A Data Dissemination Algorithm Based on Maximization of Causal Path Entropy in Vehicular Ad Hoc Networks

Efficient data dissemination protocols play a crucial role in transmitting messages among vehicles in vehicular ad hoc networks (VANETs). In scenarios where the destination nodes in VANETs are unknown, it becomes imperative to disseminate the data across as many locations as possible, thereby enhancing the likelihood of reaching potential destinations. While multihop broadcasting facilitates rapid data coverage in networks, it often results in challenges, such as broadcast storms, data redundancy, and transmission delays. This work addresses these issues from the perspective of maximizing the diversity of system evolution, consequently increasing the probability of receiving data by potential destination nodes. To achieve this objective, an efficient data dissemination algorithm causal entropy increase-based dissemination (CEID) is proposed based on causal path entropy theory. By modeling the topology of urban VANETs and calculating the degrees of nodes centrality, the algorithm selects the relay nodes that can increase the diversity of the system during the data dissemination process. This approach aims to maximize the increase of causal path entropy, allowing as many vehicles as possible to receive data quickly while reducing redundancy and collisions during the data dissemination. In addition, a multidirectional synchronous data dissemination strategy is designed for the environment of urban road intersections to achieve fast and efficient data dissemination in the network. Simulation experiments show the superiority of CEID in terms of system entropy increase rate, data transmission delay, and data redundancy when compared to existing algorithms.