無線感測網路是近年備受矚目的研究領域，應用範圍廣泛，例如: 監測環境、目標追蹤和科學探測，這些應用通常是在險惡且無人維護的環境長時間運作，無線感測節點通常配置電池進行運作，電量耗盡後為節點進行充電或是更換電池並不容易，因此，電力對於無線感測網路是一項十分珍貴的資源，如何有效減少無線感測節點運作的時間來延長整個應用的執行時間是很重要的議題，低工作週期的概念因此被提出以減少結點的能源消耗。於低工作週期的環境中，節點大部分的時間處於睡眠狀態，只有少部分時間處於運作狀態，因此導致傳輸延遲的問題，當接收端處於睡眠狀態，發送端必須將訊息儲存在緩衝區，根據接收端的排班表，直到接收端成為運作狀態才能將訊息送出，此等待狀態改變的時間稱為等待延遲。點對點延遲指封包從來源端經由多個中繼節點傳遞訊息到達目的端所需的時間。來源端傳送封包後，目的端需要多少時間才能收到封包是很多資料傳輸演算法主要評估效能的標準之一，該評估標準代表整體網路的運作效能。在低工作週期的環境中，來源端到目的端之間每經過一個中繼節點代表增加一份等待延遲，因此大幅增加點對點延遲。不同匯流點的擺放位置是影響點對點延遲的重要因素。匯流點在很多應用中扮演整個網路的樞紐，適用很多不同的用途，像是傳播指令到每個節點、重新設定節點內部設置、查詢節點狀態等等。由於匯流點傳遞封包或接收封包時，會以中繼節點代傳方式傳遞，因此不同匯流點位置可能會導致封包傳送經過的中繼節點不相同，中繼節點有各自的工作排程造成不同的等待延遲，進而改變平均的點對點傳輸延遲。在低工作週期環境中良好的匯流點位置使得訊息傳遞更加快速，本論文提出找尋適合匯流點位置以減少由匯流點傳送資料到環境節點花費時間演算法（Geographic Cluster-based Sink Location Determination, GCSLD）及找尋適合的匯流點位置以減少由匯流點從環境節點接收資料所花費時間演算法（Receiving Sink Location Determination, RSLD）。GCSLD演算法主要包含三個步驟：預測匯流點可能出現區域、建立cluster、以及計算平均點對點時間。根據模擬結果顯示，GCSLD與最佳化演算法的效能僅差1%，但找尋適當匯流點的速度快了13倍。RSLD演算法由三個步驟組成：邊界結點廣播、決定匯流點位置、建立路由樹。根據模擬實驗結果顯示，RSLD的平均延遲時間比傳統方法(匯流點設立在網路中心)節省14%；與最佳化演算法比較，僅需34%的時間即可找到適當的匯流點位置。

A low-duty-cycle approach can significantly reduce the energy consumption in wireless sensor networks. Sensors stay dormant most of the time and wake up only when performing tasks. However, such a technique, while prolonging the network lifetime, sets excessive challenges for reducing the transmission delay within the network. This thesis develops the enhancement schemes for reducing the sending transmission latency and the receiving transmission latency in this environment.
Many applications need a sink node to send packets to all the nodes in the network, such as code updates, emergency commands, and so on. The dissemination at different locations of the sink node results in various end-to-end
delays because of the different transmission sequences corresponding to the working schedules of relay nodes. Thus, determining a suitable location for the sink node can efficiently improve the end-to-end delay. A Geographic Cluster-based Sink Location Determination (GCSLD) algorithm is introduced to reduce the dissemination latency in low-duty-cycle wireless sensor networks by searching for an appropriate location for the sink node. The GCSLD algorithm is mainly consisted of three steps: (1) Potential sink region estimation, (2) Cluster construction, and (3) Average end-to-end delay calculation. The GCSLD algorithm is implemented and evaluated successfully. The simulation results show that the average end-to-end delay of the GCSLD is close to the performance of the optimal algorithm. Moreover, the GCSLD runs about 13 times faster than the optimal algorithm. The simulation results confirm that the GCSLD determines suitable locations with lower execution overhead.
Low-latency data receiving is an important requirement for achieving effective monitoring through low-duty-cycle
wireless sensor networks. However, for the sink, the end-to-end delays of receiving packets from sensor nodes is much longer than traditional wireless sensor networks because sensor nodes are in sleep state most of the time. It is significantly to design a data collection scheme in low-duty-cycle wireless sensor networks. Due to the different transmission order corresponding to the working schedule of the relay node, the different locations of the sink nodes result in various end-to-end delays. Therefore, determining the appropriate receiver (sink) location can effectively improve the reception delay. A Receiving Sink Location Determination (RSLD) algorithm is introduced to reduce the receiving latency in low-duty-cycle wireless sensor networks. The RSLD scheme incorporates three steps, namely (1) Border nodes broadcast, (2) Sink location decision, and (3) Routing tree construction. To evaluate the performance of the proposed RSLD scheme, RSLD, Center (the sink node is
set on the geographic center.), and the optimal algorithm are simulated. The results show that RSLD reduces the average end-to-end delay by approximately 14% compared to Center. The results also show that RSLD reduces the average execution time required by approximately 66%
compared to the optimal algorithm. Since the network requires fewer average end-to-end delay to receive the packet, RSLD yields a significant improvement in the transmission efficiency. The simulation results also demonstrated that not only the end-to-end delay of the RSLD is close to the optimal algorithm but also required much less execution time to determine suitable locations.

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