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Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks

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Abstract

With recent development in vehicular communication technologies, much attention has been paid to data dissemination in vehicular networks. In particular, the infrastructure-to-vehicle (I2V) communication is one of the primary technologies to provide a variety of information services. To enhance the bandwidth efficiency of I2V communication, this work considers in a software-defined vehicular networks (SDVN), aiming at exploiting synergistic effects of network coding and vehicular caching. First, we consider a data service scenario in which roadside unites (RSUs) are connected with the controller, which exercises scheduling decisions based on service requests received from vehicles. On this basis, we formulate a cooperative coding and caching scheduling problem with the objective of maximizing the bandwidth efficiency of I2V communication. Then, we propose a binary particle swarm optimization (BPSO)-based coding scheduling (BPSO_CS) algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation. The results conclusively demonstrate the superiority of the proposed solution.

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References

  1. Dai P, Liu K, Zhuge Q, Sha EHM, Lee VCS, Son SH (2016) Quality-of-experience-oriented autonomous intersection control in vehicular networks. IEEE Trans Intell Transp Syst 17(7):1956–1967

    Article  Google Scholar 

  2. Fu Y, Li C, Luan TH, Zhang Y, Yu FR (2020) Graded warning for rear-end collision: an artificial intelligence-aided algorithm. IEEE Trans Intell Transp Syst 21(2):565–579

    Article  Google Scholar 

  3. Liu K, Lim HB, Frazzoli E, Ji H, Lee VC (2013) Improving positioning accuracy using gps pseudorange measurements for cooperative vehicular localization. IEEE Trans Veh Technol 63(6):2544–2556

    Article  Google Scholar 

  4. Morgan YL (2010) Notes on DSRC & WAVE standards suite: its architecture, design, and characteristics. IEEE Commun Surv Tutor 12(4):504–518

    Article  Google Scholar 

  5. Shojafar M, Cordeschi N, Baccarelli E (2016) Energy-efficient adaptive resource management for real-time vehicular cloud services. IEEE Trans Cloud Comput 7(1):196–209

    Article  Google Scholar 

  6. Dai P, Liu K, Feng L, Zhuge Q, Lee VC, Son SH (2016) Adaptive scheduling for real-time and temporal information services in vehicular networks. Transp Res C Emerg Technol 71:313–332

    Article  Google Scholar 

  7. Li J, Luo G, Cheng N, Yuan Q, Wu Z, Gao S, Liu Z (2018) An end-to-end load balancer based on deep learning for vehicular network traffic control. IEEE Internet Things J 6(1):953–966

    Article  Google Scholar 

  8. Yu B, Bao S, Feng F, Sayer J (2019) Examination and prediction of drivers’ reaction when provided with v2i communication-based intersection maneuver strategies. Transp Res C Emerg Technol 106:17–28

    Article  Google Scholar 

  9. Atallah RF, Assi CM, Yu JY (2016) A reinforcement learning technique for optimizing downlink scheduling in an energy-limited vehicular network. IEEE Trans Veh Technol 66(6):4592–4601

    Article  Google Scholar 

  10. Liu K, Ng JKY, Wang J, Lee VC, Wu W, Son SH (2015) Network-coding-assisted data dissemination via cooperative vehicle-to-vehicle/-infrastructure communications. IEEE Trans Intell Transp Syst 17(6):1509–1520

    Article  Google Scholar 

  11. Wu C, Ohzahata S, Ji Y, Kato T (2016) How to utilize interflow network coding in vanets: a backbone-based approach. IEEE Trans Intell Transp Syst 17(8):2223–2237

    Article  Google Scholar 

  12. Zhou Y, Chen J, Ye G, Wu D, Wang JH, Chen M (2019) Collaboratively replicating encoded content on rsus to enhance video services for vehicles. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2019.2960022

    Article  Google Scholar 

  13. Bhatia J, Kakadia P, Bhavsar M, Tanwar S (2019) SDN-enabled network coding based secure data dissemination in vanet environment. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2019.2956964

    Article  Google Scholar 

  14. Xiao K, Liu K, Xu X, Zhou Y, Feng L (2019) Efficient fog-assisted heterogeneous data services in software defined vanets. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01507-8

    Article  Google Scholar 

  15. Liu K, Ng JK, Lee V, Son SH, Stojmenovic I (2016) Cooperative data scheduling in hybrid vehicular ad hoc networks: vanet as a software defined network. IEEE/ACM Trans Netw (TON) 24(3):1759–1773

    Article  Google Scholar 

  16. Tang Y, Cheng N, Wu W, Wang M, Dai Y, Shen X (2019) Delay-minimization routing for heterogeneous vanets with machine learning based mobility prediction. IEEE Trans Veh Technol 68(4):3967–3979

    Article  Google Scholar 

  17. Liu K, Xu X, Chen M, Liu B, Wu L, Lee VC (2019) A hierarchical architecture for the future internet of vehicles. IEEE Commun Mag 57(7):41–47

    Article  Google Scholar 

  18. Misra S, Bera S (2019) Soft-van: Mobility-aware task offloading in software-defined vehicular network. IEEE Trans Veh Technol 69(2):2071–2078

    Article  Google Scholar 

  19. Sudheera KLK, Ma M, Chong PHJ (2019) Link stability based optimized routing framework for software defined vehicular networks. IEEE Trans Veh Technol 68(3):2934–2945

    Article  Google Scholar 

  20. Yao L, Chen A, Deng J, Wang J, Wu G (2017) A cooperative caching scheme based on mobility prediction in vehicular content centric networks. IEEE Trans Veh Technol 67(6):5435–5444

    Article  Google Scholar 

  21. Balico LN, Loureiro AA, Nakamura EF, Barreto RS, Pazzi RW, Oliveira HA (2018) Localization prediction in vehicular ad hoc networks. IEEE Commun Surv Tutor 20(4):2784–2803

    Article  Google Scholar 

  22. Harvey NJ, Karger DR, Yekhanin S (2006) The complexity of matrix completion. In: Proceedings of the seventeenth annual ACM-SIAM symposium on discrete algorithm. Society for Industrial and Applied Mathematics, pp 1103–1111

  23. Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948

  24. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics. Computational cybernetics and simulation, IEEE, vol 5, pp 4104–4108

  25. Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of sumo-simulation of urban mobility. Int J Adv Syst Meas 5(3&4):128–138

    Google Scholar 

  26. Bai F, Sadagopan N, Helmy A (2003) The important framework for analyzing the impact of mobility on performance of routing protocols for adhoc networks. Ad Hoc Netw 1(4):383–403

    Article  Google Scholar 

  27. Wong JW (1988) Broadcast delivery. Proc IEEE 76(12):1566–1577

    Article  Google Scholar 

  28. Zhan C, Lee VC, Wang J, Xu Y (2011) Coding-based data broadcast scheduling in on-demand broadcast. IEEE Trans Wirel Commun 10(11):3774–3783

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61872049, 61876025, and 61803054, in part by the Fundamental Research Funds for the Central Universities (2019CDQYZDH030).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Ke Xiao, Kai Liu, Xincao Xu & Liang Feng

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing, China

    Kai Liu

  3. School of Automation, Chongqing University, Chongqing, China

    Zhou Wu

  4. Wuhan Cabit Information Company, Wuhan, Hubei Province, China

    Qiangwei Zhao

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  1. Ke Xiao
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  2. Kai Liu
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  3. Xincao Xu
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  4. Liang Feng
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  5. Zhou Wu
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  6. Qiangwei Zhao
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Correspondence to Kai Liu.

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Xiao, K., Liu, K., Xu, X. et al. Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks. Neural Comput & Applic 33, 1467–1478 (2021). https://doi.org/10.1007/s00521-020-04978-5

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  • DOI: https://doi.org/10.1007/s00521-020-04978-5

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