【摘要】：無線傳感器網(wǎng)絡是由部署在監(jiān)控區(qū)域的傳感器節(jié)點采用多跳方式傳輸數(shù)據(jù)而構成的自組織網(wǎng)絡,已在諸多領域得到了廣泛應用。由于傳感器節(jié)點采用低功耗功率進行無線電通信,并且其所處環(huán)境惡劣、復雜多變,從而導致節(jié)點之間的通信鏈路不穩(wěn)定。若能及時感知鏈路質量信息,為轉發(fā)數(shù)據(jù)提供路由參考,則能有效減少數(shù)據(jù)重傳次數(shù),提高網(wǎng)絡數(shù)據(jù)吞吐率。因此有效的鏈路質量預測方法對于提高數(shù)據(jù)傳輸?shù)某晒β�、延長網(wǎng)絡生存期非常重要。論文介紹了無線鏈路特性和現(xiàn)有的鏈路質量預測方法,分析了鏈路質量參數(shù)的定義和相關性。在此分析的基礎上,提出基于高斯過程回歸(Gaussian Process Regression,GPR)的鏈路質量預測模型。物理層參數(shù)實時靈敏,而直接測量包接收率需要消耗的能量比較多,因此本文構建物理層參數(shù)和包接收率之間的非線性映射關系。由于鏈路質量參數(shù)之間存在信息冗余,會降低模型的訓練速度,本文首先利用灰關聯(lián)分析方法分析鏈路質量參數(shù)之間的灰關聯(lián)度,選取有效影響因子;再結合鏈路質量時間序列特點,選取合適的協(xié)方差函數(shù),構建鏈路質量預測模型。無線鏈路通信易受到所處空間環(huán)境、地理位置、無線信號的影響和干擾。論文研究對象為節(jié)點靜止的無線傳感器網(wǎng)絡,選取大學校園樹林、教學樓實驗室、圖書館廣場和公路四個場景部署實驗,收集處于不同方向和距離的多對節(jié)點之間的實驗數(shù)據(jù)。論文分析了不同場景下各節(jié)點對之間的鏈路波動情況以及不同鏈路質量參數(shù)之間的灰關聯(lián)度,確定預測模型的輸入?yún)?shù)。論文選取兩種鏈路進行實驗分析和模型驗證。實驗結果表明,降維后的實驗數(shù)據(jù)樣本依然涵蓋了鏈路質量信息,沒有對預測準確性造成影響;在兩種鏈路場景下,基于組合協(xié)方差函數(shù)形式的高斯過程回歸模型的預測性能強于基于單一協(xié)方差函數(shù)的模型;與基于支持向量回歸機的模型相比,本文提出的模型具有更好的預測精度。
[Abstract]:Wireless sensor network (WSN) is a self-organized network which is composed of sensor nodes deployed in monitoring area and transmits data in multi-hop mode. It has been widely used in many fields. The wireless communication of sensor nodes is based on low power consumption, and the environment is harsh and complex, which leads to the instability of communication links between the nodes. If we can perceive link quality information in time and provide routing reference for forwarding data, we can effectively reduce the number of data retransmissions and improve the throughput of network data. Therefore, effective link quality prediction method is very important to improve the success rate of data transmission and prolong the network lifetime. In this paper, the characteristics of wireless link and the existing link quality prediction methods are introduced, and the definition and correlation of link quality parameters are analyzed. Based on this analysis, a link quality prediction model based on Gao Si process regression (Gaussian Process) is proposed. The physical layer parameters are sensitive in real time, and the direct measurement of packet reception rate requires more energy consumption. Therefore, a nonlinear mapping relationship between physical layer parameters and packet reception rate is constructed in this paper. Because of the information redundancy among the link quality parameters, the training speed of the model will be reduced. Firstly, the grey correlation degree between link quality parameters is analyzed by using the grey correlation analysis method, and the effective influence factors are selected. According to the characteristics of link quality time series, a link quality prediction model is constructed by selecting appropriate covariance function. Wireless link communication is easily affected and interfered by the space environment, geographical location and wireless signal. The research object of this paper is wireless sensor network with static nodes. Four scene deployment experiments are selected, including university campus forest, teaching building laboratory, library square and highway, and the experimental data between multiple pairs of nodes in different directions and distances are collected. In this paper, the link fluctuation between each node pair in different scenarios and the grey correlation between different link quality parameters are analyzed, and the input parameters of the prediction model are determined. In this paper, two kinds of links are selected for experimental analysis and model verification. The experimental results show that the data samples after dimensionality reduction still cover the link quality information and have no effect on the prediction accuracy. The prediction performance of Gao Si process regression model based on combined covariance function is better than that based on single covariance function, and the proposed model has better prediction accuracy than the model based on support vector regression machine.
【學位授予單位】：南昌航空大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP212.9;TN929.5

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