Internet of Things (IoT) refers to a system of interconnected heterogeneous smart devices communicating without human intervention. A significant portion of existing IoT networks is under the umbrella of ad-hoc and quasi ad-hoc networks. Ad-hoc based IoT networks suffer from the lack of resource-rich network infrastructures that are able to perform heavyweight network management tasks using, e.g. machine learning-based Network Traffic Monitoring and Analysis (NTMA) techniques. Designing light-weight NTMA techniques that do not need to be (re-) trained has received much attention due to the time complexity of the training phase. In this study, a novel pattern recognition method, called Trend-based Online Network Traffic Analysis (TONTA), is proposed for ad-hoc IoT networks to monitor network performance. The proposed method uses a statistical light-weight Trend Change Detection (TCD) method in an online manner. TONTA discovers predominant trends and recognizes abrupt or gradual time-series dataset changes to analyze the IoT network traffic. TONTA is then compared with RuLSIF as an offline benchmark TCD technique. The results show that TONTA detects approximately 60% less false positive alarms than RuLSIF.

物联网（IoT）指的是互连的异构智能设备的系统，无需人工干预即可进行通信。现有物联网网络的很大一部分都处于ad-hoc网络和准ad-hoc网络的保护之下。基于Ad-hoc的IoT网络缺少资源丰富的网络基础架构，这些资源无法使用例如基于机器学习的网络流量监控和分析（NTMA）技术来执行重量级的网络管理任务。由于训练阶段的时间复杂性，设计不需要（重新）训练的轻量级NTMA技术受到了广泛的关注。在这项研究中，提出了一种新的模式识别方法，称为基于趋势的在线网络流量分析（TONTA），用于自组织IoT网络监控网络性能。所提出的方法以在线方式使用统计轻量趋势变化检测（TCD）方法。TONTA发现主要趋势并识别突然或逐渐的时间序列数据集更改以分析IoT网络流量。然后，将TONTA与RuLSIF作为离线基准TCD技术进行比较。结果表明，TONTA检测到的误报率比RuLSIF大约低60％。