MDiag: Mobility-assisted diagnosis for wireless sensor networks

  • Authors:

  • Junjie Xiong;Yangfan Zhou;Michael R. Lyu;Evan F. Y. Young

  • Affiliations:

  • Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China and Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong;Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China and Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong;Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China and Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong;Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

  • Venue:
  • Journal of Network and Computer Applications
  • Year:
  • 2013

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Abstract

Though widely employed in various applications, wireless sensor networks (WSNs) are liable to failures, especially after deployment. Since the on-site failures are difficult to reproduce, it is of critical importance to perform in-situ diagnosis. Current in-situ diagnosis methods are either intrusive or inefficient, because they either inject diagnosis agents into each sensor node or build up another network for diagnosis purpose. To tackle these issues, we propose MDiag, a mobility-assisted diagnosis approach that employs smartphones to patrol the WSNs and diagnose failures. Diagnosing with a smartphone which is not a component of WSNs does not intrude the execution of the WSNs. Moreover, patrolling the smartphone in the WSNs to investigate failures is more efficient than deploying another diagnosis network. Statistical rules are designed to guide the detection of abnormal cases. Aiming at improving the patrol efficiency, a patrol approach MSEP (maximum snooping efficiency patrol) is proposed. MSEP is designed to achieve better performance than the naive method, the greedy method, and the baseline method in increasing the detection rate and reducing the patrol time of MDiag. Experiments with real sensor nodes and emulations validate the effectiveness of MDiag in detecting anomalous cases caused by faults.