A Novel Fault Diagnosis Technique for Wireless Sensor Network Using Feedforward Neural Network
A Novel Fault Diagnosis Technique for Wireless Sensor Network Using Feedforward Neural Network
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DOI：
10.1109/lsens.2021.3136590
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发表时间：
2022-01-01
影响因子：
2.8
通讯作者：
Baghel, Rajendra Kumar
中科院分区：
文献类型：
--
作者：
Prasad, Rahul;Baghel, Rajendra Kumar
近年来，无线传感器网络的故障诊断研究一直是一个具有挑战性的课题。WSN中的传感器（SNs）通过火山、森林、高速公路等环境进行长时间部署；因此，它们经常遭受意想不到的故障。现有的故障诊断技术，如分布式、混合、集中式等，都是将一个SN感知到的数据传输到相邻的SN、簇头或基站，从而判断该SN的故障状态。因此，这些技术的能源效率很低，并且存在较高的通信开销和延迟，从而降低了网络的总体生命周期。此外，这些现有技术依赖于节点之间的时空相关性来检测网络中的故障网络。因此，为了解决这些问题，我们提出了一种新的故障诊断技术，其中每个SN使用自己的感知数据来检测其故障状态。将该方法的性能与现有的故障诊断技术进行了比较，验证了其有效性。实验结果表明，所提出的故障诊断技术在各种性能指标上都优于现有的故障诊断技术。
In recent times, research on fault diagnosis for wireless sensor networks (WSNs) has been a challenging task. The sensors (SNs) in WSN are deployed in the environment through volcanoes, forests, highways, etc., for extended periods; hence, they are subjected to frequent and unexpected faults. Existing fault diagnosis techniques such as distributed, hybrid, and centralized require transmitting the data sensed by an SN to their neighboring SNs, cluster head, or base station to identify their fault status. Hence, these techniques are energy-inefficient and suffer from high communication overhead and delay, which decreases the overall lifetime of the network. Also, these existing techniques depend on the spatial-temporal correlation between the nodes to detect the faulty SNs in the network. Therefore, to address these issues, we propose a novel fault diagnosis technique where each SN detects its fault status using its own sensed data. The performance of the proposed approach has been compared with the existing fault diagnosis techniques to demonstrate its effectiveness. The experimental results show the efficiency of the proposed fault diagnosis technique over existing techniques in terms of the various performance metrics.