臺灣博碩士論文加值系統

此篇論文中，為了因應一般無線感測網路(wireless sensor networks)中分散式偵測(distributed detection)的問題，針對每個感測器，設計了以資料可靠度來控制傳輸(data-dependent transmission control)的函數。由於此函數，可靠的感測器將有越大的機會傳出，所以感測器所傳之訊息便包含了實體層的時間訊息(MAC timing information)；同時在決策中心(fusion center)可設計相對應的決策方式並降低整體系統的錯誤率。
首先，每一個感測器它都會在每一個觀測週期(observation period)的開端作一次觀測並且將此觀測出所做的決定，然後經由隨機存取通道(random access channel)送到決策中心。在slotted ALOHA隨機存取網路的假設下，可推導出傳輸控制函數；此函數依照每個感測器所作之觀測值的可靠度來決定每個感測器該什麼樣的機率來競爭隨機存取通道。由於此函數的設計，決策中心所收到的資料順序便包含著資料是否可靠的訊息；同時設計決策中心的決策方法與有效利用時間訊息便可以有效的改善系統整體的錯誤率。
再來將問題到延伸到叢集(multi-cluster)感測網路，假設感測器所傳之訊息最多可以經由兩次跳躍到決策中心。更明確的說，感測器可以經由第一次跳躍將訊息傳到叢集首(cluster head)，由叢集首匯整所有在此叢集中的感測器之決定來作出第二次跳躍時叢集首的決定；第二次跳躍則將每個叢集首的決定送到決策中心。經由模擬結果，依照資料可靠型來傳輸的方法，可以有效降低整體系統錯誤率。

A data-dependent sensor MAC protocol and a cross-layered fusion rule that exploits MAC timing information are proposed for distributed detection in sensor networks. In this system, each sensor first makes a local decision at the beginning of each observation period and transmits the decision to the fusion over a random access channel. Based on the slotted ALOHA random access protocol, we derive a transmission control function that assigns transmission
probabilities to each sensor based on the reliability of its local decisions. By doing so, the packet arrival time at the fusion center may embed soft information regarding the sensors' observations and may be exploited to reduce the error probability at the fusion center. We then extend the proposed strategies to the multi-cluster scenario where the sensors' local decisions are transmitted to the fusion center in a two-hop fashion. More specifically, the sensors
first send their local decisions to their respective cluster-heads, where a local fusion is performed, and the resulting decision is then forwarded to the fusion center. We show, through numerical simulations, that the proposed schemes outperform those without cross-layered transmission and fusion strategies.

Abstract
Contents
1 Introduction
2 System Model
3 Observation period K = 1
4 Observation period K > 1
5 Extensions to the Multi-Cluster Scenario
6 Numerical Simulations and Performance Comparisons
6.1 Single Cluster Scenario
6.1.1 Multi-Cluster Scenario
7 Conclusion

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