In order to solve the routing problem of mobile IOT (IOV)

based on our analyzing the details about motion characteristics of the vehicle and the reasons that cause links down

we set up link model of the duration time and using the duration time as key parameter to design the new routing method. Q-Learning as a kind of heuristic machine learning strategy is able to dynamically adjust the routing path through interaction with the surrounding environment. So a kind of new routing algorithm with adaptivity for mobile IOT based on Q-learning has been presented in this paper. It distributes the learning task into each vehicle node and maintains the reliable routing path by continuously exchanging the beacon information with the neighbor nodes. With the NS-2 simulator

the performance of the algorithm is tested. The results show that it has better performances on delivery

end-to-end delay and average hops in many mobile applications.