本文選題：物聯(lián)網(wǎng) + 流量特性�。� 參考：《西南交通大學》2017年碩士論文

【摘要】：大量測量數(shù)據(jù)表明傳統(tǒng)的互聯(lián)網(wǎng)流量具有自相似(或長相關(guān)性)特性,該特性對網(wǎng)絡(luò)性能評價和業(yè)務(wù)建模技術(shù)產(chǎn)生了很大的影響。物聯(lián)網(wǎng)具有不同于傳統(tǒng)互聯(lián)網(wǎng)的特點,包括低移動性、上行占優(yōu)、沒有人的參與等,因此物聯(lián)網(wǎng)流量特性將會發(fā)生重大地變化。網(wǎng)絡(luò)流量特性研究是網(wǎng)絡(luò)規(guī)劃設(shè)計和性能評估的基礎(chǔ),對了解網(wǎng)絡(luò)的運行規(guī)律、保證網(wǎng)絡(luò)安全具有非常重要的作用。本文在分析了物聯(lián)網(wǎng)的發(fā)展、體系結(jié)構(gòu)和應(yīng)用模式的基礎(chǔ)上,對四種典型的物聯(lián)網(wǎng)業(yè)務(wù)進行了分析,包括遠程醫(yī)療、智能農(nóng)業(yè)、自動駕駛和自動售貨,依據(jù)分析結(jié)果構(gòu)建相應(yīng)的物聯(lián)網(wǎng)業(yè)務(wù)流量模型,根據(jù)業(yè)務(wù)流量模型產(chǎn)生仿真流量,分析了流量在不同時間尺度、不同流量密度和不同人參與程度下的Hurst參數(shù)和方差特性。結(jié)果表明網(wǎng)絡(luò)數(shù)據(jù)流量在不同情況下具有不同的流量特性。同種業(yè)務(wù)在不同流量密度下,有的自相似特性隨著流量密度的增加而增加,有的比較穩(wěn)定。同一種業(yè)務(wù)產(chǎn)生的流量,有的時間尺度上具有自相似特性,有的時間尺度上沒有。然后分析了具有不同特性的流量聚合之后流量的特性,發(fā)現(xiàn)沒有自相似特性的流量聚合之后依然沒有自相似特性,具有自相似特性的流量聚合以后仍然具有自相似特性,當沒有自相似特性的流量和具有自相似特性的流量聚合之后,聚合流量的特性和組成聚合流量的各類業(yè)務(wù)的Hurst參數(shù)和方差參數(shù)有關(guān)。接著分析了在不同方差和Hurst參數(shù)情況下聚合流量的特性,發(fā)現(xiàn)流量方差對聚合流量有著很大的影響。最后通過OPNET仿真,分析了網(wǎng)絡(luò)流量參數(shù)對網(wǎng)絡(luò)時延特性的影響,發(fā)現(xiàn)網(wǎng)絡(luò)流量的方差和Hurst參數(shù)對網(wǎng)絡(luò)性能都有很大的影響。
[Abstract]:A large number of measured data show that the traditional Internet traffic has self-similar (or long correlation) characteristics, which has a great impact on network performance evaluation and business modeling technology. The Internet of things is different from the traditional Internet of things, including low mobility, uplink dominance, no participation and so on, so the characteristics of Internet of things traffic will change greatly. The study of network traffic characteristics is the basis of network planning, design and performance evaluation, which plays an important role in understanding the operation rules of the network and ensuring the network security. Based on the analysis of the development, architecture and application mode of the Internet of things, this paper analyzes four typical Internet of things businesses, including telemedicine, intelligent agriculture, self-driving and self-selling. According to the analysis results, the corresponding traffic model of the Internet of things is constructed. According to the traffic model, the Hurst parameters and variance characteristics of the traffic are analyzed under different time scales, different traffic density and different degree of participation. The results show that network data traffic has different traffic characteristics under different conditions. Under different traffic density, some self-similar characteristics of the same service increase with the increase of traffic density, and some are more stable. The traffic generated by the same service is self-similar in time scale and not in time scale. Then, the characteristics of traffic after flow aggregation with different characteristics are analyzed. It is found that the flow aggregation without self-similarity still has no self-similarity, and the flow aggregation with self-similarity still has self-similarity. When traffic without self-similarity and traffic with self-similarity are aggregated, the characteristics of aggregate traffic are related to Hurst parameters and variance parameters of various services that make up aggregate traffic. Then, the characteristics of aggregate flow under different variance and Hurst parameters are analyzed, and it is found that traffic variance has great influence on aggregate flow. Finally, through OPNET simulation, the influence of network traffic parameters on network delay characteristics is analyzed. It is found that both network traffic variance and Hurst parameters have great influence on network performance.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.5;TP393.06;TP391.44

【參考文獻】

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1 劉婉Y，

本文編號：2097870