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Neural network based congestion detection protocol

机译:基于神经网络的拥塞检测协议

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摘要

Congestion in Wireless Sensor Networks (WSNs) is an important challenge. Congestion causes packets loss, energy consumption as well as delay which decrease the sensor lifetime. For this reasons, congestion detection is crucial for WSNs. In this paper, a Neural Network Congestion Detection (NNCD)is proposed. It allows each intermittent node to estimate congestion and to identify malicious nodes by the use of local information such as participants, buffer occupancy, and traffic rate as input for neural network. Simulation results show that NNCD can detect the congestion level for intermittent nodes.
机译:无线传感器网络(WSN)的拥塞是一项重要的挑战。拥塞会导致数据包丢失,能耗以及延迟,从而缩短传感器寿命。因此,拥塞检测对于WSN至关重要。在本文中,提出了一种神经网络拥塞检测(NNCD)。它允许每个间歇节点使用本地信息(例如参与者,缓冲区占用率和流量速率)作为神经网络的输入来估计拥塞并识别恶意节点。仿真结果表明,NNCD可以检测间歇节点的拥塞程度。

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