Multipath Based Congestion Propagation via Information Network Interaction in IIoT

The Industrial Internet of Things (IIoT) has found extensive applications in intelligent transportation. However, as the number of vehicles increases, the issue of traffic congestion becomes more prominent, emphasizing the need for accurate congestion propagation prediction to enhance traffic conditions. Current methods for predicting congestion propagation lack consideration for the influence of communication networks and do not incorporate the path characteristics of congestion propagation. Therefore, we propose a path-based congestion propagation model, UAU_SIS_Path, employing multigrain abstraction of traffic congestion and information propagation. Specifically, UAU_SIS_Path effectively captures the propagation dynamics of congestion in IIoT by leveraging the interaction of two-layer networks and the path propagation characteristics of traffic congestion. Subsequently, we validate the effectiveness of the UAU_SIS_Path model through theoretical analysis, establishing tight upper and lower bounds of the propagation threshold and elucidating the relationship between the propagation of congestion information in the information network and the diffusion of congestion in the road network. Finally, based on theoretical analysis, we examine the impact of our model on congestion control strategies, utilizing path replanning, and traffic restriction as congestion control strategies. Experimental results in simulated road network BA and real road networks of varying sizes (GC, TA, and As) demonstrate the stability and scalability of our model. In comparison to the traditional contact-based propagation model, our model achieves a reduction in congestion propagation rates of 58.2%, 66.9%, 32.6%, and 48.6%, respectively.