期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 7, 页码 9466-9477出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3122438
关键词
Software-defined vehicular network; social computing; context prediction; predictive routing
资金
- Department of Science and Technology of Liaoning Province
- Young and Middle-Aged Science and Technology Innovation Talent Support Plan of Shenyang [RC190026]
- Science Foundation of Liaoning Province [2020-MS-237]
- Liaoning Provincial Department of Education Science Foundation [JYT19052]
- Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring Research Fund, Fujian Normal University [202001]
- National Natural Science Foundation of China [61872086]
- Science Foundation of Liaoning Province
Software-defined vehicular network separates network management and data transmission to provide intelligent information exchanges. The proposed social computing inspired predictive routing scheme (SPIDER) in this paper enables low-latency reliable data exchange under dynamic vehicular networks.
Software-defined vehicular network (SDVN) is a promising networking paradigm that can provide intelligent information exchanges by separating network management and data transmission. Although the transmission quality of vehicles can he greatly improved by deploying softwarized networking schemes, critical networking issues such as the timeliness of data packets remain due to the dynamic nature of vehicular networks. It is vital to design efficient networking schemes by deeply considering the characteristics of the network, transportation system, and users, to improve overall network performance. To this end, this paper proposes a social computing inspired predictive routing scheme (SPIDER) for SDVNs that has a comprehensive consideration to enable low-latency reliable data exchange under dynamic vehicular networks. As for the link lifetime grounded on the vehicular historical data, we introduce the context feature mining and one-shot prediction method to predict vehicle movements with considering the energy saving. We also involve social computing techniques to find the relay nodes with good data spreading abilities. The extensive experiments prove our proposed scheme outperforms four existing schemes.
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