臺灣博碩士論文加值系統

一個多資料匯集點的無線感測網路是由區域中部署大量的感測器及一些特別的感測器所組成，其特別的感測器稱之為資料匯集點，負責接收這些感測器所收集的數據。資料匯集點具有連結網路的功能，因此它可以將收集完畢的數據藉由網路回傳給負責收集數據的伺服器。如此一來，每個感測器可以把它所收集數據都傳送到這些資料匯集點。當資料匯集點坐落於不同的位置時，它可能會有不同的通訊壓力。舉例來說，資料匯集點藉著感測器來收集數據，而每個感測器的負載和能源消耗情況皆不相同，因此會有一些感測器的能源會提早耗盡，進而造成瓶頸現象。在這種情況下，數據就有可能無法傳送到資料匯集點。因此在論文中，我們評估了無線感測網路中，多個資料匯集點的感測器在均勻分佈的情況下，其相對應的能源消耗及負載分配和壓縮數據的影響。有了網路負載分佈的觀念之後，我們發現不僅靠近資料匯集點的感測器能源損耗較快，感測器的數據轉送數據到資料匯集點的路徑越遠的話，能源的耗損也會越多。路徑較長的感測器負載會較大，所以我們提出一個線性的下游選擇法來分散其負載。此外，在多資料匯集點之無線感測網路中，感測器在不同層的中經由不同的密度控制後，能使整體網路感測器的負載較為平衡，這將會有效的增加數據成功回傳到資料匯集點的可能。分析和模擬的結果會後面顯示，並證實經由我們的方法資料的到達率和存活率將會有效地改進。

A multi-sink wireless sensor network (WSN) is composed of a large number of sensors deployed in a wide area and there are some special sensors referred as sinks which play the role of data collector. Sinks are connected to internet so that they can forward data to the data collecting server. Thus each sensor can forward the information they sensed to any one of these sinks. Since sensors located in different positions might bear different communication responsibilities, e.g. forwarding the data generated by some other sensors, the loading or energy consumption of each sensor in network is heterogeneous such that some might die earlier than the others causing bottleneck in the network. In this case, data from sensors might not be delivered successfully to the sinks. In this paper, we evaluate the corresponding energy consumption as well as the load distribution of sensors deployed uniformly in multi-sink WSN by taking the effect of data compression into consideration. With the knowledge of load distribution of network,
we will find that sensors located not only near around the sinks but also in the forwarding paths from the farthest sensor to the sinks would consume more energy. A descent selection method is proposed to leverage the loading of sensors in the longest forwarding path. Moreover, deploying sensors in multi-sink WSN by density control to balance the loading of sensors in different tiers would be effectively prolong the network lifetime and increase the successful data delivery as possible. Analytic and simulation results show the conclusions that the data delivery and survival ratesis significantly improved by the proposed methods.

第一章 緒論 1

研究背景 1

研究動機和目的 2

論文架構 3

第二章 相關研究背景及文獻探討 4

第三章 感測器的負載與分佈 7

3.1. 網路感測器的分佈 7

3.2. 網路的負載分佈 10

3.3. 線性之下游選擇法 19

3.4. 經密度控制後的感測器部署 20

第四章 模擬結果 23

第五章 結論 30

參考文獻 31

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