Delay-Tolerant Distributed Algorithms for Decision-Making in Vehicular Networks

Learning a fast global model that describes the observed phenomenon well is a crucial goal in the inherently distributed Vehicular Networks. This global model is further used for decision-making, which is especially important for some safety-related applications (i.e., the altering of accident and warning of traffic jam). Most existing works have ignored the network overhead caused by synchronizing with neighbors, which inevitably delays the time for agents to stabilize. In this paper, we focus on developing an asynchronous distributed clustering algorithm to learn the global model, where cluster models, rather than raw data points, are shared and updated. Empirical experiments on a message delay simulator show the efficiency of our methods, with a reduced convergence time, declined network overhead and improved accuracy (relative to the standard solution). This algorithm is further improved by introducing a tolerant delay. Compared to the algorithm without delay, the performance is improved significantly in terms of convergence time (by as much as 47%) and network overhead (by around 53%) if the underlying network is geometric or regular.