Tutorial 4: Date & time: TBA
Vehicular Ad-Hoc Network (VNAT)
Dr. Taieb Znati
Computer Science Department
Imagine the day when traffic movements are assessed in real time and collected data is used to prevent car collisions; the day when car automatically determine current road conditions and avoid traffic congestion; the day when highway deaths are reduced to zero. Are we ever going to get there? The Vehicular Ad-Hoc Network (VANET) technology is enabling a wide range of safety and non-safety critical applications and services with the potential to move us closer to these targets. The objective of this tutorial is to discuss the unique networking and communications requirements of VANETs, provide a detailed descriptions of the state-of-the-art in the area of vehicular technology, ranging from communications, networking and applications to security and privacy, discuss current standards and standardization efforts arcos the world, and outline challenges and future research directions to enable large-scale, efficient and safe vehicular ad-hoc networks and applications.
The specific topics to be covered in this tutorial include:
Prof. Taieb Znati Biography:
Dr. Znati obtained the MS (Computer Science) from Purdue University, and the PhD (Computer Science, 1988) from Michigan State University.
He joined the faculty of the Department of Computer Science with a joint appointment in the Graduate Program in Telecommunications at the University of Pittsburgh in 1988. Prior to that he was a member of the Syrius research group headed by Dr. G. Lelan at INRIA (France), which investigated issues related to the design and analysis of distributed data base systems.
Dr. Znati's current research interests focus on the design of network level channel abstractions for real-time communication networks to support multimedia environments, the design and analysis of medium access control protocols to support distributed real-time systems, and the investigation of fundamental design issues related to distributed systems in the areas of machine learning, cognitive modeling, problem solving, and analogical reasoning.