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http://dx.doi.org/10.3837/tiis.2019.02.004

Inter-Process Correlation Model based Hybrid Framework for Fault Diagnosis in Wireless Sensor Networks  

Zafar, Amna (Department of Computer Science and Engineering, University of Engineering and Technology)
Akbar, Ali Hammad (Department of Computer Science and Engineering, University of Engineering and Technology)
Akram, Beenish Ayesha (Department of Computer Science and Engineering, University of Engineering and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.2, 2019 , pp. 536-564 More about this Journal
Abstract
Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.
Keywords
Correlation; process; error classification; fault diagnosis; wireless sensor network;
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