A hybrid fault diagnosis architecture for Wireless Sensor Networks

Zafar, Amna; Wajid, Bilal; Akram, Beenish Ayesha

doi:10.1109/icosst.2015.7396395

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…To the best of authors' knowledge, most of the current fault diagnosis schemes do not substantiate relationships between fault diagnosis and inter-process correlations of functional peers. In [19], the authors presented a hybrid fault diagnosis architecture based on inter-process communication of routing and MAC layer processes. However, PHY layer processes were not handled.…”

Section: Communication Protocols Analysis For Fault Diagnosismentioning

confidence: 99%

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

2019

KSII TIIS

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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.

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Section: Communication Protocols Analysis For Fault Diagnosismentioning

confidence: 99%

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

2019

KSII TIIS

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“…To the best of authors' knowledge, existing network monitoring schemes do not establish relationship between fault diagnosis and inter-process communication of functional peers. In [16], a hybrid fault diagnosis architecture based upon intra-process communication and inter-process dependencies is proposed by the authors.…”

Section: Diagnostic Agent Based Inter-process Communication Aware Monitoring System For Wireless Sensor Networkmentioning

confidence: 99%

Diagnostic Agent Based Inter-Process Communication Aware Monitoring System for Wireless Sensor Networks

Zafar

Akber

2019

Mehran Univ. res. j. eng. technol.

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Process failures are instigated by underlying errors and faults in various layers of WSN (Wireless Sensor Network) communication protocol stack. Therefore, efficient and effective monitoring systems for fault detection and diagnosis are imperative for fault tolerance and robust operation of WSN to meet critical application requirements for reliability and throughput. Existing detection-diagnosis regimen are either centralized or distributed and network monitoring is performed passively or actively. This work presents a diagnostic agent based inter-process communication aware monitoring system for WSNs. Diagnostic agent actively performs probe-based process execution tracking and examines the effects of errors, omissions and channel misbehavior on process execution at node, link and network levels to implement failure detection and fault diagnosis. Such diagnosis is performed through the inference of inter-process communication of stacked and peer layer processes on sender and receiver side. The monitoring system has been implemented in Castalia simulator for WSN. Local diagnostic agent is implemented on sensor nodes for self-monitoring and network wide fault diagnosis is performed by global diagnostic agent on cluster head. Simulation results show that the system performs robust root cause analysis of critical process failures due to errors in stacked and peer layer processes. The decentralized distribution of diagnostic load on sensor nodes and cluster head produces lesser communication overhead and is energy efficient.

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“…Once the fault has been detected, the next step is to classify it. Network faults can be broadly classified into two types: hardware and software [148]. Hardware faults are related to the malfunctioning of nodes' components (e.g., microcontroller, transceiver, sensor, and batteries).…”

Section: Fault Classificationmentioning

confidence: 99%

A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

Salas¹

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Proponemos un marco conceptual para acoplar técnicas de auto-organización y técnicas de autocuración. A este marco se le llama de auto-curación y es capaz de hacer frente a enlaces inalámbricos inestables y nodos defectuosos. Dividimos el marco en dos componentes principales: la auto-organización y auto-curación. En el componente de auto-organización, nosotros construimos una topología de árbol que determine las rutas hacia el sumidero. En el componente de auto-curación, la topología del árbol se adapta a ambos tipos de fallas siguiendo tres pasos: recopilación de información, detección de fallas, y la recuperación de fallos. En el paso de recopilación de información, los nodos determinan el estado actual de la red mediante la recopilación de información de la capa MAC. En el paso de detección de fallas, los nodos analizan la información recopilada y detectan nodos/enlaces defectuosos. En el paso de recuperación de fallos, los nodos recuperan la topología del árbol mediante la sustitución de componentes defectuosos con redundantes (es decir, componentes de respaldo). Este marco permite una red con resiliencia que se recupera sin agotar los recursos de la red.

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A hybrid fault diagnosis architecture for Wireless Sensor Networks

Cited by 11 publications

References 9 publications

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

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

Diagnostic Agent Based Inter-Process Communication Aware Monitoring System for Wireless Sensor Networks

A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

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