Performance analysis of self-learning forwarding algorithms for Vehicle-to-Vehicle networks on Named Data Networking (NDN)

Vehicle Ad-Hoc Network (VANET) is a special network that provides wireless communication for moving vehicles and other infrastructure to reduce accident rates and collect various information related to the vehicle environment that is useful as a support for driver navigation to improve the safety of drivers and vehicle passengers. The network architecture currently being developed, namely Named Data Networking (NDN) implemented in VANET is an innovative technology to meet future information needs. The self-learning forwarding algorithm is one of the forwarding strategies whose working mechanism avoids running routing protocols, the network can learn routes by itself after broadcasting packets of Interest Base on successful data packet retrieval routes. Self-learning forwarding algorithm based on broadcast is used in VANET networks on NDN which can enable networks to adapt to changing environments and enable mobility of vehicle nodes without using a routing mechanism on NDN networks, thereby reducing computation time and speeding up the information exchange process.