TMODF: Trajectory-based multi-objective optimal data forwarding in vehicular networks

Vehicular networks have been increasingly used for applications like road infrastructure monitoring and traffic jam detection, etc. Data forwarding is a well-known challenging problem in vehicular networks, which suffers from delay and error due to the frequent network disruption and fast topological change. The minimizations of the delivery delay and network cost are both central to data forwarding in vehicular networks. However, previous works usually focus on only one of the two objectives and most of them do not make good use of vehicle trajectory information. In this paper, we formulate the V2V (vehicle to vehicle) data forwarding problem as a novel multi-objective Markov Decision Process (MDP). We exploit the vehicle trajectory information and traffic statistics to estimate the parameters of the MDP (i.e., transition probabilities, rewards). The optimal routing policy is then developed by solving the multi-objective MDP. We conduct extensive simulations on a taxi network in a mega-city, the experimental results validate the effectiveness of our proposed mechanism.