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A dynamic priority strategy for IoV data scheduling towards key data

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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

    Google Scholar 

  2. Bouk S et al (2017) Named-data-networking based ITS for smart cities. IEEE Commun Mag 55:105–111

    Article  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  4. Chochlidakis G et al (2017) Mobility aware virtual network embedding. IEEE Trans Mobile Comput 16:1343–1356

    Article  Google Scholar 

  5. Kerrache CA et al (2019) TACASHI: trust-aware communication architecture for social internet of vehicles. IEEE Internet Things J 6:5870–5877

    Article  Google Scholar 

  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

    Article  Google Scholar 

  7. Silva R, Iqbal R (2019) Ethical implications of social internet of vehicles systems. IEEE Internet Things J 6(1):517–531

    Article  Google Scholar 

  8. Wang J et al (2018) Internet of vehicles: sensing-aided transportation information collection and diffusion. IEEE Trans Veh Technol 67(5):3813–3825

    Article  Google Scholar 

  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

    Article  Google Scholar 

  10. Iqbal R et al (2018) Context-aware data-driven intelligent framework for fog infrastructures in internet of vehicles. IEEE Access 6:58182–58194

    Article  Google Scholar 

  11. Bello O, Zeadally S (2016) Intelligent device-to-device communication in the internet of things. IEEE Syst J 10:1172–1182

    Article  Google Scholar 

  12. El-Sayed H et al (2019) Trust enforcement in vehicular networks: challenges and opportunities. IET Wirel Sens Syst 9:237–246

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  16. Aadil F et al (2018) Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). J Supercomput 74:4542–4567

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  19. Kibilda J et al (2016) Modelling multi-operator base station deployment patterns in cellular networks. IEEE Trans Mob Comput 15:3087–3099

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  24. Luan T et al (2015) Feelbored? Joinverse! Engineering vehicular proximity social networks. IEEE Trans Veh Technol 64:1120–1131

    Article  Google Scholar 

  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

    Google Scholar 

  26. Wang M et al (2015) Real-time path planning based on hybrid-VANET enhanced transportation system. IEEE Trans Veh Technol 64:1664–1678

    Article  Google Scholar 

  27. Wang M et al (2015) Asymptotic throughput capacity analysis of VANETs exploiting mobility diversity. IEEE Trans Veh Technol 64:4187–4202

    Article  Google Scholar 

  28. Jianwei H et al (2020) A survey on digital forensics in internet of things. IEE Internet Things J 7:1–15

    Article  Google Scholar 

  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

    Article  Google Scholar 

  31. Chen MS et al (2019) Driving behaviors analysis based on feature selection and statistical approach: a preliminary study. J Supercomput 75:2007–2026

    Article  Google Scholar 

  32. Huibing Z et al (2019) An efficient IoV trajectory compression method in vehicle terminals using width-direction-angle. IEEE Access 7:71447–71458

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  38. Rasheed A et al (2017) Vehicular ad hoc network (VANET): a survey, challenges, and applications. Adv Intell Syst Comput 548:39–51

    Google Scholar 

  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

    Article  Google Scholar 

  40. Zhaolong N et al (2016) Integration of scheduling and network coding in multi-rate wireless mesh networks. Optim Models Algorithms. 36:386–397

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  43. Wen S, Guo Ge (2017) Static-dynamic hybrid communication scheduling and control co-design for network control systems. ISA Trans 71:553–562

    Article  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Informatics, Xiamen University, Xiamen, People’s Republic of China

    Chenxi Huang & Fan Lin

  2. School of Civil Engineering, Tsinghua University, Beijing, People’s Republic of China

    Hao Wang

  3. School of Electronic and Information Engineering, Tongji University, Shanghai, People’s Republic of China

    Dong Guo

  4. School of Software Engineering, Tongji University, Shanghai, People’s Republic of China

    Guokai Zhang

  5. Department of Computer Science and Technology, Tongji University, Shanghai, People’s Republic of China

    Gaowei Xu

  6. School of Computer and Information, Anhui Normal University, Wuhu, People’s Republic of China

    Wen Zhou

  7. Department of Computer Science and Technology, University of Hull, Hull, England

    Yongqiang Cheng

  8. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, England

    Yonghong Peng

  9. The Affiliated Changshu Hospital of Soochow University (Changshu No.1 People’s Hospital), Suzhou, People’s Republic of China

    Kaijian Xia

  10. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, People’s Republic of China

    Kaijian Xia

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  1. Chenxi Huang
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  2. Hao Wang
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  3. Dong Guo
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  6. Wen Zhou
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  9. Kaijian Xia
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  10. Fan Lin
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Correspondence to Kaijian Xia or Fan Lin.

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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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  • DOI: https://doi.org/10.1007/s11227-020-03350-7

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