Showing papers by "Celimuge Wu published in 2008"

An enhanced reinforcement routing protocol for inter-vehicular unicast application

Abstract: In Vehicular Ad-Hoc Network (VANET), as a result of frequent changes of network topology caused by vehicle's movement, the general purpose ad hoc routing protocols such as AODV and DSR cannot work efficiently. This paper proposed a VANET routing protocol QLAODV which fits for unicast application in high mobility scenario. QLAODV is a distributed reinforcement learning routing protocol, which uses Q-Learning algorithm to infer network state information and uses unicast control packets checking the availability of paths in a real time manner in order to allow Q-Learning to work efficiently in highly dynamic network environment. In this paper, we show the performance analysis of QLAODV by simulation with NS2 in different mobility models, and give the simulation results confirming that QLAODV outperforms original AODV significantly in highly dynamic networks.