本文關鍵詞： 無線傳感網(wǎng)絡 數(shù)據(jù)融合 神經(jīng)網(wǎng)絡 模糊神經(jīng)網(wǎng)絡 網(wǎng)絡學習效率　出處：《太原理工大學》2014年碩士論文　論文類型：學位論文

【摘要】：無線傳感器網(wǎng)絡是由若干耗能較低,功能各異的傳感器節(jié)點組成的。它們可以在不同的環(huán)境中監(jiān)測和采集周邊環(huán)境信息并將信息發(fā)送給工作人員。在此過程中,節(jié)點具有信息采集,處理和存儲等功能,但考慮到其能源有限,且主要依靠無法替換的電池供電,同時采集到的信息具有高冗余性等特點,若是將這些數(shù)據(jù)全部發(fā)送給匯聚節(jié)點(Sink),會使節(jié)點能耗過快,降低網(wǎng)絡使用效率。為了避免上述問題的產(chǎn)生,人們提出了數(shù)據(jù)融合(data fusion或data aggregation)技術。把數(shù)據(jù)融合應用于無線傳感網(wǎng)絡中,用以減少無線傳感器網(wǎng)絡的通信量,提高信息的融合度和準確度成為降低節(jié)點能耗、延長網(wǎng)絡生命周期的主要手段之一。 本文以環(huán)境監(jiān)測為背景,首先提出了一種適用于WSN的基于神經(jīng)網(wǎng)絡的分簇路由協(xié)議數(shù)據(jù)融合模型。該算法將無線傳感網(wǎng)絡(WSN)的分簇路由協(xié)議與BP神經(jīng)網(wǎng)絡相結合,通過神經(jīng)網(wǎng)絡方法對簇內(nèi)節(jié)點采集到的信息進行數(shù)據(jù)擬合,在此基礎上,通過對網(wǎng)絡訓練參數(shù)的改進,網(wǎng)絡訓練收斂加快,縮短了網(wǎng)絡收斂時長。最后,通過只將數(shù)據(jù)的特征值發(fā)送給匯聚(Sink)節(jié)點,以此來減少節(jié)點數(shù)據(jù)流量、節(jié)約能耗。通過仿真實驗驗證,該算法可有效減少網(wǎng)絡通信量,降低節(jié)點能耗,延長網(wǎng)絡壽命,同時還驗證了本算法在環(huán)境監(jiān)測等方面的實時性和有效性。 再引入以T-S推理系統(tǒng)為基礎的模糊神經(jīng)網(wǎng)絡數(shù)據(jù)融合方法,通過對模糊神經(jīng)網(wǎng)絡學習算法的學習、研究,提出了一種新的改進學習算法,最后再與分簇路由協(xié)議相結合,利用上文中提出的創(chuàng)新結合,提出了一種新的基于模糊神經(jīng)網(wǎng)絡的WSN數(shù)據(jù)融合模型。 最后通過仿真實驗表明,以水環(huán)境監(jiān)測系統(tǒng)為背景,與傳統(tǒng)的T-S模糊神經(jīng)網(wǎng)絡相對比,分別從網(wǎng)絡預測準確度及網(wǎng)絡收斂速率兩方面,驗證了改進算法模型的高效性,最終達到節(jié)省了節(jié)點能耗,延長網(wǎng)絡壽命的目的,同時證明了其在水環(huán)境監(jiān)測系統(tǒng)上的可行性及高效性。
[Abstract]:Wireless sensor networks are made up of a number of sensor nodes with low energy consumption and various functions. They can monitor, collect and send information about the surrounding environment to staff in different environments. The node has the functions of information collection, processing and storage, but considering that its energy is limited, and it mainly depends on the battery that can not be replaced, the information collected has the characteristics of high redundancy and so on. If all these data are sent to the convergent node, it will make the node consume energy too fast and reduce the efficiency of the network. Data fusion data fusion or data aggregation technology is proposed to reduce node energy consumption by using data fusion in wireless sensor networks to reduce the traffic of wireless sensor networks and improve the fusion degree and accuracy of information. One of the main means of prolonging the network life cycle. In this paper, based on the background of environmental monitoring, a clustering routing protocol data fusion model based on neural network for WSN is proposed, which combines the clustering routing protocol of wireless sensor network (WSN) with BP neural network. The neural network method is used to fit the information collected by the nodes in the cluster. On this basis, the network training convergence is accelerated and the network convergence time is shortened by improving the network training parameters. In order to reduce the data flow and save energy consumption, the algorithm can effectively reduce the network traffic, reduce the energy consumption and prolong the network lifetime by sending only the eigenvalues of the data to the convergent Sink node, and the simulation results show that the proposed algorithm can effectively reduce the network traffic, reduce the energy consumption of the nodes and prolong the network life. At the same time, the real-time and effectiveness of this algorithm in environmental monitoring is also verified. Then the data fusion method of fuzzy neural network based on T-S reasoning system is introduced. By studying the learning algorithm of fuzzy neural network, a new improved learning algorithm is proposed. Finally, it combines with clustering routing protocol. A new WSN data fusion model based on fuzzy neural network is proposed. Finally, the simulation results show that the improved algorithm model is more efficient than the traditional T-S fuzzy neural network in terms of network prediction accuracy and network convergence rate. Finally, the node energy consumption is saved and the network life is prolonged. At the same time, it is proved to be feasible and efficient in the water environment monitoring system.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP212.9;TN929.5

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