  Efficient routing algorithms based on vehicular ad hoc networks (VANETs) play
an important role in emerging intelligent transportation systems. This highly
dynamic topology faces a number of wireless communication service challenges.
In this paper, we propose a protocol based on reinforcement learning and
vehicle node clustering, the protocol is called Qucts, solve
vehicle-to-fixed-destination or V2V messaging problems. Improve message
delivery rates with minimal hops and latency, link stability is also taken into
account. The agreement is divided into three levels, first cluster the
vehicles, each cluster head broadcasts its own coordinates and speed, to get
more cluster members. Also when a cluster member receives another cluster head
broadcast message, the cluster head generates a list of surrounding clusters,
find the best cluster to the destination as the next cluster during message
passing. Second, the protocol constructs a Q-value table based on the state
after clustering, used to participate in the selection of messaging clusters.
Finally, we introduce parameters that express the stability of the vehicle
within the cluster, for communication node selection. This protocol hierarchy
makes Qucts an offline and online solution. In order to distinguish unstable
nodes within a cluster, Coding of each road, will have vehicles with planned
routes, For example, car hailing and public bus. Compare the overlap with other
planned paths vehicles in the cluster, low overlap is labeled as unstable
nodes. Vehicle path overlap rate without a planned path is set to the mean
value. Comparing Qucts with existing routing protocols through simulation, Our
proposed Qucts scheme provides large improvements in both data delivery rate
and end-to-end delay reduction.
