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|Title:||State space reduction for sensor networks using two-level partial order reduction|
|Source:||Zheng, M.,Sanań, D.,Sun, J.,Liu, Y.,Dong, J.S.,Gu, Y. (2013). State space reduction for sensor networks using two-level partial order reduction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7737 LNCS : 515-535. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-35873-9_30|
|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 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. © Springer-Verlag 2013.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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