Distributed Self Intermittent Fault outlier identification technique for WSN s

• Published in: 2022 OITS International Conference on Information Technology (OCIT) pp. 301 - 306
• Main Authors: Gouda, Bhabani Sankar, Das, Sudhakar, Panigrahi, Trilochan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2022
• Subjects: Clustering algorithms; Data communication; Data models; Distributed Fault; Fault diagnosis; Information technology; K-mean; Statistical analysis; Wireless sensor networks; WSN and Neighbor Coordination; z-score
• DOI: 10.1109/OCIT56763.2022.00064

In this research paper, we provide a distributed K-mean strategy based on distributed Self Intermittent fault outer identification (DISF) algorithm for identifying the fault due to outlier. It deals with locating problematic nodes by using intermittent faults and clustering mechanisms in the sensor network. The described model, sensor node, takes into account the average of all the cluster regions by using the median-based K-mean approach to gather the data from nearby sensors within the specified environment. The proposed method is rigorously tested, with the cluster head presumed to be the trustworthy node that reliably supplies the right data. The correctness is determined after taking into account the data from the dispersed cluster heads. With regard to the different parameters are to use for predicting the accuracy of data, fault positive rate and fault alarm ratio over the data transmission. This proposed model is contrasted with other ones already in use. The outcomes of the statistical analysis show that the proposed methodology produces an accurate result as compared to the traditional or existing approaches.