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TOS-LRPLM: a task value-aware offloading scheme in IoT edge computing system

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Abstract

Maximizing the utility of large-scale Internet of Things (IoT) is an important issue in practice. In this paper, we attempt to improve the performance of IoT edge computing system (IoT ECS) from a perspective of task value, which decays with execution time. We consider such an IoT ECS which is composed of multiple mobile equipments (MEs) and edge nodes (ENs). Each ME holds a task with a certain task value decay curve (TVDC) that decides whether to execute locally or at the edge nodes. Further more, we use a system utility function to describe the overall performance of the network by trading-off task value, calculation cost, and network risk factor. We convert the IoT ECS utility maximization problem into a multi-knapsack and multi-dimensional knapsack problem and prove it’s NP-hard. Then, we adopt the piecewise linearization method to conquer the non-linear, even non-convex challenge of the objective function, and develop a distributed task offloading scheme based on Lagrange relaxation framework (TOS-LRPLM). Finally, numerical experiments prove the effectiveness of our proposed strategies and its superiority to others.

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Data availability

Data sharing is not applicable to this paper as no datasets were generated or analysed during the current study.

Code availability

The custom code required to reproduce these findings cannot be shared at this time as the code also forms part of an ongoing study.

References

  1. Agiwal, M., Saxena, N., Roy, A.: Towards connected living: 5G enabled internet of things (IoT). IETE Tech. Rev. 36(2), 190–202 (2019)

    Article  Google Scholar 

  2. Qiu, J., Tian, Z., Du, C., Zuo, Q., Su, S., Fang, B.: A survey on access control in the age of internet of things. IEEE Internet Things J. 7(6), 4682–4696 (2020). https://doi.org/10.1109/JIOT.2020.2969326

    Article  Google Scholar 

  3. Yu, W., Liang, F., He, X., Hatcher, W.G., Lu, C., Lin, J., Yang, X.: A survey on the edge computing for the internet of things. IEEE Access 6, 6900–6919 (2018). https://doi.org/10.1109/ACCESS.2017.2778504

    Article  Google Scholar 

  4. Khan, A., Othman, M., Madani, S.A., Khan, S.U.: A survey of mobile cloud computing application models. IEEE Commun. Surv. Tutor. 16(1), 393–413 (2014). https://doi.org/10.1109/SURV.2013.062613.00160

    Article  Google Scholar 

  5. Lee, K., Chu, D., Cuervo, E., Kopf, J., Wolman, A., Degtyarev, Y., Grizan, S., Flinn, J.: Outatime: Using speculation to enable low-latency continuous interaction for mobile cloud gaming. Mobile Comput. Commun. Rev. 19(3), 14–17 (2015)

    Google Scholar 

  6. Li, C., Li, L.Y.: Cost and energy aware service provisioning for mobile client in cloud computing environment. J. Supercomput. 71(4), 1196–1223 (2015)

    Article  Google Scholar 

  7. Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., Wang, W.: A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access 5, 6757–6779 (2017). https://doi.org/10.1109/ACCESS.2017.2685434

    Article  Google Scholar 

  8. Chen, X., Liu, Z., Chen, Y., Li, Z.: Mobile edge computing based task offloading and resource allocation in 5g ultra-dense networks. IEEE Access 7, 184172–184182 (2019). https://doi.org/10.1109/ACCESS.2019.2960547

    Article  Google Scholar 

  9. Fan, Y., Zhai, L., Wang, H.: Cost-efficient dependent task offloading for multiusers. IEEE Access 7, 115843–115856 (2019). https://doi.org/10.1109/ACCESS.2019.2936208

    Article  Google Scholar 

  10. Liu, F., Huang, Z., Wang, L.: Energy-efficient collaborative task computation offloading in cloud-assisted edge computing for IoT sensors. Sensors 19(5), 1 (2019)

    Article  Google Scholar 

  11. Lin, Q., Wang, F., Xu, J.: Optimal task offloading scheduling for energy efficient D2D cooperative computing. IEEE Commun. Lett. 23(10), 1816–1820 (2019). https://doi.org/10.1109/LCOMM.2019.2931719

    Article  Google Scholar 

  12. Hao, Y., Chen, M., Hu, L., Hossain, M.S., Ghoneim, A.: Energy efficient task caching and offloading for mobile edge computing. IEEE Access 6, 11365–11373 (2018). https://doi.org/10.1109/ACCESS.2018.2805798

    Article  Google Scholar 

  13. Tajallifar, M., Ebrahimi, S., Javan, M.R., Mokari, N., Chiaraviglio, L.: Qos-aware joint power allocation and task offloading in a mec/nfv-enabled C-RAN network. CoRR (2019). arXiv:1912.00187

  14. Hu, J., Li, K., Liu, C., Li, K.: Game-based task offloading of multiple mobile devices with QoS in mobile edge computing systems of limited computation capacity. ACM Trans. Embed. Comput. Syst. 19(4), 1–21 (2020). https://doi.org/10.1145/3398038

    Article  Google Scholar 

  15. Paymard, P., Mokari, N., Orooji, M.: Task scheduling based on priority and resource allocation in multi-user multi-task mobile edge computing system. In: 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1–7 (2019). https://doi.org/10.1109/PIMRC.2019.8904174

  16. Li, L., Zhang, H.: Delay optimization strategy for service cache and task offloading in three-tier architecture mobile edge computing system. IEEE Access 8, 170211–170224 (2020). https://doi.org/10.1109/ACCESS.2020.3023771

    Article  Google Scholar 

  17. Wang, Y., Tao, X., Zhang, X., Zhang, P., Hou, Y.T.: Cooperative task offloading in three-tier mobile computing networks: An ADMM framework. IEEE Trans. Veh. Technol. 68(3), 2763–2776 (2019). https://doi.org/10.1109/TVT.2019.2892176

    Article  Google Scholar 

  18. Qiao, G., Leng, S., Zhang, K., He, Y.: Collaborative task offloading in vehicular edge multi-access networks. IEEE Commun. Mag. 56(8), 48–54 (2018). https://doi.org/10.1109/MCOM.2018.1701130

    Article  Google Scholar 

  19. Zhang, H., Yang, Y., Huang, X., Fang, C., Zhang, P.: Ultra-low latency multi-task offloading in mobile edge computing. IEEE Access 9, 32569–32581 (2021). https://doi.org/10.1109/ACCESS.2021.3061105

    Article  Google Scholar 

  20. Howard, R.A.: Information value theory. IEEE Trans. Syst. Sci. Cybern. 2(1), 22–26 (1966)

    Article  Google Scholar 

  21. Bisdikian, C., Kaplan, L.M., Srivastava, M.B.: On the quality and value of information in sensor networks. ACM Trans. Sens. Netw. 9(4), 4:81-48:26 (2013). https://doi.org/10.1145/2489253.2489265

    Article  Google Scholar 

  22. Zhang, J., Li, Z., Tang, S.: Value of information aware opportunistic duty cycling in solar harvesting sensor networks. IEEE Trans. Ind. Inf. 12(1), 348–360 (2016). https://doi.org/10.1109/TII.2015.2508745

    Article  Google Scholar 

  23. Patil, K., Turck, K.D., Fiems, D.: A two-queue model for optimising the value of information in energy-harvesting sensor networks. Perform. Eval. 119, 27–42 (2018). https://doi.org/10.1016/j.peva.2017.12.003

    Article  Google Scholar 

  24. Gjanci, P., Petrioli, C., Basagni, S., Phillips, C.A., Bölöni, L., Turgut, D.: Path finding for maximum value of information in multi-modal underwater wireless sensor networks. IEEE Trans. Mob. Comput. 17(2), 404–418 (2018). https://doi.org/10.1109/TMC.2017.2706689

    Article  Google Scholar 

  25. Tran, T.X., Hajisami, A., Pandey, P., Pompili, D.: Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges. IEEE Commun. Mag. 55(4), 54–61 (2017). https://doi.org/10.1109/MCOM.2017.1600863

    Article  Google Scholar 

  26. Liu, Q., Chen, Z., Wu, J., Deng, Y., Liu, K., Wang, L.: An efficient task scheduling strategy utilizing mobile edge computing in autonomous driving environment. Electronics 8(11), 1221 (2019). https://doi.org/10.3390/electronics8111221

    Article  Google Scholar 

  27. Li, Z., Ge, J., Yang, H., Huang, L., Hu, H., Hu, H., Luo, B.: A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Future Gener. Comput. Syst. 65, 140–152 (2016). https://doi.org/10.1016/j.future.2015.12.014

    Article  Google Scholar 

  28. Wu, D., Shen, G., Huang, Z., Cao, Y., Du, T.: A trust-aware task offloading framework in mobile edge computing. IEEE Access 7, 150105–150119 (2019). https://doi.org/10.1109/ACCESS.2019.2947306

    Article  Google Scholar 

  29. Li, M., Sun, Y., Lu, H., Maharjan, S., Tian, Z.: Deep reinforcement learning for partially observable data poisoning attack in crowdsensing systems. IEEE Internet Things J. 7(7), 6266–6278 (2020). https://doi.org/10.1109/JIOT.2019.2962914

    Article  Google Scholar 

  30. Tian, Z., Luo, C., Qiu, J., Du, X., Guizani, M.: A distributed deep learning system for web attack detection on edge devices. IEEE Trans. Ind. Inf. 16(3), 1963–1971 (2020). https://doi.org/10.1109/TII.2019.2938778

    Article  Google Scholar 

  31. Latifa, E., Kiram, M.A.E., Ghazouani, M.E.: New security risk value estimate method for android applications. Comput. J. 63(4), 593–603 (2020). https://doi.org/10.1093/comjnl/bxz109

    Article  Google Scholar 

  32. Hussein, M., Mousa, M.: Efficient task offloading for IoT-based applications in fog computing using ant colony optimization. IEEE Access 8, 37191–37201 (2020). https://doi.org/10.1109/ACCESS.2020.2975741

    Article  Google Scholar 

  33. Liu, L., Qin, X., Zhang, Z., Zhang, P.: Joint task offloading and resource allocation for obtaining fresh status updates in multi-device MEC systems. IEEE Access 8, 38248–38261 (2020). https://doi.org/10.1109/ACCESS.2020.2976048

    Article  Google Scholar 

  34. Colbourn, C.J., Cui, M., Lloyd, E.L., Syrotiuk, V.R.: A carrier sense multiple access protocol with power backoff (CSMA/PB). Ad Hoc Netw. 5(8), 1233–1250 (2007). https://doi.org/10.1016/j.adhoc.2007.02.017

    Article  Google Scholar 

  35. Kim, M., Chung, J.: Autonomous transmission power control for CSMA/CA-based wireless networks. In: International Conference on Information and Communication Technology Convergence, ICTC 2013, Jeju Island, South Korea, 4–16 October 2013, pp. 419–420. IEEE (2013). https://doi.org/10.1109/ICTC.2013.6675386

  36. Correa-Posada, C.M., Sanchez-Martin, P.: Integrated power and natural gas model for energy adequacy in short-term operation. IEEE Trans. Power Syst. 30(6), 3347–3355 (2015). https://doi.org/10.1109/TPWRS.2014.2372013

    Article  Google Scholar 

  37. Shao, C., Wang, X., Shahidehpour, M., Wang, X., Wang, B.: An milp-based optimal power flow in multicarrier energy systems. IEEE Trans. Sustain. Energy 8(1), 239–248 (2017). https://doi.org/10.1109/TSTE.2016.2595486

    Article  Google Scholar 

  38. Boyd, S., Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  39. Kuhn, H.W.: The Hungarian Method for the Assignment Problem, pp. 29–47. Springer, Berlin (2010). https://doi.org/10.1007/978-3-540-68279-0_2

    Book  MATH  Google Scholar 

  40. Wang, Y., Tao, X., Zhang, X., Mao, G.: Joint caching placement and user association for minimizing user download delay. IEEE Access 4, 8625–8633 (2016). https://doi.org/10.1109/ACCESS.2016.2633488

    Article  Google Scholar 

  41. Diwekar, U.: Nonlinear Programming. Introduction to Applied Optimization (2008)

  42. Karmarkar, N.: A new polynomial-time algorithm for linear programming. In: Proceedings of the sixteenth annual ACM symposium on Theory of computing, pp. 302–311 (1984)

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Acknowledgements

This work is supported by the Natural Science Foundation of China (Grant No. 61872104).

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Authors and Affiliations

  1. Harbin Engineering University, Harbin, 150001, China

    Jiayu Sun, Huiqiang Wang, Guangsheng Feng, Hongwu Lv, Jingyao Liu & Zihan Gao

  2. Peng Cheng Laboratory, Shenzhen, 518000, Guangdong, China

    Huiqiang Wang

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Contributions

JS contributed to the conception of the study and performed the experiment, HW and GF contributed significantly to analysis, HL helped perform the analysis with constructive discussions, JL and ZG helped prepare the manuscript.

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Correspondence to Guangsheng Feng.

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Sun, J., Wang, H., Feng, G. et al. TOS-LRPLM: a task value-aware offloading scheme in IoT edge computing system. Cluster Comput 26, 319–335 (2023). https://doi.org/10.1007/s10586-021-03498-8

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