1. • Irresponsible Forwarding--In a self-organizing traffic information system, vehicles share and distribute traffic information by rebroadcasting a received information packet to their neighbors. However, it is inefficient to let every vehicle rebroadcast the information packet, since the redundant packets waste the valuable (finite) radio channel bandwidth. Reducing the number of redundant packets, while still ensuring good coverage and reachability, is one of the main objectives in multi-hop broadcasting. In this research, we propose a new probabilistic-based rebroadcast scheme, called Irresponsible Forwarding, where each vehicle rebroadcasts a received information on the basis of (i) its distance from the source and (ii) the density of its neighbors. The key idea is that a vehicle implicitly evaluates the probability that there is another vehicle which can rebroadcast more successfully: if this probability is sufficiently high, then the vehicle ``irresponsibly'' does not rebroadcast.
   

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About Me

I’m currently an assistant professor in the Mobile Computing and Sensor Networks Lab, Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok Thailand. I received the B.S., M.S., and Ph.D. degrees from Carnegie Mellon University, Pittsburgh, PA, USA in 2000, 2002, and 2006, respectively, all in electrical and computer engineering.

Research Interests

1. • Ad Hoc Wireless Networks
   

2. • Vehicular Networks
   

3. • Intelligent Transportation Systems (ITS)
   

4. • Performance Modeling

Last update: 15-Feb-2013

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Recent Work

1. • Vehicle Density Estimation--Vehicle density is one of the main metrics used for assessing the road traffic condition. A high vehicle density indicates that the traffic is congested. Currently, most of the vehicle density estimation approaches are designed for infrastructure-based traffic information systems. These approaches require detecting devices such as inductive loop detectors or traffic surveillance cameras to be installed at various locations. Consequently, they are not appropriate for an emerging self-organizing vehicular traffic information system, where the vehicles have to collect and process the traffic information without relying on any fixed infrastructure.  In this work, we consider a few methods for estimating the vehicle density based on the number of vehicles in the vicinity of the probe vehicle and based on the number of vehicles in a communication cluster.
   

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