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

• Published in: IEEE transactions on intelligent transportation systems 2019-01, Vol.20 (1), p.218-231
• Main Authors: Dai, Penglin, Liu, Kai, Feng, Liang, Zhang, Haijun, Lee, Victor Chung Sing, Son, Sang Hyuk, Wu, Xiao
• Format: Article
• Language: English
• Subjects: Ad hoc networks; Adaptation models; Bandwidth; Bandwidths; Communications systems; Computer simulation; Data dissemination; Data integrity; Evolutionary algorithms; evolutionary multi-objective optimization; Information services; Intelligent transportation systems; Knowledge management; Multiple objective analysis; Optimization; Performance evaluation; Routing; temporal information services; Vehicle dynamics; Vehicle-to-infrastructure; Vehicular networks; Wireless communications; Wireless networks
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2018.2803842

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 called MO-TDUD , which consists of a decomposition scheme for handling multiple objectives, a scalable chromosome representation for TDUD solution encoding, and an evolutionary operator designed for TDUD solution reproduction. The proposed MO-TDUD can 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 implement MO-TDUD for performance evaluation. The comprehensive simulation results demonstrate the superiority of the proposed solution.