Temporal Information Services in Large-Scale Vehicular Networks Through Evolutionary Multi-Objective Optimization

Temporal information services are critical in implementing emerging intelligent transportation systems. Nevertheless, it is challenging to realize timely temporal data update and dissemination due to an intermittent wireless connection and a limited communication bandwidth in dynamic vehicular networks. Some previous studies have considered the temporal data dissemination in vehicular networks, but they are limited to the service region, which is inside the coverage of roadside units. To enhance system scalability, it is imperative to exploit the synergic effect of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications for providing efficient temporal information services in such an environment. With the above motivations, we propose a novel system architecture to enable efficient data scheduling in hybrid V2I/V2V communications by having the global knowledge of network resources of the system. On this basis, we formulate a temporal data upload and dissemination (TDUD) problem, aiming at optimizing two conflict objectives simultaneously, which are enhancing the data quality and improving the delivery ratio. Furthermore, we propose an evolutionary multi-objective algorithm calledMO-TDUD, which consists of a decomposition scheme for handling multiple objectives, a scalable chromosome representation forTDUDsolution encoding, and an evolutionary operator designed forTDUDsolution reproduction. The proposedMO-TDUDcan be adaptive to different requirements on data quality and delivery ratio by selecting the best solution from the derived Pareto solutions. Last but not least, we build the simulation model and implementMO-TDUDfor performance evaluation. The comprehensive simulation results demonstrate the superiority of the proposed solution.