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A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks

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Abstract

The rapid development of Industrial Internet of Things (IIoT) has brought unprecedented opportunities to the industry informatization. However, facing with billions access of IIoT devices, the traditional IIoT architecture based on cloud computing is no longer suitable in terms of flexibility, efficiency and elasticity. Multi-access Edge Computing (MEC) has been seen as a enabling technology to process massive time-sensitive tasks. Meanwhile, the multi-task collaborative offloading is an urgent problem for IIoT-MEC networks. In this paper, a Smart Collaborative Framework (SCF) scheme is designed to achieve dynamic service prediction and make multi-task offloading decisions. First, a theoretical model, including a Hierarchical Spatial-Temporal Monitoring (HSTM) module and a Fine-grained Resource Scheduling (FRS) module, is established. Hybrid deep learning algorithms are applied to the monitoring module from spatial-temporal dimensions. Besides, both mixed game and improved queuing theories are adopted to enhance offloading efficiency in the FRS module. Second, a specific framework and an implementation process are designed for illustrating scheme details. Third, a prototype environment are created with optimal parameter settings. The validation results demonstrated that the SCF scheme can achieve better task awareness, abnormality inference and task offloading compared to other candidate algorithms. The proposed model has enhanced 7.8% and 8.5% in accuracy and detection rate, and optimized the offloading efficiency.

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References

  1. Cirillo F, Gòmez D, Diez L, Elicegui Maestro I, Gilbert TBJ, Akhavan R (2020) Smart City IoT services creation through large-scale collaboration. IEEE Internet Things J 7(6):5267-5275

  2. Han Y, Zhang C, Wang L, Zhang Y (2020) Industrial IoT for intelligent steelmaking with converter mouth flame spectrum information processed by deep learning. IEEE Trans Industr Inf 16(4):2640–2650

    Article  Google Scholar 

  3. Gheisarnejad M, Khooban MH (2020) IoT-Based DC/DC deep learning power converter control: real-time implementation. IEEE Trans Power Electron 35(12):13621–13630

    Article  Google Scholar 

  4. Ramson SRJ, Vishnu S, Kirubaraj AA, Anagnostopoulos T, Abu-Mahfouz AM (2022) A LoRaWAN IoT-enabled trash bin level monitoring system. IEEE Trans Industr Inf 18(2):786–795

    Article  Google Scholar 

  5. Ericsson (2018) About the IIC paper: untangling industrial IoT networking. [Online]. Available: https://www.ericsson.com/en/blog/2018/10/untangling-industrial-iot-networking. Accessed 21 Jun 2022

  6. Cisco (2019) Visual networking index: forecast and trends, 2017–2022. White Paper. [Online]. Available: http://www.cisco.com/go/vni. Accessed 23 Jul 2022

  7. Raveendran N, Zhang H, Song L, Wang L-C, Hong CS, Han Z (2022) Pricing and resource allocation optimization for IoT fog computing and NFV: an EPEC and matching based perspective. IEEE Trans Mob Comput 21(4):1349–1361

    Article  Google Scholar 

  8. Li M, Chen C, Wu H, Guan X, Shen X (2021) Age-of-Information aware scheduling for edge-assisted industrial wireless networks. Transactions on Industrial Informatics 17(8):5562–5571

    Article  Google Scholar 

  9. Zomaya AY, Kumar N, Rodrigues JJPC, Aujla GS (2020) Guest editorial: special section on intelligent informatics for edge of things in smart industrial ecosystem. IEEE Trans Industr Inf 16(3):1933–1937

    Article  Google Scholar 

  10. Zhang C, Zhao M, Zhu L, Zhang W, Wu T, Ni J (2022) FRUIT: a blockchain-based efficient and privacy-preserving quality-aware incentive scheme. IEEE J Sel Areas Commun. https://doi.org/10.1109/JSAC.2022.3213341

    Article  Google Scholar 

  11. Xiao F, Sha L, Yuan Z, Wang R (2020) VulHunter: a discovery for unknown bugs based on analysis for known patches in industry internet of things. IEEE Trans Emerg Top Comput 8(2):267–279

    Article  Google Scholar 

  12. Boudagdigue C, Benslimane A, Kobbane A, Liu J (2020) Trust management in industrial internet of things. IEEE Trans Inf Forensics Secur 15:3667–3682

    Article  Google Scholar 

  13. Karaagac A, De Poorter E, Hoebeke J (2020) In-band network telemetry in industrial wireless sensor networks. IEEE Trans Netw Serv Manag 17(1):517–531

    Article  Google Scholar 

  14. Moore AW, Zuev D (2005) Internet traffic classification using Bayesian analysis techniques. Proc ACM Sigmetrics pp. 50–60

  15. Yang D, Gong K, Ren J, Zhang W, Wu W, Zhang H (2022) TC-Flow: Chain flow scheduling for advanced industrial applications in time-sensitive networks. IEEE Netw 36(2):16–24

    Article  Google Scholar 

  16. Zhang W et al (2021) Optimizing federated learning in distributed industrial IoT: a multi-agent approach. IEEE J Sel Areas Commun 39(12):3688–3703

    Article  Google Scholar 

  17. Assefa BG, Özkasap Ö (2020) RESDN: A novel metric and method for energy efficient routing in software defined networks. IEEE Trans Netw Serv Manag 17(2):736–749

    Article  Google Scholar 

  18. Tang F, Zhang H, Yang LT (2019) Multipath cooperative routing with efficient acknowledgement for LEO satellite networks. IEEE Trans Mob Comput 18(1):179–192

    Article  Google Scholar 

  19. Al-Abiad MS, Douik A, Sorour S, Hossain MJ (2022) Throughput maximization in cloud-radio access networks using cross-layer network coding. IEEE Trans Mob Comput 21(2):696–711

    Article  Google Scholar 

  20. Farhadi F, Golestani SJ, Teneketzis D (2019) A surrogate optimization-based mechanism for resource allocation and routing in networks with strategic agents. IEEE Trans Autom Control 64(2):464–479

    Article  MathSciNet  MATH  Google Scholar 

  21. Hussain F, Hussain R, Anpalagan A, Benslimane A (2020) A new block-based reinforcement learning approach for distributed resource allocation in clustered IoT networks. IEEE Trans Veh Technol 69(3):2891–2904

    Article  Google Scholar 

  22. Xiong X, Zheng K, Lei L, Hou L (2020) Resource allocation based on deep reinforcement learning in IoT edge computing. IEEE J Sel Areas Commun 38(6):1133–1146

    Article  Google Scholar 

  23. Chen X, Liu Y, Cai LX, Chen Z, Zhang D (2020) Resource allocation for wireless cooperative IoT network with energy harvesting. IEEE Trans Wirel Commun 19(7):4879–4893

    Article  Google Scholar 

  24. Zhou Z, Yu S, Chen W, Chen X (2020) CE-IoT: Cost-effective cloud-edge resource provisioning for heterogeneous IoT applications. IEEE Internet Things J 7(9):8600–8614

    Article  Google Scholar 

  25. Gao W, Zhao Z, Yu Z, Min G, Yang M, Huang W (2020) Edge-computing-based channel allocation for deadline-driven IoT networks. IEEE Trans Industr Inf 16(10):6693–6702

    Article  Google Scholar 

  26. Yang H et al (2020) Coordinated resource allocation-based integrated visible light communication and positioning systems for indoor IoT. IEEE Trans Wirel Commun 19(7):4671–4684

    Article  Google Scholar 

  27. Lee J, Kim D, Niyato D (2020) Market analysis of distributed learning resource management for internet of things: a game-theoretic approach. IEEE Internet Things J 7(9):8430–8439

    Article  Google Scholar 

  28. Wang B, Sun Y, Duong TQ, Nguyen LD, Zhao N (2020) Popular matching for security-enhanced resource allocation in social internet of flying things. IEEE Trans Commun 68(8):5087–5101

    Article  Google Scholar 

  29. Ren H, Pan C, Deng Y, Elkashlan M, Nallanathan A (2020) Resource allocation for secure URLLC in mission-critical IoT scenarios. IEEE Trans Commun 68(9):5793–5807

    Article  Google Scholar 

  30. Qin C, Schlemper J, Caballero J, Price AN, Hajnal JV, Rueckert D (2019) Convolutional recurrent neural networks for dynamic mr image reconstruction. IEEE Trans Med Imaging 38(1):280–290

    Article  Google Scholar 

  31. Kang J et al (2021) Optimizing task assignment for reliable blockchain-empowered federated edge learning. IEEE Trans Veh Technol 70(2):1910–1923

    Article  Google Scholar 

  32. Li M, Chen C, Wu H, Guan X, Shen X (2022) Edge-assisted spectrum sharing for freshness-aware industrial wireless networks: a learning-based approach. IEEE Trans Wirel Commun

  33. Zhang W et al (2021) Deep reinforcement learning based resource management for DNN inference in industrial IoT. IEEE Trans Veh Technol 70(8):7605–7618

    Article  Google Scholar 

  34. Zhang W, Yang D, Xu Y, Huang X, Zhang J, Gidlund M (2021) DeepHealth: a self-attention based method for instant intelligent predictive maintenance in industrial internet of things. IEEE Trans Industr Inf 17(8):5461–5473

    Article  Google Scholar 

  35. Fan M, Si Z, Xie X, Liu Y, Liu T (2021) Text backdoor detection using an interpretable rnn abstract model. IEEE Trans Inf Forensics Secur 16:4117–4132

    Article  Google Scholar 

  36. Zhang P, Xue J, Lan C, Zeng W, Gao Z, Zheng N (2020) EleAtt-RNN: adding attentiveness to neurons in recurrent neural networks. IEEE Trans Image Process 29:1061–1073

    Article  MathSciNet  MATH  Google Scholar 

  37. Kang J, Xiong Z, Niyato D, Zou Y, Zhang Y, Guizani M (2020) Reliable federated learning for mobile networks. IEEE Wirel Commun 27(2):72–80

    Article  Google Scholar 

  38. Kollias D, Zafeiriou S (2021) Exploiting Multi-CNN features in CNN-RNN based dimensional emotion recognition on the OMG in-the-wild dataset. IEEE Trans Affect Comput 12(3):595–606

    Article  Google Scholar 

  39. Moore Andrew, Zuev Denis, Crogan Michael (2005) Discriminators for use in flow-based classification, technical report RR-05-13. Queen Mary, University of London, Department of Computer Science

    Google Scholar 

  40. Alqerm I, Pan J (2021) DeepEdge: a new QoE-Based resource allocation framework using deep reinforcement learning for future heterogeneous Edge-IoT applications. IEEE Trans Netw Serv Manag 18(4):3942–3954

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Fundamental Research Funds for Central Universities under Grant 2022RC006, in part by the the National Natural Science Foundation of China under Grant 62201029, and in part by the China Postdoctoral Science Foundation under Grant Grant BX20220029 and 2022M710007.

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Authors and Affiliations

  1. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, 100029, China

    Zhengyang Ai, Pengxiao Li & Lei Shi

  2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Weiting Zhang

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

    Mingyan Li

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  1. Zhengyang Ai
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  2. Weiting Zhang
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  3. Mingyan Li
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Contributions

Zhengyang Ai, Weiting Zhang and Mingyan Li wrote the main manuscript text, Pengxiao Li and Lei Shi prepared the figure 3, figure 4 and figure 5-8. Finally, all authors reviewed the manuscript.

Corresponding author

Correspondence to Weiting Zhang.

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Ai, Z., Zhang, W., Li, M. et al. A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks. Peer-to-Peer Netw. Appl. 16, 749–764 (2023). https://doi.org/10.1007/s12083-022-01441-1

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