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arXiv · 1403.2821

Towards an Agent-Oriented Modeling and Evaluation Approach For Vehicular Systems Security

Mohamed Garoui·Belhassen Mazigh·Béchir El Ayeb·Abderrafiaa Koukam
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Abstract

Agent technology is a software paradigm that permits to implement large and complex distributed applications. In order to assist the development of multi-agent systems, agent-oriented methodologies (AOM) have been created in the last years to support modeling more and more complex applications in many different domains. By defining in a non-ambiguous way concepts used in a specific domain, Meta modeling may represent a step towards such interoperability. In the Transport domain, this paper propose an agent-oriented meta-model that provides rigorous concepts for conducting transportation system problem modeling. The aim is to allow analysts to produce a transportation system model that precisely captures the knowledge of an organization so that an agent-oriented requirements specification of the system-to-be and its operational corporate environment can be derived from it. To this end, we extend and adapt an existing meta-model, Extended Gaia, to build a meta-model and an adequate model for transportation problems. Our new agent-oriented meta-model aims to allow the analyst to model and specify any transportation system as a multi-agent system. Based on the proposed meta-model, we proposes an approach for modeling and evaluating the Transportation System based on Stochastic Activity Network (SAN) components. The proposed process is based on seven steps from Recognition phase to Quantitative Analysis phase. These analyzes are based on the Dependability models which are built using the formalism Stochastic Activity Network. A real case study of Urban Public Transportation System has been conducted to show the benefits of the approach.

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Mohamed Garoui, Belhassen Mazigh, Béchir El Ayeb, Abderrafiaa Koukam. 2014-03-12. Towards an Agent-Oriented Modeling and Evaluation Approach For Vehicular Systems Security. https://arxiv.org/abs/1403.2821

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