A Tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications

The advancement of the Internet of Things (IoT) technologies will play a significant role in the growth of smart cities and industrial applications. Wireless Sensor Network (WSN) is one of the emerging technology utilized for sensing and data transferring processes in IoT-based applications. However, heterogeneous faults like hardware, software, and time-based faults are the major determinants that affect the network stability of IoT based WSN (IWSN) model. In this paper, a novel Energy-Efficient Heterogeneous Fault Management scheme has been proposed to manage these heterogeneous faults in IWSN. Efficient heterogeneous fault detection in the proposed scheme can be achieved by using three novel diagnosis algorithms. The new Tuned Support Vector Machine classifier facilitates to classify the heterogeneous faults where the tuning parameters of the proposed classifier will be optimized through Hierarchy based Grasshopper Optimization Algorithm. Finally, the performance results evident that the diagnosis accuracy of the proposed scheme acquires 99% and the false alarm rate sustains below 1.5% during a higher fault probability rate. The diagnosis accuracy rate is enhanced up to 17% as compared with existing techniques.

Automated summary

The proposed novel Energy-Efficient Heterogeneous Fault Management scheme aims to address hardware, software, and time-based faults in IoT-based Wireless Sensor Networks (IWSN). By using new diagnosis algorithms and a Support Vector Machine classifier, the scheme significantly improves diagnosis accuracy and performance.