大量的感測器(Sensors)同時連線到基地台(Base Station)會增加基地台的負擔，所以感測器會先分成不同的叢集(Cluster)，每個叢集會先選出一個叢集頭(Cluster Head)，此叢集頭負責轉傳其叢集內的感測器所收集的資料，感測器收集的資料可以分成即時性(Real-Time, RT)與非即時性(Non-Real-Time, NRT)，因為一個叢集頭的處理速度(Processing Capacity)有限，如果一個叢集內有太多感測器傳送RT訊框(Frame)時，某些RT訊框會因為超過其時效限制(Time Constraint)而被丟棄。另外，大部分的感測器傳送的訊框其標頭(Header)的位元組遠大於資料承載(Payload)，傳送多個標頭位元組遠大於資料承載的訊框會造成大量的網路頻寬浪費(Waste)。為了不使一個RT訊框超過其時效性並解決訊框因為標頭遠大於資料承載所造成的頻寬浪費，本論文提出一個群組歸類(Group Classification)與資料合併(Data Merge)的機制，此機制分為兩個演算法，第一個演算法讓一個新加入的感測器選擇傳送RT訊框的感測器個數最少的叢集加入。在第二個演算法中，叢集頭首先會在標頭的新增RT欄位中判斷訊框是即時性或非即時性，如果訊框為非即時性，叢集頭會將這個訊框與其他非即時性的訊框做合併。最後，我們撰寫C語言來模擬本論文所提出的群組歸類與資料合併機制，在模擬中，我們改變感測器的傳送頻率來比較叢集頭在不同感測器數目下的訊框遺失率與系統傳輸率，另外，我們比較叢集頭有無使用本機制的訊框延遲時間，從模擬結果中，我們驗證此機制確實可以同時降低RT與NRT的訊框遺失率、有效提高RT的傳輸率、減少RT訊框的延遲時間。

A large number of sensors connected to a base station at the same time will increase the burden of the base station. Normally, the sensors will first be divided into different clusters, and each cluster will select a cluster head which is responsible for forwarding the data collected by the sensors in its cluster. The data collected by the sensors can be classified into Real-Time (RT) and Non-Real-Time (NRT). Since the processing capacity of a cluster head is limited, some RT frames will be discarded for not meeting their time constraints, if too many sensors in a cluster transmitting RT frames. In addition, most frames transmitted by sensors have header much longer than the payload. Thus, transmitting multiple frames with much longer header than the payload will waste a lot of network bandwidth. To make sure a RT frame not exceeding its time constraint and to save network bandwidth, in this thesis we propose a group classification and data-merge mechanism with two algorithms. The first algorithm allows a newly added sensor to select a cluster with the least number of sensors transmitting RT frames. In the second algorithm, the cluster head first determines whether a frame is RT or NRT by designing a RT field in the header. If the frame is NRT, the cluster head will merge this frame with other NRT frames. Finally, we write C code to simulate the proposed group classification and data-merge mechanism. In the simulation, we change the transmission frequency of sensors to compare the frame loss ratio and system throughput of the cluster head with different number of sensors. In addition, we compare the frame delay in the cluster head with and without using the proposed mechanism. From the simulation results, we have validated that the proposed mechanism can reduce the frame loss ratio of both RT and NRT, effectively improve the RT throughput, and reduce the latency of RT frame.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 導論 1
1.1研究動機 1
1.2研究方法 1
1.3章節介紹 3
第二章 叢集式無線感測網路 4
2.1叢集式感測網路 4
2.1.1叢集式感測網路拓樸 4
2.1.2 IEEE 802.15.4協定 5
2.1.3訊框格式 6
2.2封包合併 8
2.2.1媒體存取控制層 8
2.2.2傳輸控制協定層 9
2.3 K-近鄰演算法 10
2.4加權輪詢演算法 12
2.5相關研究 13
第三章 叢集式無線感測網路的資料合併 17
3.1感測器的群組歸類 17
3.2感測器資料合併 20
3.2.1訊框欄位的設計 20
3.2.2叢集頭的運作流程 21
3.3 GCA與MSD的虛擬碼 26
第四章 模擬與結果分析 33
4.1網路模擬拓樸 33
4.2流量產生器的虛擬碼 34
4.3模擬與效能參數 36
4.4模擬結果與討論 38
4.4.1改變感測器傳送頻率 39
4.4.2訊框延遲時間 48
第五章 結論與未來工作 49
5.1結論 49
5.2模擬遭遇的問題 49
5.3未來工作 50
Reference 51
Acronyms 55
Index 58

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