论文标题
事物的网格（MOT）网络驱动的异常检测在连接的对象中
Mesh of Things (MoT) Network-Driven Anomaly Detection in Connected Objects
论文作者
论文摘要
本文介绍了事物（MOT）网络性能模型的混合网格，以评估端到端数据包输送率（PDR）和延迟。这些PDR和延迟度量用于识别脱角网格以及成功跟踪网格。脱角网格是一个异常的网格，其中一个或多个节点与其他网络网络分开。我们通过考虑空气货物监控应用并使用实验性PDR和延迟数据来证明混合BLE网格-PLC网络的性能模型。蓝牙低能（BLE）网格中的链路不确定性可能归因于（a）RF干扰，（b）〜发射器附近范围和（c）接收器敏感性。相反，电源线通信（PLC）的链路不确定性可以归因于：（a）彩色背景噪声，（b）〜通道频率响应，（c）由于负载状态以及电力线中的变化而出现的脉冲噪声。在我们的工作中，我们构建了一个等效的贝叶斯网络，以追踪网格，使用嘈杂的或嘈杂的添加模型捕获网格链路内的不确定性，并执行信念传播以检测和定位网络异常。
This paper presents a hybrid Mesh of Things (MoT) network performance model to evaluate the end-to-end Packet Delivery Ratio (PDR) and latency. These PDR and latency measures are used to identify both a de-tangled mesh as well as to track the mesh successfully. A de-tangled mesh is a mesh with an anomaly where one or more nodes are separated from the rest of the mesh network. We demonstrate the performance model of a hybrid BLE mesh-PLC network by considering an air cargo monitoring application and validate with experimental PDR and latency data. The link uncertainty in Bluetooth Low Energy (BLE) mesh may be attributed to (a) RF interference,(b)~Transmitter's vicinity range, and (c) Receiver sensitivity. In contrast, the link uncertainty in Power Line Communication (PLC) may be attributed to: (a) Colored background noise, (b)~Channel frequency response, and (c) Impulse noise appearing due to load state as well as variations in the powerline. In our work, we construct an equivalent Bayesian network for the mesh to be tracked, capture the uncertainty within the mesh links using the Noisy-OR and the Noisy-Integer addition model and perform belief propagation to detect and localize a network anomaly.
