Abstract:
The growing rate of interest in Vehicular Communication Networks and vehicular adhoc
networks (VANETs) over the recent years is alarming. This shows in the emergence
and amazing realities of Intelligent Transportation System (ITS). Vehicular communication networks are designed in such a way that it supports traffic monitoring, vehicular safety such as speed information, condition of the road ahead and other vehicular applications. However, communication within this dynamic environment tends to encounter major challenges, one of which is void problem. Hence a recovery strategy that will guarantee packet delivery in such networks is required. The widely used face routing for recovery strategy to guarantee packet delivery relies on planar graph which is not always achievable in realistic wireless networks and may generate long paths. This study presents the development of a C-GRACO, a cluster- based geographic routing technique that combines a greedy forwarding and a recovery strategy based on swarm
intelligence. In recovery, ant packet search for optimal path taking advantage of stored
node location information and drops pheromone trails to lead the upcoming packets
through the network. This study employs the use of both static and dynamic
infrastructure, such as the Road Side Units (RSU): street signs, traffic light. These
facilities and their embedded modules help to achieve efficient routing technique and
enable smooth communication between vehicles in a low density or sparse scenario. CGRACO avoids detours, handles cascaded void with its optimal path technique. The
research work is implemented using Virtual Robotic Experiment Platform with Lua
programming language and Database Management System. Simulation results shows
that C-GRACO produces a distinct improvement on performance and scalability when
compared to GRACO.
