基于网络参数挖掘的无线传感器网络恶意节点检测

IF 0.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Digital Crime and Forensics Pub Date : 2021-09-01 DOI:10.4018/IJDCF.20210901.OA8
R. Sunitha, J. Chandrika
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引用次数: 0

摘要

物联网和联合应用的指数级增长已经更新了学术界对日益精通的路由策略的需求。服务质量(QoS)和降低功耗是有效传输数据的主要要求。目前包括物联网(IoT)通信在内的大部分应用都要求功率有效和qos驱动的WSN配置。本文针对无线传感器网络的QoS和功率效率,设计了一种非常强大和有效的进化计算联合路由协议。所提出的路由约定包括称为基于网络状态的恶意节点检测的熟练能力。它冒险或挖掘动态节点/网络参数来识别恶性节点。实验使用网络模拟器工具NS2完成。结果表明,所提出的路由模型具有较高的吞吐量、较低的能量利用率和较低的延迟,保持了其对实时WSN的适用性。
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Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks
The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.
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来源期刊
International Journal of Digital Crime and Forensics
International Journal of Digital Crime and Forensics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
2.70
自引率
0.00%
发文量
15
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