Vanet Ir-Cas

Abstract

The proposed VANET IR-CAS is a context aware system that utilizes information retrieval (IR) techniques, such as indexing, document scoring and document similarity, to enhance context aware information dissemination in VANET. It uses a hybrid context model; spatial model for service filtering, ontology model for context reasoning and knowledge sharing, markup model for file exchange, and situational model for safety and convenience services. Its VANET OWL ontology managed by Jena semantic web framework succeeded in formalizing the semantics of VANET context domain and heightened the system abstraction level. Relevance of dispatched information to prospective recipients is enhanced by employing IR techniques and partial relevance. For commercial services, we used the hybrid vehicular communication (HVC) to increase the decentralized processing, exploit the vehicle processing power and increase user satisfaction and privacy. V2V is used for safety and convenience services where the level of abstraction has increased by using high level situation context attributes. In addition, more precise application notifications are now feasible after improving reasoning about situation certainty and severity. Hence, the main novelty of VANET IR-CAS is that it provides a highly abstract hybrid context model with IR based processing that raises the notification relevance, certainty and precision beside increasing decentralization and user satisfaction.

Evaluating VANET information retrieval context aware systems using the average distance measure ADM

“…Multilevel context modeling is used in these systems to represent the vehicle context relevant to the considered application. A detailed description of VANET IR-CAS and its applications can be found in [14,15,16]. Two of these applications are discussed in this section; the ACN and CRN applications.…”