PhD defence - Lobna Nassar

Candidate

Lobna Nassar

Title

Use of Information Retrieval Techniques in Building Context Aware Systems for VANET [Vehicular Ad hoc NETwork]

Supervisors

Fakhreddine Karray and Mohamed Kamel

Abstract

The proposed IR-CAS systems are Vehicular Ad hoc Network (VANET) context aware systems that utilize the information retrieval (IR) model techniques like indexing, document scoring and document similarity in the context aware information dissemination. It uses a hybrid context model that enhances scalability. It improves the dispatched information by employing the vector space model for estimating the degree of relevance or severity by calculating the Manhattan distance between the current situation context and the severest context vectors. The proposed model is compared to the binary and fuzzy logic IR models and shows highest effectiveness by binary and non-binary measures. Three IR-CAS systems are developed for three VANET applications: the safety Automatic Crash Notification (ACN), the convenience Congested Road Notification (CRN) and the commercial Service Announcement (SA) systems. They outperform current systems in decentralization, exploitation of vehicle processing power, dissemination of informative notifications with certainty degrees and partial rather than binary or graded notifications that are insensitive to differences in severity within grades, and protection of privacy which achieves user satisfaction. First, the IR-CAS SA utilizes hybrid vehicular communication (HVC) and uses smart service grouping which raises filtering efficiency due to reduction in connection time. Second, the IR-CAS ACN improves the BMW Advanced ACN (AACN) by fully automating the solution. Third, the IR-CAS CRN uses V2V communication as the ACN for efficiency and outperforms decentralized implementations with partial notifications, like the CoTEC fuzzy system, by differentiating among service levels of unsaturated freeways and precisely distinguishing severities of oversaturated ones. The visual-manual and speech-visual dual-mode user interface is designed to improve user safety by minimizing distraction. An evaluation model containing ACN and CRN test collections, with around 500,000 North American test cases each, is designed to enable fair effectiveness comparisons among VANET context aware systems. Hence, the novelty of VANET IR-CAS systems is: First, providing scalable abstract hybrid context model with IR based processing that raises the notification relevance, certainty and precision. Second, increasing decentralization, user privacy, and safety with the least distracting user interface. Third, designing unbiased performance evaluation system as a ground for distinguishing significantly effective VANET context aware systems.