this case, data from sensors might not be delivered properly to the base station (BS). Many previous studies have focused on energy-efficient routing protocols to prolong the network lifetime without considering the influences of transmitting range, availability of compression or coalescing, and variety of sensor mechanics etc. Sensors located in different positions might bear different communication responsibilities, e.g. forwarding the data generated by some other sensors. In this paper, we propose an analytic quantum model to simulate the operations of clustering routing protocols to evaluate the energy consumption of networks as well as the load distribution of sensors. Besides, the cluster head selection algorithm would be developed correspondingly. With the knowledge of the load distribution and energy consumption of sensors in networks, routing protocols could be developed or adjusted properly to prolong the network lifetime and enhance the network availability. Analytic results of the energy consumption and load distribution of network and the comparison of performance of LEACH and our method are shown in this paper.

第一章 绪論
1.1. 研究背景
1.2. 研究動機與目的
1.3. 論文架構
第二章 相關研究背景與文獻探討
第三章 量子化模型與群集建構
3.1. 量子化繞送模型
3.2. 群集構成
第四章 能源消耗評估與實驗結果
4.1. 能源消耗評估
4.2. 能源消耗常態化
4.3. 實驗結果I
4.4. 實驗結果II
第五章 結論與未來展望
5.1. 結論
5.2. 未來展望
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