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Grey Wolf Optimizer Enhanced SVM for IoT Fault Detection

机译：灰狼优化器增强了SVM，用于IOT故障检测

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Recently, Internet of things (IoT) is invading our daily life, which make it very attractive to industry as well as research community. the functionality of devices is prone to many failures. Discovering these failures is challenging problem due to field of devices deployment or due to device itself as resource constrained. We propose, in this paper, a novel IoT fault detection method. Feature extraction and classification is done by Support Vector Machines. Grey Wolf Optimiser (GWO) is added to the scheme for eliminating the irrelevant and redundant features. GWO optimizer is used to maximize the classification accuracy and to evaluate the selected features for the SVM classifier. This added component reflects the robustness of the proposed solution that has achieved very satisfying results. The overall accuracy is improved reaching 90.28 %.
机译：最近，事物互联网（物联网）正在侵入我们的日常生活，这使得它与工业和研究界非常有吸引力。 设备的功能容易出现许多故障。 发现这些故障是由于设备部署领域的挑战性问题，或者由于设备本身作为资源受限。 我们提出了一种新颖的物联网故障检测方法。 特征提取和分类是通过支持向量机完成的。 灰狼优化器（GWO）被添加到方案中，以消除无关紧要和冗余功能。 GWO优化器用于最大限度地提高分类准确性，并评估SVM分类器的所选功能。 此添加的组件反映了所提出的解决方案的稳健性，该解决方案已经实现了非常令人满意的结果。 整体准确性提高了90.28％。

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