An Improved Heterogeneous Motion Prediction Algorithm for Vehicle-to-Vehicle Message Ferrying in VANETs

In infrastructure-less ad-hoc networks, the nodes can converse directly with other nodes
without centralized servers. The vehicular networks are derived from the features of mobile
ad-hoc networks (MANETs) whereas the message transmissions between the nodes are
direct with each other or through some fixed terminals. But, a unique trademark is that
the vehicle-to-vehicle ad-hoc links are poor because of extreme mobility which leads to
frequent route breakage that causes emergency message delivery failure. The routing protocols
availed in MANETS does not suit due to quick topology changes. The hop selection
is based on the best cumulative communication duration that will be failing in some cases.
Also, the worst/average case scenario is that the minimum connectivity to the source vehicle
is not described in existing approaches. The core idea about the proposed research is to
deliver data through an optimum path and to eliminate rupture link during the packet transmission.
The system uses the Heterogeneous Motion Guess (HMG) algorithm for vehicular
networks to set up a vested path. The HMG will select the relay vehicle’s current position
is near to the target vehicle plus the vehicle will opt the relay forwarder that has good radio
coverage. The link lifetime of the selected hop should be greater than the minimum connectivity
duration with the source vehicle. The selected vehicle will append the link expiry
time and minimum connectivity duration in Route Demand Dispatch message in a unicast
manner. This approach significantly minimizes the hop count numbers and improves the
stability of the path. It radically predicts the link life, so the node can decide an alternative
vested route. The simulation result shows how well the reactive demand-based HMG
affords optimum results in terms of packet transmission success rate, average minimum
hop counts, and communication delay.