Abstract
With the rapid development of Internet of Vehicles (IoV) technology, IoV applications have been developed from their infant stage to intermediate stage, i.e. from primarily providing entertainment and navigation services to travel guidance and energy saving driving. As the communication technology determines the overall performance of IoV, we first elaborate the architecture of IoV and algorithms of communication schedule. To improve the efficiency and accuracy of information reception and processing, we then propose a new dynamic priority strategy towards key data. The strategy includes a framework of five parts which are the application layer, libraries, the scheduling layer, operation system and hardware. The five parts above have completed the processes of data sending, data transmission and data receiving. In terms of data transmission, a new formula for updating the priorities of data has been put forward along with a procedure for taking feedback into account. A series of experiments have been conducted to validate the performance of the newly proposed strategy. The results have showed that the priority strategy and the updating formula for the priorities are effective in IoV environment.
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References
- 1.
Kim D et al (2017) A new comprehensive RSU installation strategy for cost-efficient vanet deployment. IEEE Trans Veh Technol 66:4200–4421
- 2.
Bouk S et al (2017) Named-data-networking based ITS for smart cities. IEEE Commun Mag 55:105–111
- 3.
Tian D, Zhou J, Sheng Z (2017) An adaptive fusion strategy for distributed information estimation over cooperative multi-agent networks. IEEE Trans Inf Theory 63:3076–3091
- 4.
Chochlidakis G et al (2017) Mobility aware virtual network embedding. IEEE Trans Mobile Comput 16:1343–1356
- 5.
Kerrache CA et al (2019) TACASHI: trust-aware communication architecture for social internet of vehicles. IEEE Internet Things J 6:5870–5877
- 6.
Yang H et al (2019) intelligent resource management based on reinforcement learning for ultra-reliable and low-latency IoV communication networks. IEEE Trans Veh Technol 68(5):4157–4169
- 7.
Silva R, Iqbal R (2019) Ethical implications of social internet of vehicles systems. IEEE Internet Things J 6(1):517–531
- 8.
Wang J et al (2018) Internet of vehicles: sensing-aided transportation information collection and diffusion. IEEE Trans Veh Technol 67(5):3813–3825
- 9.
Liu Y, Wang Y, Chang G (2017) Efficient privacy-preserving dual authentication and key agreement scheme for secure V2V communications in an IoV paradigm. IEEE Trans Intell Transp Syst 18(10):2740–2749
- 10.
Iqbal R et al (2018) Context-aware data-driven intelligent framework for fog infrastructures in internet of vehicles. IEEE Access 6:58182–58194
- 11.
Bello O, Zeadally S (2016) Intelligent device-to-device communication in the internet of things. IEEE Syst J 10:1172–1182
- 12.
El-Sayed H et al (2019) Trust enforcement in vehicular networks: challenges and opportunities. IET Wirel Sens Syst 9:237–246
- 13.
Wan S et al. (2019) Efficient computation offloading for internet of vehicles in edge computing-assisted 5G networks. J Supercomput 1–30
- 14.
Tian D et al (2016) Robust energy-efficient MIMO transmission for cognitive vehicular networks. IEEE Trans Veh Technol 65:3845–3859
- 15.
Wang Y et al (2016) Delivery delay analysis for roadside unit deployment in vehicular ad hoc networks with intermittent connectivity. IEEE Trans Veh Technol 65:8591–8602
- 16.
Aadil F et al (2018) Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). J Supercomput 74:4542–4567
- 17.
Yao W et al (2019) A secured and efficient communication scheme for decentralized cognitive radio-based internet of vehicles. IEEE Access 7:160889–160900
- 18.
Kou F et al (2019) Common semantic representation method based on object attention and adversarial learning for cross-modal data in IoV. IEEE Trans Veh Technol 68(12):11588–11598
- 19.
Kibilda J et al (2016) Modelling multi-operator base station deployment patterns in cellular networks. IEEE Trans Mob Comput 15:3087–3099
- 20.
Jo Y, Jeong JRPA (2016) Road-side units placement algorithm for multihop data delivery in vehicular networks. In: Proceedings of the 30th International Conference on Advanced Information Networking and Applications Workshops, Crans-Montana, Switzerland, pp 262–266
- 21.
Wang J, Liao J et al (2016) Game-theoretic model of asymmetrical multipath selection in pervasive computing environment. Pervasive Mob Comput 27:37–57
- 22.
Cumbal R et al. (2016) Optimum deployment of RSU for efficient communications multi-hop from vehicle to infrastructure on VANET. In: Proceedings of the IEEE Colombian Conference on Communications and Computing, Cartagena, Colombia, pp 1–6
- 23.
Zhang H et al (2016) Smart identifier network: a collaborative architecture for the future internet. IEEE Netw 30:46–51
- 24.
Luan T et al (2015) Feelbored? Joinverse! Engineering vehicular proximity social networks. IEEE Trans Veh Technol 64:1120–1131
- 25.
Wang C et al (2019) SaliencyGAN: deep learning semi-supervised salient object detection in the fog of IoT. IEEE Trans Ind Inf 16(6):2667–2676
- 26.
Wang M et al (2015) Real-time path planning based on hybrid-VANET enhanced transportation system. IEEE Trans Veh Technol 64:1664–1678
- 27.
Wang M et al (2015) Asymptotic throughput capacity analysis of VANETs exploiting mobility diversity. IEEE Trans Veh Technol 64:4187–4202
- 28.
Jianwei H et al (2020) A survey on digital forensics in internet of things. IEE Internet Things J 7:1–15
- 29.
Zhao P et al. (2019) A survey of local differential privacy for securing internet of vehicles. J Supercomput 1–2
- 30.
Shancang L et al (2019) Blockchain-based digital forensics investigation framework in the internet of things and social systems. IEEE Trans Comput Soc Syst 6:1433–1441
- 31.
Chen MS et al (2019) Driving behaviors analysis based on feature selection and statistical approach: a preliminary study. J Supercomput 75:2007–2026
- 32.
Huibing Z et al (2019) An efficient IoV trajectory compression method in vehicle terminals using width-direction-angle. IEEE Access 7:71447–71458
- 33.
Liuand J, Kato N (2015) Device-to-device communication overlaying two-hop multi-channel uplink cellular networks. In: Proceedings of ACM MobiHoc, pp 307–316
- 34.
Liu J, Zhang S, Nishiyama H, Kato N, Guo J (2015) A stochastic geometry analysis of D2D overlaying multi-channel downlink cellular networks. In: Proceedings of IEEE INFOCOM, pp 46–54
- 35.
Kumar N et al (2015) Collaborative learning automata-based routing for rescue operations in dense urban regions using vehicular sensor networks. IEEE Syst J 9:1081–1090
- 36.
Tian D et al (2015) A dynamic and self-adaptive network selection method for multi-mode communications in heterogeneous vehicular telematics. IEEE Trans Intell Transp Syst 16:3033–3049
- 37.
Delen D et al (2017) Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods. J Transp Health 4:118–131
- 38.
Rasheed A et al (2017) Vehicular ad hoc network (VANET): a survey, challenges, and applications. Adv Intell Syst Comput 548:39–51
- 39.
Ouyous M et al (2017) Multi-channel coordination based MAC protocols in vehicular ad hoc networks (VANETs): a survey. Lect Notes Electr Eng 397:81–94
- 40.
Zhaolong N et al (2016) Integration of scheduling and network coding in multi-rate wireless mesh networks. Optim Models Algorithms. 36:386–397
- 41.
Wazid M et al (2019) AKM-IoV: authenticated key management protocol in fog computing-based internet of vehicles deployment. IEEE Internet Things J 6(5):8804–8817
- 42.
Li J et al (2019) When I/O interrupt becomes system bottleneck: efficiency and scalability enhancement for SR-IOV network virtualization. EEE Trans Cloud Comput 7(4):1183–1196
- 43.
Wen S, Guo Ge (2017) Static-dynamic hybrid communication scheduling and control co-design for network control systems. ISA Trans 71:553–562
- 44.
Malik A et al (2018) Satisfiability modulo theory (SMT) formulation for optimal scheduling of task graphs with communication delay. Comput Oper Res 89:113–126
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Huang, C., Wang, H., Guo, D. et al. A dynamic priority strategy for IoV data scheduling towards key data. J Supercomput 77, 2018–2032 (2021). https://doi.org/10.1007/s11227-020-03350-7
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Keywords
- Internet of vehicles
- Data transmission
- Communication schedule
- Dynamic priority