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An Autonomous Fault-Awareness model adapted for upgrade performance in clusters of homogeneous wireless sensor networks
Wireless Networks ( IF 3 ) Pub Date : 2020-06-03 , DOI: 10.1007/s11276-020-02381-5
Walaa M. Elsayed , Hazem M. El-Bakry , Salah M. El-Sayed

Wireless sensor networks (WSNs) have conquered comprehensive survey progressions in the regular control and management fields. Although WSN allows the spatial monitoring of real-world events, the mobility action depletes a huge part of a sensor’s energy cost in wireless communication. WSN sensors are often prone to various faults as frequent crashes and temporary or permanent failures. This is because it propagates them in very complex and harsh environments. So, we tend to design a Self-Adaptive based Autonomous Fault-Awareness (SAAFA) model, to limit the impact of such failures and filter them. In this paper, we incorporate the two of adaptive-filters FIR with RLS through three adaptive two-stages performed at the level of cluster head, for independent fault-correction during the propagation platform. The proposed model (SAAFA) included two stages, the first stage comprised self-detection the failure and self-aware for the lost scales, in which relied on responses of delay port and prior-knowledge of absent sensor-signals throughout monitoring, through adjusting the filter weights in the adaptive feedback loop for awarding convergent signals for the lost ones. The second stage is adaptive filtering the registered signals from the above stage for gaining pure measures and free of interferences. Compared to the state-of-the-art methods, the scheduled model attained a speed in diagnosing faults and awareness the missing readings with a rate of accuracy reached 98.8% improving the robustness of performance. Evaluation criteria revealed the progress of SAAFA in reducing the radio communication to ~ 97.47% that kept about 93.7% of battery-energy throughout the picked dataset sample. Hence, it expanded the whole network lifetime.



中文翻译:

自主故障感知模型适用于同类无线传感器网络集群中的升级性能

无线传感器网络(WSN)征服了常规控制和管理领域的全面调查进展。尽管WSN允许对现实事件进行空间监视,但是移动性动作消耗了无线通信中传感器能源成本的很大一部分。WSN传感器通常容易发生各种故障,例如频繁崩溃和临时或永久性故障。这是因为它在非常复杂和恶劣的环境中传播它们。因此,我们倾向于设计一种基于自适应的自主故障意识(SAAFA)模型,以限制此类故障的影响并对其进行过滤。在本文中,我们通过在簇头级别执行的三个自适应两阶段,将两个自适应滤波器FIR与RLS结合在一起,以便在传播平台期间进行独立的故障校正。所提出的模型(SAAFA)包括两个阶段,第一阶段包括自我检测故障和对丢失标度的自我意识,其中依赖于延迟端口的响应以及在整个监控过程中通过调整来预先了解传感器信号的缺失自适应反馈环路中的滤波器权重,用于补偿丢失信号的收敛信号。第二阶段是对来自上一级的注册信号进行自适应滤波,以获得纯净的测量结果并且没有干扰。与最先进的方法相比,该计划模型在诊断故障和识别丢失的读数方面达到了速度,准确率达到了98.8%,从而提高了性能的鲁棒性。评估标准揭示了SAAFA在将无线电通信减少到〜97.47%的过程中取得的进展,这一进展保持了约93个。整个选取的数据集样本中电池能量的7%。因此,它延长了整个网络的寿命。

更新日期:2020-06-03
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