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Joint Task Offloading Based on Distributed Deep Reinforcement Learning-Based Genetic Optimization Algorithm for Internet of Vehicles

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Abstract

The growing number of individual vehicles and intelligent transportation systems have accelerated the development of Internet of Vehicles (IoV) technologies. The Internet of Vehicles (IoV) refers to a highly interactive network containing data regarding places, speeds, routes, and other aspects of vehicles. Task offloading was implemented to solve the issue that the current task scheduling models and tactics are primarily simplistic and do not consider the acceptable distribution of tasks, which results in a poor unloading completion rate. This work evaluates the Joint Task Offloading problem by Distributed Deep Reinforcement Learning (DDRL)-Based Genetic Optimization Algorithm (GOA). A system’s utility optimisation model is initially accomplished objectively using divisions between interaction and computation models. DDRL-GOA resolves the issue to produce the best task offloading method. The research increased job completion rates by modifying the complexity design and universal best-case scenario assurances using DDRL-GOA. Finally, empirical research is performed to validate the proposed technique in scenario development. We also construct joint task offloading, load distribution, and resource allocation to lower system costs as integer concerns. In addition to having a high convergence efficiency, the experimental results show that the proposed approach has a substantially lower system cost when compared to current methods.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Wang, K., Wang, X.,, Liu, X.: Sustainable Internet of vehicles system: a task offloading strategy based on improved genetic algorithm. Sustainability 15(9), 7506 (2023)

  2. Cao, K., Wang, B., Ding, H., Lv, L., Tian, J., Hu, H.,... Gong, F.: Achieving reliable and secure communications in wireless-powered NOMA systems. IEEE Trans. Veh. Technol. 70(2), 1978–1983 (2021)

  3. Elgendy, I.A.: Souham Meshoul, and Mohamed Hammad. Joint task offloading, resource allocation, and load-balancing optimization in multi-UAV-Aided MEC systems. Appl. Sci. 13(4), 2625 (2023)

  4. Zou, W., Sun, Y., Zhou, Y., Lu Q., Nie, Y., Sun, T., Peng, L.: Limited sensing and deep data mining: a new exploration of developing city-wide parking guidance systems. IEEE Intell. Transp. Syst. Mag. 14(1), 198–215 (2022)

  5. Kai, L.I., Hou, Y., Hans, S.P., Andreson, S.P.: Research progress of green infrastructure oriented by landscape planning: from the perspective of “pattern-process-services-sustainability” research paradigm. J. Nat. Resour. 36, 435 (2021)

    Google Scholar 

  6. Cao, K., Ding, H., Li, W., Lv, L., Gao, M., Gong, F.,... Wang, B.: On the ergodic secrecy capacity of intelligent reflecting surface aided wireless powered communication systems. IEEE Wireless Commun. Lett. 1 (2022)

  7. Machadorodrigues, T., Schneider, M.: Five strategies for political participation of the media democratization movement in the New Republic. Intercom Rev. Bras. Ciências Comun 43, 189–204 (2020)

    Google Scholar 

  8. Han, Y., Wang, B., Guan, T., Tian, D., Yang, G., Wei, W.,... Chuah, J. H.: Research on road environmental sense method of intelligent vehicle based on tracking check. IEEE Trans. Intell. Transp. Syst. 1–15 (2022)

  9. Xu, J., Park, S.H., Zhang, X., Hu, J.: The improvement of road driving safety guided by visual inattentional blindness. IEEE Trans. Intell. Transp. Syst. 23(6), 4972–4981 (2022)

  10. Haaker, T., Ly, P.T.M., Nguyen-Thanh, N., Nguyen, H.T.H.: Business model innovation through the application of the Internet-of- Things: a comparative analysis. J. Bus. Res. 126, 126–136 (2021)

    Article  Google Scholar 

  11. Xu, J., Guo, K., Sun, P. Z. H.: Driving performance under violations of traffic rules: novice vs. experienced drivers. IEEE Trans. Intell. Veh. (2022)

  12. Spandonidis, C., Giannopoulos, F., Sedikos, E., Reppas, D., Theodoropoulos, P.: Development of a MEMS-based IoV system for augmenting road traffic surey. IEEE Trans. Instrum. Meas. 71, 9510908 (2022)

    Article  Google Scholar 

  13. Li, H., Huang, Q., Huang, J., Susilo, W.: Public-key authenticated encryption with keyword search supporting constant trapdoor generation and fast search. IEEE Trans. Inf. Forensics Secur. 18, 396–410 (2023)

  14. Sun, G., Sheng, L., Luo, L., Yu, H.: Game theoretic approach for multipriority data transmission in 5G vehicular networks. IEEE Trans. Intell. Transp. Syst. 23(12), 24672–24685 (2022)

  15. Wang, J., Hu, J., Min, G., Zomaya, A.Y., Georgalas, N.: Fast adaptive task offloading in edge computing based on meta reinforcement learning. IEEE Trans. Parallel Distrib. Syst. 32, 242–253 (2020)

    Article  Google Scholar 

  16. Ji, B., Zhang, X., Mumtaz, S., Han, C., Li, C., Wen, H., Wang, D.: Survey on the internet of vehicles: network architectures and applications. IEEE Commun. Stand. Mag 4, 34–41 (2020)

    Article  Google Scholar 

  17. Sun, G., Song, L., Yu, H., Chang, V., Du, X.,... Guizani, M.: V2V routing in a VANET based on the autoregressive integrated moving average model. IEEE Trans. Veh. Technol. 68(1), 908–922 (2019)

  18. Sun, L., Liang, J., Zhang, C., Wu, D., Zhang, Y.: Meta-transfer metric learning for time series classification in 6G-supported intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. (2023)

  19. Zhang, R., Cheng, P., Chen, Z., Liu, S., Li, Y., Vucetic, B.: Online learning enabled task offloading for vehicular edge computing. IEEE Wirel. Commun. Lett 9, 928–932 (2020)

    ADS  Google Scholar 

  20. Zhang, X., Wang, Z., Lu, Z.: Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm. Appl. Energy 306, 118018 (2022)

  21. Guo, Y., Zhang, C., Wang, C., Jia, X.: Towards public verifiable and forward-privacy encrypted search by using blockchain. IEEE Trans. Dependable Secure Comput. 20(3), 2111–2126 (2023)

  22. Chen, J., Chen, S., Luo, S., Wang, Q., Cao, B., Li, X.: An intelligent task offloading algorithm (iTOA) for UAV edge computing network. Digit. Commun. Netw 6, 433–443 (2020)

    Article  Google Scholar 

  23. Yao, Y., Zhao, J., Li, Z., Cheng, X., Wu, L. Jamming and eavesdropping defense scheme based on deep reinforcement learning in autonomous vehicle networks. IEEE Trans. Inf. Forensics Secur. 18, 1211–1224 (2023)

  24. Yu, Z., Gong, Y., Gong, S., Guo, Y.: Joint task offloading and resource allocation in UAV-enabled mobile edge computing. IEEE Internet Things J. 7, 3147–3159 (2020)

    Article  Google Scholar 

  25. Yao, Y., Shu, F., Li, Z., Cheng, X., Wu, L.: Secure transmission scheme based on joint radar and communication in mobile vehicular networks. IEEE Trans. Intell. Transp. Syst. (2023)

  26. Zhao, J., Song, D., Zhu, B., Sun, Z., Han, J.,... Sun, Y.: A human-like trajectory planning method on a curve based on the driver preview mechanism. IEEE Trans. Intell. Transp. Syst. 24(11), 11682–11698 (2023)

  27. Zhu, B., Sun, Y., Zhao, J., Han, J., Zhang, P.,... Fan, T.: A critical scenario search method for intelligent vehicle testing based on the social cognitive optimization algorithm. IEEE Trans. Intell. Transp. Syst. 24(8), 7974–7986 (2023)

  28. Cheng, B., Zhu, D., Zhao, S., Chen, J.: Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Trans. Netw. Service Manag. 13(2), 349–361 (2016)

  29. Xiao, Z., Shu, J., Jiang, H., Lui, J. C. S., Min, G., Liu, J.,... Dustdar, S.: Multi-objective parallel task offloading and content caching in D2D-aided MEC networks. IEEE Trans. Mob. Comput. (2022)

  30. Yang, L., Yao, H., Wang, J., Jiang, C., Benslimane, A., Liu, Y.: Multi-UAV-enabled load-balance mobile-edge computing for IoT networks. IEEE Internet Things J. 7, 6898–6908 (2020)

    Article  Google Scholar 

  31. Xiao, Z., Shu, J., Jiang, H., Min, G., Chen, H.,... Han, Z.: perception task offloading with collaborative computation for autonomous driving. IEEE J. Sel. Areas Commun. 41(2), 457–473 (2023)

  32. Dai, X., Xiao, Z., Jiang, H., Lui, J.C.S.: UAV-assisted task offloading in vehicular edge computing networks. IEEE Trans. Mobile Comput. (2023)

  33. Niknejad, N., Bidese-Puhl, R., Bao, Y., Payn, K.G., Zheng, J.: Phenotyping of architecture traits of loblolly pine trees using stereo machine vision and deep learning: Stem diameter, branch angle, and branch diameter. Comput. Electron. Agric. 211, 107999 (2023)

    Article  Google Scholar 

  34. Yang, H., Chen, C., Ni, J., Karekal, S.: A hyperspectral evaluation approach for quantifying salt-induced weathering of sandstone. Sci. Total Environ. 885, 163886 (2023)

    Article  ADS  CAS  PubMed  Google Scholar 

  35. Alahdadi, A., Safaei, A.A., Ebadi, M.J.: A truthful and budget-balanced double auction model for resource allocation in cloud computing. Soft Comput. 1–22 (2023)

  36. Li, P., Hu, J., Qiu, L., Zhao, Y., Ghosh, B.K.: A distributed economic dispatch strategy for power-water networks. IEEE Trans. Control Netw. Syst. 9(1), 356–366 (2022)

    Article  MathSciNet  Google Scholar 

  37. Duan, Y., Zhao, Y., Hu, J.: An initialization-free distributed algorithm for dynamic economic dispatch problems in microgrid: modeling, optimization and analysis. Sustain. Energy Grids Netw. 34 (2023)

  38. Ding, C., Li, C., Xiong, Z., Li, Z., Liang, Q.: Intelligent identification of moving trajectory of autonomous vehicle based on friction nano-generator. IEEE Trans. Intell. Transp. Syst. (2023)

  39. Min, H., Li, Y., Wu, X., Wang, W., Chen, L.,... Zhao, X.: A measurement scheduling method for multi-vehicle cooperative localization considering state correlation. Veh. Commun. (2023)

  40. Zhao, X., Fang, Y., Min, H., Wu, X., Wang, W.,... Teixeira, R.: Potential sources of sensor data anomalies for autonomous vehicles: an overview from road vehicle safety perspective. Expert Syst. Appl. 236 (2024)

  41. Mou, J., Gao, K., Duan, P., Li, J., Garg, A.,... Sharma, R.: A machine learning approach for energy-efficient intelligent transportation scheduling problem in a real-world dynamic circumstances. IEEE Trans. Intell. Transp. Syst. (2022)

  42. Cao, B., Li, Z., Liu, X., Lv, Z., He, H.: Mobility-aware multiobjective task offloading for vehicular edge computing in digital twin environment. IEEE J. Sel. Areas Commun. 41(10), 3046–3055 (2023)

    Article  Google Scholar 

  43. Zhang, L., Sun, C., Cai, G., Koh, L. H.: Charging and discharging optimization strategy for electric vehicles considering elasticity demand response. eTransportation 18 (2023)

  44. Cao, B., Zhao, J., Lv, Z., Gu, Y., Yang, P., ... Halgamuge, S. K.: Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Trans. Fuzzy Syst. 28(5), 939-952 (2020)

  45. Lu, J., Osorio, C.: On the analytical probabilistic modeling of flow transmission across nodes in transportation networks. Transp. Res. Rec. 2676(12), 209–225 (2022)

    Article  Google Scholar 

  46. Lu, J., Osorio, C.: A probabilistic traffic-theoretic network loading model suitable for large-scale network analysis. Transp. Sci. 52(6), 1509–1530 (2018)

    Article  Google Scholar 

  47. Luo, R., Peng, Z., Hu, J., Ghosh, B.K.: Adaptive optimal control of affine nonlinear systems via identifier–critic neural network approximation with relaxed PE conditions. Neural Netw. 167, 588–600 (2023)

    Article  PubMed  Google Scholar 

  48. Ma, B., Liu, Z., Dang, Q., Zhao, W., Wang, J., Cheng, Y.,... Yuan, Z.: Deep reinforcement learning of UAV tracking control under wind disturbances environments. IEEE Trans. Instrum. Meas. 72 (2023)

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Funding

This research received no specific grant from any funding agency.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230031, China

    Hulin Jin & Chunyang Fan

  2. Department of Computer Engineering, Sejong University, 05006, Seoul, South Korea

    Yong-Guk Kim

  3. Foothill Preparatory School, Temple City, CA, 91780, USA

    Zhiran Jin

  4. Tencent Yantai Emerging Engineering Research Institute, Yantai, 264006, China

    Yonglong Xu

Authors
  1. Hulin Jin
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  2. Yong-Guk Kim
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  3. Zhiran Jin
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  4. Chunyang Fan
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  5. Yonglong Xu
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Contributions

H.J: Conceptualization, Methodology, Formal analysis, Supervision, Writing - original draft, Writing - review & editing. Y.G: Writing - original draft, Writing - review & editing. Z.J: Investigation, Data Curation, Resources, Writing - review & editing. C.F Investigation, Data Curation, Validation, Writing - review & editing. Y.X: Investigation, Validation, Resources, Writing - review & editing.

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Correspondence to Hulin Jin.

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Jin, H., Kim, YG., Jin, Z. et al. Joint Task Offloading Based on Distributed Deep Reinforcement Learning-Based Genetic Optimization Algorithm for Internet of Vehicles. J Grid Computing 22, 34 (2024). https://doi.org/10.1007/s10723-024-09741-x

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