Vehicular delay-tolerant networks are widely used in intelligent transport application. Vehicle nodes exchange and share various information in vehicular delay-tolerant networks. However, current delay-tolerant network routing algorithms do not take into account the dynamic characteristic of traffic flow, and they do not effectively resist cyber attacks, such as black hole attack. To address this issue, we propose a data dissemination mechanism for vehicular delay-tolerant networks. In this mechanism, we develop a combined model to estimate the real-time traffic density. Simultaneously, we propose the metrics which include node interaction dispersion, node interaction freshness, node interaction participation, and node interaction contribution to evaluate behavior of nodes. Based on these metrics, a routing method is constructed. In this routing method, a relay node is selected by evaluating communication interaction behaviors among vehicle nodes. Considering the factors of traffic flow density and communication behaviors of vehicle nodes, a message forwarding strategy scheme is built for different traffic density scenarios. Extensive simulations show that the proposed mechanism exhibits superior performance over existing methods in forwarding traffic information and alleviates negative effects from black hole attacks.

SummaryTraffic information of disparate types in Internet of Vehicles (IoV) is the basis for supporting various IoV applications. Since traffic flows vary in real time, it is challenging to carry out efficient and reliable transfer of such information. Moreover, IoV is vulnerable to security threats due to its inherent properties such as dynamically changing topology and high‐speed motion of vehicles. Attacks, once launched successfully, would also disrupt message transmission between vehicles. We propose an adaptive traffic information forwarding mechanism, which divides traffic information in two categories, that is, early warning information and service information. The former is handled by selecting a relay node based on node connection stability evaluation, while the latter is handled by adopting an appropriate forwarding method according to the identified traffic flow density. Specifically, in a low‐density environment, we employ a broadcast method; while in a high‐density environment, we evaluate the cognitive interaction values of vehicle nodes and employ a cognitive interaction‐based method to select a relay node. Simulation results show that the proposed mechanism improves the forwarding efficiency of traffic information and yields satisfactory performance in mitigating black hole attacks.