A MODEL BASED ON A STOCHASTIC PETRI NET APPROACH FOR DEPENDABILITY EVALUATION OF CONTROLLER AREA NETWORKS

The paper proposes a dependability model to evaluate the behavior of a CAN network in scenarios of transient faults which affect data communications. Fault occurrence is modeled by a Markov Modulated Poisson Process (MMPP) which is capable to describe the typical behavior of electromagnetic interferences (EMI) that occur in industrial environments. An accurate and efficient representation of the network behavior is achieved by adopting a set of assumptions that reduce the pessimism level and which are closer to the real operating conditions. The model is based on Stochastic Petri Nets, which are a high-level modeling formalism able to produce very compact and efficient models, supporting both analytical and simulation solutions. Dependability measures are established from the fulfillment of the real-time constraints (deadlines) defined on messages exchanged between network nodes. Analytical and simulation solutions are both investigated. A case study is proposed to asses both model performance and network dependability.