SECURE AND ROBUST WSN ARCHITECTURE: DETECTION OF MALICIOUS NODES AND IMPACT ASSESSMENT ON LINK STABILITY.
Wireless Sensor Networks (WSNs) have become a core technology for modern applications, including surveillance, smart agriculture, and Internet of Things (IoTs) ecosystems, but they remain highly vulnerable to malicious nodes that compromise link reliability and communication performance. Conventiona...
| Published in: | Scientific Culture Vol. 12; no. 1, Part 1; pp. 4189 - 4206 |
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| Main Authors: | , , |
| Format: | Article |
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University of the Aegean
2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=192213845&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 192213845 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 1, Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 192213845 10.5281/zenodo.121126311 ppf: 4189 ppct: 17 formats: tig: atl: SECURE AND ROBUST WSN ARCHITECTURE: DETECTION OF MALICIOUS NODES AND IMPACT ASSESSMENT ON LINK STABILITY. aug: au: Sharma, Priyanka Kidwai, Mohd Suhaib Charan, Piyush affil: ECE Department, Integral University Lucknow, UP, India. ECE Department, Manav Rachna University, Faridabad, Haryana, India. su: Wireless sensor networks Anomaly detection (Computer security) Computer network security Long short-term memory Reinforcement learning Deep learning Network performance sug: subj: Wireless sensor networks Anomaly detection (Computer security) Computer network security Long short-term memory Reinforcement learning Deep learning Network performance keyword: Graph Convolutional Networks Internet of Things Link stability Long Short-Term Memory Malicious nodes Q learning SMOTE Wireless Sensor Networks ab: Wireless Sensor Networks (WSNs) have become a core technology for modern applications, including surveillance, smart agriculture, and Internet of Things (IoTs) ecosystems, but they remain highly vulnerable to malicious nodes that compromise link reliability and communication performance. Conventional anomaly detection and cryptographic approaches are often inadequate due to the resource limitations of sensor nodes and the dynamic nature of network topologies. This study aims to design a secure and robust detection framework that accurately identifies malicious nodes while maintaining link stability. The paper proposes a hybrid model integrating federated Graph Convolutional Networks (GCN) for spatial learning and Long ShortTerm Memory (LSTM) networks for temporal sequence analysis, combined with Q learning for self-healing routing. The methodology uses the WSN BFSF dataset with multi-domain feature extraction across traffic, energy, topology, and storage, class balancing with SMOTE, and feature selection through mutual information. Experimental validation using NS3 simulations demonstrates a classification accuracy of 99.70 percent with precision, recall, and F1 scores exceeding 0.996 for all classes. Link stability also improved significantly, with packet delivery ratio rising from 0.57 to 1.0 and average delay reduced from 3.32 to 3.09 units. The findings confirm that the proposed framework enhances both the detection of malicious activity and the resilience of wireless sensor networks in real-world scenarios. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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