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  • State space reduction for sensor networks using two-level partial order reduction

    Author(s)
    Zheng, M
    Sanań, D
    Sun, J
    Liu, Y
    Dong, JS
    Gu, Y
    Griffith University Author(s)
    Dong, Jin-Song
    Year published
    2013
    Metadata
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    Abstract
    Wireless sensor networks may be used to conduct critical tasks like fire detection or surveillance monitoring. It is thus important to guarantee the correctness of such systems by systematically analyzing their behaviors. Formal verification of wireless sensor networks is an extremely challenging task as the state space of sensor networks is huge, e.g., due to interleaving of sensors and intra-sensor interrupts. In this work, we develop a method to reduce the state space significantly so that state space exploration methods can be applied to a much smaller state space without missing a counterexample. Our method explores the ...
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    Wireless sensor networks may be used to conduct critical tasks like fire detection or surveillance monitoring. It is thus important to guarantee the correctness of such systems by systematically analyzing their behaviors. Formal verification of wireless sensor networks is an extremely challenging task as the state space of sensor networks is huge, e.g., due to interleaving of sensors and intra-sensor interrupts. In this work, we develop a method to reduce the state space significantly so that state space exploration methods can be applied to a much smaller state space without missing a counterexample. Our method explores the nature of networked NesC programs and uses a novel two-level partial order reduction approach to reduce interleaving among sensors and intra-sensor interrupts. We define systematic rules for identifying dependence at sensor and network levels so that partial order reduction can be applied effectively. We have proved the soundness of the proposed reduction technique, and present experimental results to demonstrate the effectiveness of our approach.
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    Journal Title
    Lecture Notes in Computer Science
    Volume
    7737 LNCS
    DOI
    https://doi.org/10.1007/978-3-642-35873-9_30
    Subject
    Software engineering not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/172902
    Collection
    • Journal articles

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