MetaDetect：面向多无人机无线网络的基于蜕变测试的异常检测
MetaDetect: Metamorphic Testing Based Anomaly Detection for Multi-UAV Wireless Networks
AI总结：
针对多无人机WANET异常难以提前预测的问题，提出基于蜕变测试的MetaDetect方法，仅利用物理层和Mac层数据自动识别异常事件，并发现物理层传输速率指标性能最佳。
摘要：
无线自组织网络（WANET）通信的可靠性远低于有线网络。WANET会受到节点过载、路由协议、天气、障碍物遮挡以及许多其他因素的影响，所有这些异常都无法避免。在人们能够进行网络重路由或切换到不同频段之前，提前准确预测网络完全停止至关重要。在本研究中，有两个主要目标。首先，基于蜕变测试（MT）方法设计异常事件检测模式。其次，比较各项评估指标的性能，例如传输速率、占用率以及接收数据包数量。与其他研究相比，其最显著的优势在于数学可解释性，并且不需要依赖物理环境信息，仅依赖网络物理层和Mac层数据。结果分析表明，所提出的MT检测方法有助于自动识别WANET上的事件/事故。物理层传输速率指标能够获得最佳性能。
英文摘要：
The reliability of wireless Ad Hoc Networks (WANET) communication is much lower than wired networks. WANET will be impacted by node overload, routing protocol, weather, obstacle blockage, and many other factors, all those anomalies cannot be avoided. Accurate prediction of the network entirely stopping in advance is essential after people could do networking re-routing or changing to different bands. In the present study, there are two primary goals. Firstly, design anomaly events detection patterns based on Metamorphic Testing (MT) methodology. Secondly, compare the performance of evaluation metrics, such as Transfer Rate, Occupancy rate, and the Number of packets received. Compared to other studies, the most significant advantage of mathematical interpretability, as well as not requiring dependence on physical environmental information, only relies on the networking physical layer and Mac layer data. The analysis of the results demonstrates that the proposed MT detection method is helpful for automatically identifying incidents/accident events on WANET. The physical layer transfer Rate metric could get the best performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
