With the wide application of wireless sensor networks, network security has been a terrible problem when it provides many more services and applications. Rapid usage of internet and connectivity demands a network anomaly system combating cynical network attacks. Meanwhile, it is a common approach for acquiring, which can be used by network operators to carry out network management and configuration. Moreover, a great number of evaluations have been proposed to simulate and analyse the Wireless Sensor Network traffic, it is still a remarkable challenge since, and network traffic characterization has been tremendously changed, in particular, for a sensor computing network. Bayesian Based Network Traffic Prediction (BNTP) is proposed to solve the deep learning of statistical features of network traffic flow so that all the packets were sent to the receiver properly without any traffic density. Bayesian network-based peer-to-peer network traffic design is proposed to determine the spatial structure of traffic flow. PVM fault localization feature is proposed to remove the accuracy measure issues and performance problems. The co-existence mechanism is used to minimize the inference and overlap problem in wireless network devices. This paper avoids the conflicts in traffic analysis and statistical features of the network. The performance of the network is increased to 80% when compared to the existing methods.

随着无线传感器网络的广泛应用，当提供更多服务和应用程序时，网络安全已成为一个可怕的问题。Internet和连接的快速使用要求网络异常系统与愤世嫉俗的网络攻击作斗争。同时，这是一种常见的获取方法，网络运营商可以使用它来进行网络管理和配置。此外，已经提出了大量评估来模拟和分析无线传感器网络流量，因为这仍然是一个巨大的挑战，而且网络流量特性已发生了巨大变化，尤其是对于传感器计算网络而言。提出了基于贝叶斯的网络流量预测（BNTP）技术，以解决网络流量统计特征的深度学习问题，使所有数据包都能正确地发送到接收方，而不会产生任何流量密度。提出了一种基于贝叶斯网络的点对点网络流量设计方法，用于确定流量的空间结构。提出了PVM故障定位功能，以消除精度测量问题和性能问题。共存机制用于最小化无线网络设备中的推理和重叠问题。本文避免了网络流量分析和统计特性上的冲突。与现有方法相比，网络的性能提高到80％。提出了一种基于贝叶斯网络的点对点网络流量设计方法，用于确定流量的空间结构。提出了PVM故障定位功能，以消除精度测量问题和性能问题。共存机制用于最小化无线网络设备中的推理和重叠问题。本文避免了网络流量分析和统计特性上的冲突。与现有方法相比，网络的性能提高到80％。提出了一种基于贝叶斯网络的点对点网络流量设计方法，用于确定流量的空间结构。提出了PVM故障定位功能，以消除精度测量问题和性能问题。共存机制用于最小化无线网络设备中的推理和重叠问题。本文避免了网络流量分析和统计特性上的冲突。与现有方法相比，网络的性能提高到80％。本文避免了网络流量分析和统计特性上的冲突。与现有方法相比，网络的性能提高到80％。本文避免了网络流量分析和统计特性上的冲突。与现有方法相比，网络的性能提高到80％。