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Efficient approaches for task offloading in point-of-interest based vehicular fog computing

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Abstract

Vehicular fog computing (VFC), as the extension of the mobile cloud, has been widely investigated. However, in most existing VFC works, the scenario with point-of-interests (PoIs) is rarely considered, which can lead to underutilization of vehicle computing resources and overloading of the computing load for roadside units (RSUs). Therefore, this paper focuses on improving the quality of services for PoI-based VFC by utilizing the computation capabilities of vehicles to assist the RSU. An NP-hard problem is formulated to maximize the number of tasks completed within the duration of delay constraints. To solve the problem, a delay-aware task offloading algorithm, named Delay-Aware Task Offloading (DTO), is proposed. DTO preferentially operates on tasks with tighter delay constraints. To minimize the completion delay for each offloaded task, DTO selects the set of service vehicles to perform tasks. Moreover, a vehicle-resource-aware task offloading algorithm, named Vehicle-Resources-Aware Task Offloading (VRTO), is proposed to maximize the number of tasks completed within the duration of delay constraints by efficiently utilizing the available resource of the vehicle. Specifically, VRTO allocates the data to each service vehicle according to the service configuration of vehicles and the delay constraints of tasks. Finally, extensive experiments are carried out to evaluate the effectiveness of the proposed algorithms. The performance is successfully improved by more than 30% compared with the baselines in terms of the number of completed tasks.

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References

  1. Liu Y, Wang S, Zhao Q, Du S, Zhou A, Ma X, Yang F (2020) Dependency-aware task scheduling in vehicular edge computing. IEEE Internet Things J 7(6):4961–4971

    Article  Google Scholar 

  2. Liwang M, Dai S, Gao Z, Tang Y, Dai H (2018) A truthful reverse-auction mechanism for computation offloading in cloud-enabled vehicular network. IEEE Internet Things J 6(3):4214–4227

    Article  Google Scholar 

  3. Huang J, Qian Y, Hu RQ (2019) A vehicle-assisted data offloading in mobile edge computing enabled vehicular networks. In: IEEE Global Communications Conference, pp 1–6

  4. Zhang X, Zhang J, Liu Z, Cui Q, Tao X, Wang S (2020) Mdp-based task offloading for vehicular edge computing under certain and uncertain transition probabilities. IEEE Trans Veh Technol 69(3):3296–3309

    Article  Google Scholar 

  5. Luo Q, Li C, Luan TH, Shi W (2020) Collaborative data scheduling for vehicular edge computing via deep reinforcement learning. IEEE Internet Things J 7(10):9637–9650

    Article  Google Scholar 

  6. Ke H, Wang J, Deng L, Ge Y, Wang H (2020) Deep reinforcement learning-based adaptive computation offloading for mec in heterogeneous vehicular networks. IEEE Trans Veh Technol 69(7):7916–7929

    Article  Google Scholar 

  7. Chen L, Wu J, Zhang J (2021) Long-term optimization for mec-enabled hetnets with device-edge-cloud collaboration. Comput Commun 166:66–80

    Article  Google Scholar 

  8. Zhao J, Li Q, Gong Y, Zhang K (2019) Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Trans Veh Technol 68(8):7944–7956

    Article  Google Scholar 

  9. Hou X, Li Y, Chen M, Wu D, Jin D, Chen S (2016) Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans Veh Technol 65(6):3860–3873

    Article  Google Scholar 

  10. Zhou Z, Liao H, Wang X, Mumtaz S, Rodriguez J (2020) When vehicular fog computing meets autonomous driving: computational resource management and task offloading. IEEE Netw 34(6):70–76

    Article  Google Scholar 

  11. Vemireddy S, Rout RR (2021) Fuzzy reinforcement learning for energy efficient task offloading in vehicular fog computing. Comput Netw 199:108463

    Article  Google Scholar 

  12. Tang C, Wei X, Zhu C, Wang Y, Jia W (2020) Mobile vehicles as fog nodes for latency optimization in smart cities. IEEE Trans Veh Technol 69(9):9364–9375

    Article  Google Scholar 

  13. Jemaa IB, Shagdar O, Muhlethaler P, de La Fortelle A (2015) Extended mobility management and geocast routing for internet-to-vanet multicasting. ITS world congress, pp 1–13

  14. Zheng Y, Liu F, Hsieh H-P (2013) U-air: When urban air quality inference meets big data. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1436–1444

  15. Safavi S, Jalali M, Houshmand M (2022) Toward point-of-interest recommendation systems: a critical review on deep-learning approaches. Electronics 11(13):1998

    Article  Google Scholar 

  16. Huang C-M, Chen Y-F, Xu S, Zhou H (2018) The vehicular social network (vsn)-based sharing of downloaded geo data using the credit-based clustering scheme. IEEE Access 6:58254–58271

    Article  Google Scholar 

  17. de Sousa DDA, de Sousa JI, Vieira LF (2020) SEGMETRIK: protocol and metrics for advertisement performance tracking in VANETs. Veh Commun 22:100212

    Google Scholar 

  18. Krause CM, Zhang L (2019) Short-term travel behavior prediction with gps, land use, and point of interest data. Transp Res Part B Methodol 123:349–361

    Article  Google Scholar 

  19. Rajadurai R, Jayalakshmi N (2013) Vehicular network: properties, structure, challenges, attacks, solution for improving scalability and security. Int J Adv Res 1(3):41–50

    Google Scholar 

  20. Zhou Y, Liu K, Xu X, Liu C, Feng L, Chen C (2020) Multi-period distributed delay-sensitive tasks offloading in a two-layer vehicular fog computing architecture. In: International Conference on Neural Computing for Advanced Applications, pp 461–474

  21. Zhou Y, Liu K, Xu X, Guo S, Wu Z, Lee V, Son S (2020) Distributed scheduling for time-critical tasks in a two-layer vehicular fog computing architecture. In: Annual Consumer Communications and Networking Conference, pp 1–7

  22. Shi J, Du J, Wang J, Wang J, Yuan J (2020) Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning. IEEE Trans Veh Technol 69(12):16067–16081

    Article  Google Scholar 

  23. Zhou Z, Liu P, Feng J, Zhang Y, Mumtaz S, Rodriguez J (2019) Computation resource allocation and task assignment optimization in vehicular fog computing: a contract-matching approach. IEEE Trans Veh Technol 68(4):3113–3125

    Article  Google Scholar 

  24. Zhu C, Tao J, Pastor G, Xiao Y, Ji Y, Zhou Q, Li Y, Ylä-Jääski A (2018) Folo: latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J 6(3):4150–4161

    Article  Google Scholar 

  25. Lee S, Lee S, Choi Y, Ben-Othman J, Kim H (2022) Geoss: geographic segmentation security barriers for virtual emotion detection with discriminative priorities in intelligent cooperative vehicular system. In: IEEE transactions on vehicular technology, pp 6491–6502

  26. Kim H, Ben-Othman J, Mokdad L (2023) Intelligent terrestrial and non-terrestrial vehicular networks with green ai and red ai perspectives. Sensors 23(2):1–13

    Article  Google Scholar 

  27. Sarkar I, Kumar S (2022) Delay-aware intelligent task offloading strategy in vehicular fog computing. In: International Conference on Connected Systems and Intelligence, pp 1–6

  28. Peng X, Han Z, Xie W, Yu C, Zhu P, Xiao J, Yang J (2022) Deep reinforcement learning for shared offloading strategy in vehicle edge computing. IEEE Syst J 2089–2100

  29. Alam MZ, Jamalipour A (2022) Multi-agent DRL-based Hungarian algorithm for task offloading in multi-access edge computing internet of vehicle. IEEE Trans Wirel Commun 21(9):7641–7652

    Article  Google Scholar 

  30. Gao Y, Wang Z, Li Z, Li Z (2022) Task migration based on deep reinforcement learning in mobile crowdsourcing. In: IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, pp 410–417

  31. Wang H, Lv T, Lin Z, Zeng J (2022) Energy-delay minimization of task migration based on game theory in mec-assisted vehicular networks. IEEE Trans Veh Technol 71(8):8175–8188

    Article  Google Scholar 

  32. Chen J, Kang J, Xu M, Xiong Z, Niyato D, Tong Y (2023) Multiple-agent deep reinforcement learning for avatar migration in vehicular metaverses. In: Companion Proceedings of the ACM Web Conference, pp 1258–1265

  33. Yadav VK, Verma S, Venkatesan S (2020) Efficient and secure location-based services scheme in vanet. IEEE Trans Veh Technol 69(11):13567–13578

    Article  Google Scholar 

  34. Zhang P, Hu C, Chen D, Li H, Li Q (2018) Shiftroute: achieving location privacy for map services on smartphones. IEEE Trans Veh Technol 67(5):4527–4538

    Article  Google Scholar 

  35. Li Y, Chen T, Zhang P-F, Huang Z, Yin H (2022) Self-supervised graph-based point-of-interest recommendation. arXiv:2210.12506

  36. Zhi L, Rui S, Yuxuan Y, Xiaohuan L (2022) Recommending point-of-interests with real-time event detection. Data Anal Knowl Discov 6(10):114–127

    Google Scholar 

  37. Ogah CA (2020) Security and privacy in VANET-based intelligent transport systems. University of Surrey, pp 1–24

  38. Hou Y, Wei Z, Zhang R, Cheng X, Yang L (2023) Hierarchical task offloading for vehicular fog computing based on multi-agent deep reinforcement learning. IEEE Trans Wirel Commun 1–12

  39. Raza S, Liu W, Ahmed M, Anwar MR, Mirza MA, Sun Q, Wang S (2020) An efficient task offloading scheme in vehicular edge computing. J Cloud Comput 9:1–14

    Article  Google Scholar 

  40. Ghosh S, Saha Misra I, Chakraborty T (2023) Optimal rsu deployment using complex network analysis for traffic prediction in vanet. Peer-to-Peer Netw Appl 16(2):1135–1154

    Article  Google Scholar 

  41. Oza P, Hudson N, Chantem T, Khamfroush H (2023) Deadline-aware task offloading for vehicular edge computing networks using traffic lights data. ACM Trans Embed Comput Syst 1–24

  42. Cai M, Yan R, Doryab A (2022) Daily trajectory prediction using temporal frequent pattern tree. In: International congress on information and communication technology, pp 333–343

  43. Zhao G, Xu H, Zhao Y, Qiao C, Huang L (2020) Offloading dependent tasks in mobile edge computing with service caching. In: IEEE Conference on Computer Communications, pp 1997–2006

  44. Zhao J, Kong M, Li Q, Sun X (2019) Contract-based computing resource management via deep reinforcement learning in vehicular fog computing. IEEE Access 8:3319–3329

    Article  Google Scholar 

  45. Sun Y, Wu J, Chen L, Liu T, Yao M, Sun W (2019) Latency optimization for mobile edge computing with dynamic energy harvesting. In: IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, pp 79–83

  46. Cong R, Zhao Z, Min G, Feng C, Jiang Y (2021) Edgego: a mobile resource-sharing framework for 6g edge computing in massive iot systems. IEEE Internet Things J 9(16):14521–14529

    Article  Google Scholar 

  47. Tang F, Liu C, Li K, Tang Z, Li K (2021) Task migration optimization for guaranteeing delay deadline with mobility consideration in mobile edge computing. J Syst Architect 112:101849

    Article  Google Scholar 

  48. Cole R (1988) Parallel merge sort. SIAM J Comput 17(4):770–785

    Article  MathSciNet  Google Scholar 

  49. Hou X, Ren Z, Wang J, Cheng W, Ren Y, Chen K-C, Zhang H (2020) Reliable computation offloading for edge-computing-enabled software-defined iov. IEEE Internet Things J 7(8):7097–7111

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by National Natural Science Foundation of China under Grant Nos. 62106052, 62072118, 62202108 and U1911401, National Key Research and Development Project under Grant 2018YFB1802400, Key-Area Research and Development Program of Guangdong Province under Grant 2020B0101130001, Huangpu International Sci &Tech Cooperation Fundation of Guangzhou, China under Grant 2021GH12.

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Authors and Affiliations

  1. Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Yifei Sun, Jigang Wu & Long Chen

  2. School of Integrated Circuits, Guangdong University of Technology, Guangzhou, China

    Yalan Wu

  3. School of Automation, Guangdong University of Technology, Guangzhou, China

    Weijun Sun

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  1. Yifei Sun
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  4. Long Chen
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Contributions

YS, LC, and JW conceived of the presented idea. JW and YW encouraged YS to investigate the algorithms for the proposed problem. YS carried out the experiment and wrote the main manuscript text. WS prepared figures 1–2. All authors discussed the results and revised the manuscript.

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Correspondence to Jigang Wu.

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Sun, Y., Wu, J., Wu, Y. et al. Efficient approaches for task offloading in point-of-interest based vehicular fog computing. J Supercomput 80, 6285–6310 (2024). https://doi.org/10.1007/s11227-023-05698-y

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