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Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

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Abstract

In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem and a recently introduced multiobjective swarm optimization (2LB-MOPSO) algorithm is used to find an optimum trade-off between detection accuracy and detection latency. Faulty sensor nodes are identified based on comparisons of sensed data between one-hop neighboring nodes. Time redundancy is used to detect intermittent faults since an intermittent fault does not occur consistently. Simulation and analytical results show that sensor nodes with permanent faults are identified with high accuracy and by properly choosing the inter-test interval most of the intermittent faults are isolated with negligible performance degradation.

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Notes

  1. Faults are classified as: crash, omission, timing, and Byzantine. Crash faults are hard faults, and all others can be treated as soft faults.

  2. Perfect test: a fault is always detected by the test when it occurs, and is isolated.

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Authors and Affiliations

  1. National Institute of Science and Technology, Berhampur, India

    Arunanshu Mahapatro & Ajit Kumar Panda

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  1. Arunanshu Mahapatro
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  2. Ajit Kumar Panda
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Correspondence to Arunanshu Mahapatro.

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Mahapatro, A., Panda, A.K. Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach. Wireless Pers Commun 78, 649–669 (2014). https://doi.org/10.1007/s11277-014-1776-1

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