Abstract : Sensors play a vital role in Internet of Things (IoT) monitoring applications. However, the harsh nature of the environment influences negatively on the sensors reliability. They may occasionally generate inaccurate measurements when they are exposed to high level of humidity, temperature, etc. Forwarding incorrect data to the base station may cause disastrous decision-making in critical applications. To address this problem, we proposed a new fault-tolerant mechanism based on Bayesian Networks that carefully solve such a problem. The mechanism is called Reliability of Captured Data (RCD); it enables sensors to both detect and recover faulty data. To show the performance of our algorithm, we compared this latter with another recent algorithm called Fault Detection Scheme (FDS) using Network Simulator 2 (NS2). The results showed that our strategy gives remarkable enhancements in terms of fault detection accuracy and fault alarm rate.
https://hal.inria.fr/hal-01913875
Contributor : Hal Ifip
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Yasmine Harbi, Zibouda Aliouat, Sarra Hammoudi. Enhancement of IoT Applications Dependability Using Bayesian Networks. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.487-497, ⟨10.1007/978-3-319-89743-1_42⟩. ⟨hal-01913875⟩
