本文選題：數(shù)據融合 + 分簇��； 參考：《西南大學》2017年碩士論文

【摘要】：無線傳感器網絡作為物聯(lián)網的重要組成部分,是由隨機分布在感知區(qū)域內的成百上千的廉價的微型傳感器節(jié)點構成的,通過無線通信的形式構成的一個多跳的、自組織的網絡系統(tǒng)。它以感知、收集和處理無線傳感器網絡覆蓋區(qū)域的數(shù)據信息,并將最后的結果發(fā)送給用戶為主要目的。部署在感知區(qū)域的傳感器節(jié)點的能量十分有限,且不能增補。因此,如何降低節(jié)點能量的消耗以盡可能延長網絡的生命周期是目前無線傳感器網絡研究的重點。為了全面地覆蓋感知區(qū)域,傳感器節(jié)點通常部署十分地密集,這會使得相鄰傳感器節(jié)點感知的數(shù)據存在時間和空間的相關性,從而導致明顯的冗余。為了消除冗余,數(shù)據融合成為了一種非常有效的能消除冗余、最小化傳輸中的數(shù)據量、節(jié)約能量的方法。在先前的工作中,很多學者提出了很多有效的消除數(shù)據冗余的方法。但是,這些方法有的能量效率較低,有的復雜度較高。在基于簇的網絡結構中,能量主要被消耗在從簇頭節(jié)點到匯聚節(jié)點的傳輸過程中。因此,本文主要考慮通過在簇頭節(jié)點進行數(shù)據融合來降低傳輸?shù)臄?shù)據量,從而達到節(jié)約能量的目的�；谶@些考慮,本文提出了一種在網絡總延時約束下的無線傳感器網絡中的基于典型相關性分析的數(shù)據融合方法。首先,為了平衡簇與簇之間的能量消耗,我們在總延時約束下將整個網絡中的總能量消耗作為最小化目標,并通過拉格朗日對偶的方法獲得網絡中的最優(yōu)簇的數(shù)量。其次,為了避免傳輸過程中的擁塞,我們采用“分時隙”的分簇算法來構造網絡中的最優(yōu)融合樹,并保證在傳輸過程中一次傳輸只占用一個時隙。再者,在簇頭節(jié)點處,我們提出了一種基于典型相關性分析的數(shù)據融合方法,它可以低復雜度的處理不同類型的多維數(shù)據,消除簇頭節(jié)點處的數(shù)據冗余,從而最大限度的減少傳輸?shù)臄?shù)據量,節(jié)約網絡中的能量。最后的仿真結果表明,與現(xiàn)有的方法相比較,我們的方法不僅可以減少傳輸?shù)臄?shù)據量,節(jié)約網絡中消耗的能量,還可以減少網絡的延時,延長無線傳感器網絡的生命周期。
[Abstract]:As an important part of the Internet of things, wireless sensor networks are made up of hundreds of cheap sensor nodes randomly distributed in the perceptual region, and a multi-hop in the form of wireless communication. Self-organizing network system. Its main purpose is to perceive, collect and process the data information of the wireless sensor network coverage area, and send the final results to the user. The energy of sensor nodes deployed in the sensing area is very limited and cannot be supplemented. Therefore, how to reduce the energy consumption of nodes in order to prolong the lifetime of wireless sensor networks as much as possible is the focus of research on wireless sensor networks. In order to completely cover the perceptual region sensor nodes are usually deployed in a very dense manner which results in temporal and spatial correlation between sensing data of adjacent sensor nodes and resulting in obvious redundancy. In order to eliminate redundancy, data fusion has become a very effective method to eliminate redundancy, minimize the amount of data in transmission, and save energy. In previous work, many scholars have proposed many effective methods to eliminate data redundancy. However, some of these methods have lower energy efficiency and higher complexity. In cluster-based network architecture, energy is mainly consumed in the transmission process from cluster head node to sink node. Therefore, in this paper, the data fusion in cluster head node is mainly considered to reduce the amount of data transmitted, so as to save energy. Based on these considerations, this paper proposes a data fusion method based on canonical correlation analysis in wireless sensor networks with total delay constraints. First, in order to balance the energy consumption between clusters, we take the total energy consumption in the whole network as the minimization objective under the total delay constraint, and obtain the optimal number of clusters in the network by Lagrange duality method. Secondly, in order to avoid the congestion in the transmission process, we use the "time slot" clustering algorithm to construct the optimal fusion tree in the network, and ensure that only one time slot is used at a time during the transmission process. Furthermore, at the cluster head node, we propose a data fusion method based on the canonical correlation analysis, which can deal with different types of multidimensional data with low complexity and eliminate the data redundancy at the cluster head node. In order to minimize the amount of data transmission, save energy in the network. The simulation results show that compared with the existing methods, our method can not only reduce the amount of data transferred, save the energy consumed in the network, but also reduce the delay of the network and prolong the life cycle of the wireless sensor network.
【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP212.9;TN929.5

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