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IJEETC 2023 Vol.12(5): 306-316
doi: 10.18178/ijeetc.12.5.306-316

A Graph Correlated Anomaly Detection with Fuzzy Model for Distributed Wireless Sensor Networks

Yasir Abdullah R.1,2*, Mary Posonia A.1, and Barakkath Nisha U.2
1. Sathyabama Institute of Science and Technology, Chennai, India
2. Sri Krishna College of Engineering and Technology, Coimbatore, India

Manuscript received April 15, 2023; revised May 4, 2023; accepted May 11, 2023.

Abstract—Wireless sensor networks have limited power for processing data, storage, and communication. Due to power shortages and anonymous attacks, sensor nodes may produce faulty or anomaly data which affects the accuracy of the entire system. Effective anomaly detection is essential to make an accurate prediction of the result. Moreover, clustering-based anomaly detection reduces energy consumption by avoiding individual sensory data reporting to the base station. The proposed methodology consists of two phases: Correlated graph clustering, and anomaly detection using a Fuzzy model. In the first phase, the spatial correlation of the sensor readings is used to generate a graph, partitioned into clusters. The intra-cluster and inter-cluster temporal correlations are analyzed to refine the optimized cluster structure. Finally, a fuzzy Mamdani model is used to classify the clusters as either normal or anomalous based on their membership values. The proposed approach leverages both spatial and temporal correlation between sensor measurements to form optimized clusters that are more effective for anomaly detection. The Experiments performed on a real-world dataset of WSNs indicate the efficacy of the proposed methodology, which shows significant improvement over traditional anomaly detection methods the electronic file of your paper will be formatted further for final publication.

Index Terms—Anomaly detection, clustering, data aggregation, graph theory, wireless sensor networks

Cite: Yasir Abdullah R., Mary Posonia A., and Barakkath Nisha U., "A Graph Correlated Anomaly Detection with Fuzzy Model for Distributed Wireless Sensor Networks," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 12, No. 5, pp. 306-316, September 2023. doi: 10.18178/ijeetc.12.5.306-316

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.