目录
摘要Ⅱ
关键词Ⅱ
AbstractⅢ
引言
1绪论1
1.1 研究背景 1
1.2 本文主要工作1
2 文献综述2
2.1 物联网的诞生2
2.2 物联网的发展2
2.3 物联网的技术3
2.4 总结3
3 相关技术和算法4
3.1 NS3网络模拟器4
3.2 灰色系统理论 4
3.2.1 GM(1,1)模型 5
3.2.2 GM(1,N)模型 5
3.2.3 灰色预测 6
4 方案论证与实施 7
4.1 论证方案 7
4.2 实施方案 7
5 数据验证与预测 8
5.1 数据处理 8
5.2 检验级比 8
5.3 拟合与预测 9
5.4 残差检验 9
6 实验分析与结论 9
6.1 过程分析 9
6.2 结论 10
7 总结 10
致谢11
参考文献11
附录1 NS3用户脚本13
附录2 MATLAB程序14
基于网络模拟器和灰色系统理论的校园物联网流量建模和预测
摘 要
物联网是基于传统互联网诞生的一种网络，它连接人与物，极大地方便了人们的生活。物联网诞生之初主要用于物品运输管理，但是随着技术发展，融合了更多技术之后，它开始进入人们的平常生活。近年来，物联网越来越频繁地与大数据、云计算在一起被讨论，物联网拥有海量的数据，具有丰富的信息。在校园生活中，我们也随处可见物联网的技术成果，校园中的物联网技术为学生提供及时、便利的服务。而灰色系统理论是由中国学者提出的一种数据预测方法，它使用了关联度分析，根据因素之间的发展态势相似程度衡量因素间的关联的程度，通过此方法建立的模型，能够在数据量较少的情况下对数据进行较好的拟合和预测。本文通过使用网络仿真器生成适合校园物联网场景的网络流量，使用灰色系统理论，给校园物联网中的流量进行建模，拟合了通过 *51今日免费论文网|www.51jrft.com +Q: *351916072* 
仿真器得到的流量数据，并进行进一步的拓展，以预测未来短期内的流量走势，为校园物联网服务器调配资源提供依据。
CAMPUS INTERNET OF THINGS TRAFFIC MODELING AND FORECASTING BASED ON NETWORK SIMULATOR AND GREY SYSTEM THEORY
ABSTRACT
InternetofThings is a networking technology originated in traditional internet which connects people and things to significantly facilitate daily life. At the beginning of its birth, IoT was mainly used for the management of goods transportation. However, it started to enter our life with further developments and merging. In recent years, it becomes hotter and hotter in company with big data and cloud computing, mainly due to its massive data and abundant information. The technological achievements of IoT technology also prevail on campuses, which offer timely and convenient services to the students. The grey system theory is a data prediction method proposed by Chinese scholars which utilizes relevancy analysis, measures correlations between factors according to the similarity of development trends. The models established by this method can be used for better matching and predicting based on relatively fewer data. This article uses a network simulator to generate network traffic which fits IoT scenarios on the campus, establishes models in cooperation with the grey system theory, matches the simulation data and expands to foresee the traffic trends shortterm wisely. At last, it provides the basis for resource allocation by the servers.

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