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首页> 外文期刊>Journal of supercomputing >A novel countermeasure technique to protect WSN against denial-of-sleep attacks using firefly and Hopfield neural network (HNN) algorithms
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A novel countermeasure technique to protect WSN against denial-of-sleep attacks using firefly and Hopfield neural network (HNN) algorithms

机译:一种新的对策技术,可以使用萤火虫和HOHFIELD神经网络(HNN)算法保护WSN免受拒绝睡眠攻击的影响

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摘要

Wireless sensor networks (WSNs) contain numerous nodes that their main goals are to monitor and control environments. Also, sensor nodes distribute based on network usage. One of the most significant issues in this type of network is the energy consumption of sensor nodes. In fixed-sink networks, nodes which are near the sink act as an interface to transfer data of other nodes to sink. This causes the energy consumption of sensors reduces rapidly. Therefore, the lifetime of the network declines. Sensor nodes owing to their weaknesses are susceptible to several threats, one of which is denial-of-sleep attack (DoSA) threatening WSN. Hence, the DoSA refers to the energy loss in these nodes by maintaining the nodes from entering energy-saving and sleep mode. In this paper, a hybrid approach is proposed based on mobile sink, firefly algorithm based on leach, and Hopfield neural network (WSN-FAHN). Thus, mobile sink is applied to both improve energy consumption and increase network lifetime. Firefly algorithm is proposed to cluster nodes and authenticate in two levels to prevent from DoSA. In addition, Hopfield neural network detects the direction route of the sink movement to send data of CH. Furthermore, here WSN-FAHN technique is assessed through wide simulations performed in the NS-2 environment. The WSN-FAHN procedure superiority is demonstrated by simulation outcomes in comparison with contemporary schemes based on performance metrics like packet delivery ratio (PDR), average throughput, detection ratio, and network lifetime while decreasing the average residual energy.
机译:无线传感器网络(WSNS)包含许多节点,它们的主要目标是监视和控制环境。此外,传感器节点根据网络使用分配。这种网络中最重要的问题之一是传感器节点的能耗。在固定汇网络中,靠近接收器的节点充当接口,以将其他节点的数据传输到接收。这导致传感器的能量消耗迅速减少。因此,网络的寿命下降。由于它们的缺点,传感器节点易受若干威胁的影响,其中一个是拒绝睡眠攻击(Dosa)威胁WSN。因此,DOSA通过将节点维持进入节能和睡眠模式,是指这些节点中的能量损失。本文基于基于Leach,Hopfield神经网络(WSN-Fahn)的移动宿研提出了一种混合方法。因此,移动水槽应用于改善能量消耗并增加网络寿命。萤火虫算法被提出到群集节点并以两个级别进行身份验证以防止DOSA。此外,Hopfield神经网络检测到汇位移动的方向路径,以发送CH的数据。此外,这里通过在NS-2环境中进行的广泛模拟来评估WSN-FAHN技术。与基于分组传递比(PDR),平均吞吐量,检测比和网络寿命等性能指标的当代方案相比,通过模拟结果证明了WSN-FAHN程序优势,同时降低了平均剩余能量。

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