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Task offloading in Internet of Things based on the improved multi-objective aquila optimizer

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Abstract

The Internet of Things (IoT) is a network of tens of billions of physical devices that are all connected to each other. These devices often have sensors or actuators, small microprocessors and ways to communicate. With the expansion of the IoT, the number of portable and mobile devices has increased significantly. Due to resource constraints, IoT devices are unable to complete tasks in full. To overcome this challenge, IoT devices must transfer tasks created in the IoT environment to cloud or fog servers. Fog computing (FC) is a computing paradigm that bridges the gap between the cloud and IoT devices and has lower latency compared to cloud computing. An algorithm for task offloading should have smart ways to make the best use of FC resources and cut down on latency. In this paper, an improved multi-objective Aquila optimizer (IMOAO) equipped with a Pareto front is proposed to task offloading from IoT devices to fog nodes with the aim of reducing the response time. To improve the MOAO algorithm, opposition-based learning (OBL) is used to diversify the population and discover optimal solutions. The IMOAO algorithm has been evaluated by the number of tasks and the number of fog nodes in order to reduce the response time. The results show that the average response time and failure rate obtained by IMOAO algorithm are lower compared to particle swarm optimization (PSO) and firefly algorithm (FA). Also, the comparisons show that the IMOAO model has a lower response time compared to multi-objective bacterial foraging optimization (MO-BFO), ant colony optimization (ACO), particle swarm optimization (PSO) and FA.

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The data generated during the current study are available from the corresponding author on reasonable request.

References

  1. Seyfollahi, A., Abeshloo, H., Ghaffari, A.: Enhancing mobile crowdsensing in Fog-based internet of things utilizing Harris hawks optimization,. J. Ambient Intell. Humaniz. Comput. 13(9), 4543–4558 (2022)

    Google Scholar 

  2. Mohammadi, R., Ghaffari, A.: Optimizing reliability through network coding in wireless multimedia sensor networks. Indian J. Sci. Technol. 8(9), 834–841 (2015)

    Google Scholar 

  3. Cheng, B., Wang, M., Zhao, S., Zhai, Z., Zhu, D., Chen, J.: Situation-aware dynamic service coordination in an IoT environment. IEEE/ACM Trans. Netw. 25(4), 2082–2095 (2017)

    Google Scholar 

  4. Lv, Z., Qiao, L., Li, J., Song, H.: Deep-learning-enabled security issues in the internet of things. IEEE Internet Things J. 8(12), 9531–9538 (2020)

    Google Scholar 

  5. Li, B., Zhou, X., Ning, Z., Guan, X., Yiu, K.-F.C.: Dynamic event-triggered security control for networked control systems with cyber-attacks: A model predictive control approach. Inf. Sci. 612, 384–398 (2022)

    Google Scholar 

  6. Cao, K., et al.: Achieving reliable and secure communications in wireless-powered NOMA systems. IEEE Trans. Veh. Technol. 70(2), 1978–1983 (2021)

    Google Scholar 

  7. Dai, X., et al.: Task co-offloading for d2d-assisted mobile edge computing in industrial internet of things. IEEE Trans. Industr. Inf. 19(1), 480–490 (2022)

    MathSciNet  Google Scholar 

  8. Jiang, H., Xiao, Z., Li, Z., Xu, J., Zeng, F., Wang, D.: An energy-efficient framework for internet of things underlaying heterogeneous small cell networks. IEEE Trans. Mob. Comput. 21(1), 31–43 (2020)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. Lv, Z., Song, H.: Mobile internet of things under data physical fusion technology. IEEE Internet Things J. 7(5), 4616–4624 (2019)

    Google Scholar 

  11. Zhang, K., et al.: Training effective deep reinforcement learning agents for real-time life-cycle production optimization. J. Petrol. Sci. Eng. 208, 109766 (2022)

    Google Scholar 

  12. Xiong, Z., et al.: A comprehensive confirmation-based selfish node detection algorithm for socially aware networks. J. Signal Process. Syst. (2023). https://doi.org/10.1007/s11265-023-01868-6

    Article  Google Scholar 

  13. Kamalinia, A., Ghaffari, A.: Hybrid task scheduling method for cloud computing by genetic and DE algorithms. Wireless Personal Commun 97(4), 6301–6323 (2017)

    Google Scholar 

  14. Li, J., et al.: Resource orchestration of cloud-edge–based smart grid fault detection. ACM Trans. Sens. Netw. (TOSN) 18(3), 1–26 (2022)

    MathSciNet  Google Scholar 

  15. Cao, B., Sun, Z., Zhang, J., Gu, Y.: Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing. IEEE Trans. Intell. Transp. Syst. 22(6), 3832–3840 (2021)

    Google Scholar 

  16. Wang, S., Sheng, H., Zhang, Y., Yang, D., Shen, J., Chen, R.: Blockchain-empowered distributed multi-camera multi-target tracking in edge computing. IEEE Trans. Ind. Inform. (2023). https://doi.org/10.1109/TII.2023.3261890

    Article  Google Scholar 

  17. Ni, Q., Guo, J., Wu, W., Wang, H.: Influence-based community partition with sandwich method for social networks. IEEE Trans. Comput. Soc. Syst. 10(2), 819–830 (2022)

    Google Scholar 

  18. She, Q., Hu, R., Xu, J., Liu, M., Xu, K., Huang, H.: Learning high-DOF reaching-and-grasping via dynamic representation of gripper-object interaction. arXiv preprint arXiv:2204.13998, 2022.

  19. Dai, X., et al.: Task offloading for cloud-assisted fog computing with dynamic service caching in enterprise management systems. IEEE Trans. Industr. Inf. 19(1), 662–672 (2022)

    MathSciNet  Google Scholar 

  20. 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 (2022)

    Google Scholar 

  21. Cao, B., et al.: Large-scale many-objective deployment optimization of edge servers. IEEE Trans. Intell. Transp. Syst. 22(6), 3841–3849 (2021)

    Google Scholar 

  22. Wang, Y., Han, X., Jin, S.: MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems. Wireless Netw. 29(1), 47–68 (2023)

    Google Scholar 

  23. Deng, X., Liu, E., Li, S., Duan, Y., Xu, M.: Interpretable multi-modal image registration network based on disentangled convolutional sparse coding. IEEE Trans. Image Process. 32, 1078–1091 (2023)

    Google Scholar 

  24. Liu, G.: Data collection in mi-assisted wireless powered underground sensor networks: directions, recent advances, and challenges. IEEE Commun. Mag. 59(4), 132–138 (2021)

    Google Scholar 

  25. Lv, Z., Chen, D., Feng, H., Wei, W., Lv, H.: Artificial intelligence in underwater digital twins sensor networks. ACM Trans. Sens. Netw. (TOSN) 18(3), 1–27 (2022)

    Google Scholar 

  26. Cao, B., Wang, X., Zhang, W., Song, H., Lv, Z.: A many-objective optimization model of industrial internet of things based on private blockchain. IEEE Netw. 34(5), 78–83 (2020)

    Google Scholar 

  27. Wang, J., et al.: Control of time delay force feedback teleoperation system with finite time convergence. Front. Neurorobot. 16, 877069 (2022)

    Google Scholar 

  28. Pan, S., Lin, M., Xu, M., Zhu, S., Bian, L.-A., Li, G.: A low-profile programmable beam scanning holographic array antenna without phase shifters. IEEE Internet Things J. 9(11), 8838–8851 (2021)

    Google Scholar 

  29. Salimian, M., Ghobaei-Arani, M., Shahidinejad, A.: An evolutionary multi-objective optimization technique to deploy the IoT services in Fog-enabled networks: an autonomous approach. Appl. Artificial Intell. 36(1), 2008149 (2022)

    Google Scholar 

  30. Li, R., et al.: Denoising method of ground-penetrating radar signal based on independent component analysis with multifractal spectrum. Measurement 192, 110886 (2022)

    Google Scholar 

  31. Li, X., Zang, Z., Shen, F., Sun, Y.: Task offloading scheme based on improved contract net protocol and beetle antennae search algorithm in fog computing networks. Mobile Netw. Appl. 25, 2517–2526 (2020)

    Google Scholar 

  32. Li, A., Masouros, C., Swindlehurst, A.L., Yu, W.: 1-bit massive MIMO transmission: embracing interference with symbol-level precoding. IEEE Commun. Mag. 59(5), 121–127 (2021)

    Google Scholar 

  33. Jiang, Y., Li, X.: Broadband cancellation method in an adaptive co-site interference cancellation system. Int. J. Electron. 109(5), 854–874 (2022)

    Google Scholar 

  34. Gong, J., Rezaeipanah, A.: A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks,". Multimed. Tools and Appl. (2023). https://doi.org/10.1007/s11042-023-14349-6

    Article  Google Scholar 

  35. Guo, F., Zhou, W., Lu, Q., Zhang, C.: Path extension similarity link prediction method based on matrix algebra in directed networks. Comput. Commun. 187, 83–92 (2022)

    Google Scholar 

  36. Li, B., Zhang, M., Rong, Y., Han, Z.: Transceiver optimization for wireless powered time-division duplex MU-MIMO systems: non-robust and robust designs. IEEE Trans. Wireless Commun. 21(6), 4594–4607 (2021)

    Google Scholar 

  37. Cao, B., Zhao, J., Gu, Y., Ling, Y., Ma, X.: Applying graph-based differential grouping for multiobjective large-scale optimization. Swarm Evol. Comput. 53, 100626 (2020)

    Google Scholar 

  38. Lu, Z., Cheng, R., Jin, Y., Tan, K.C., Deb, K.: Neural architecture search as multiobjective optimization benchmarks: problem formulation and performance assessment,". IEEE Trans. Evol. Comput. (2023). https://doi.org/10.1109/TEVC.2022.3233364

    Article  Google Scholar 

  39. Xiao, Z., et al.: Multi-objective parallel task offloading and content caching in D2D-aided MEC networks. IEEE Trans. Mobile Comput. (2023). https://doi.org/10.1109/TMC.2022.3199876

    Article  Google Scholar 

  40. Cao, B., et al.: Multiobjective 3-D topology optimization of next-generation wireless data center network. IEEE Trans. Industr. Inf. 16(5), 3597–3605 (2019)

    Google Scholar 

  41. Cao, B., Zhang, W., Wang, X., Zhao, J., Gu, Y., Zhang, Y.: A memetic algorithm based on two_Arch2 for multi-depot heterogeneous-vehicle capacitated arc routing problem. Swarm Evol. Comput. 63, 100864 (2021)

    Google Scholar 

  42. Zheng, W., Yin, L.: Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network. PeerJ Comput. Sci. 8, e908 (2022)

    Google Scholar 

  43. Movahedi, Z., Defude, B., Hosseininia, Am.: An efficient population-based multi-objective task scheduling approach in fog computing systems,". J. Cloud Comput. 10, 53 (2021)

    Google Scholar 

  44. Zhou, G., Zhang, R., Huang, S.: Generalized buffering algorithm. IEEE Access 9, 27140–27157 (2021)

    Google Scholar 

  45. Subbaraj, S., Thiyagarajan, R., Rengaraj, M.: A smart fog computing based real-time secure resource allocation and scheduling strategy using multi-objective crow search algorithm,". J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03354-y

    Article  Google Scholar 

  46. Najafizadeh, A., Salajegheh, A., Rahmani, A.M., Sahafi, A.: Multi-objective task scheduling in cloud-fog computing using goal programming approach,". Cluster Comput. 25, 141–165 (2022)

    Google Scholar 

  47. Yang, S., Li, Q., Li, W., Li, X., Liu, A.-A.: Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Trans. Circuits Syst. Video Technol. 32(11), 8037–8050 (2022)

    Google Scholar 

  48. He, H., Xu, G., Pang, S., Zhao, Z.: AMTS: Adaptive multi-objective task scheduling strategy in cloud computing. China Commun. 13(4), 162–171 (2016)

    Google Scholar 

  49. Yadav, A.M., Tripathi, K.N., Sharma, S.C.: An enhanced multi-objective fireworks algorithm for task scheduling in fog computing environment,". Cluster Comput. 25(2), 983–998 (2022)

    Google Scholar 

  50. Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-qaness, M.A.A., Gandomi, A.H.: Aquila optimizer: a novel meta-heuristic optimization algorithm,". Comput. Ind. Eng. 157(2), 107250 (2021)

    Google Scholar 

  51. Jaganathan, A., Mathesan, K.: Risk prediction model and classification of various hazards in automobile industry using HAO based deep CNN,". Sādhanā 47(3), 165 (2022)

    Google Scholar 

  52. Sun, H., Yu, H., Fan, G., Chen, L.: Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture,". Peer-to-Peer Netw. Appl. 13(2), 548–563 (2020)

    Google Scholar 

  53. Kishor, A., Chakarbarty, C.: Task offloading in fog computing for using smart ant colony optimization,". Wireless Personal Commun. 127(2), 1683–1704 (2022)

    Google Scholar 

  54. Khaledian, N., Khamforoosh, K., Azizi, S., Maihami, V.: IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment. Sustain. Comput.: Inform. Syst. 37(1), 100834 (2023)

    Google Scholar 

  55. Azizi, S., Shojafar, M., Abawajy, J., Buyya, R.: Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: a semi-greedy approach,". J. Netw. Comput. Appl. 201(1), 103333 (2022)

    Google Scholar 

  56. Shahryari, O.-K., Pedram, H., Khajehvand, V., TakhtFooladi, M.D.: Energy and task completion time trade-off for task offloading in fog-enabled IoT networks,". Pervasive and Mobile Comput. 74, 101395 (2021)

    Google Scholar 

  57. Hussain, S.M., Begh, G.R.: Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment,". J. Comput. Sci. 64(5), 101828 (2022)

    Google Scholar 

  58. Aburukba, R.O., Landolsi, T., Omer, D.: A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices,". J. Netw. Comput. Appl. 180, 102994 (2021)

    Google Scholar 

  59. Dai, X., Xiao, Z., Jiang, H., Lui, J.C.S.: UAV-assisted task offloading in vehicular edge computing networks,". IEEE Trans. Mobile Comput. (2023). https://doi.org/10.1109/TMC.2023.3259394

    Article  Google Scholar 

  60. Qiu, S., et al.: Digital-twin-assisted edge-computing resource allocation based on the whale optimization algorithm," (in eng). Sens. (Basel, Switzerland) 22(23), 1–17 (2022)

    Google Scholar 

  61. Du, X., Du, C., Chen, J., Liu, Y.: An energy-aware resource allocation method for avionics systems based on improved ant colony optimization algorithm,". Comput. Electric. Eng. 105(1), 108515 (2023)

    Google Scholar 

  62. Jiang, H., Dai, X., Xiao, Z., Iyengar, A.K.: Joint task offloading and resource allocation for energy-constrained mobile edge computing,". IEEE Trans. Mobile Comput. (2022). https://doi.org/10.1109/TSC.2022.3190276

    Article  Google Scholar 

  63. Dubey, K., Sharma, S.C.: A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing,". Sustain. Comput.: Inform. Syst. 32, 100605 (2021)

    Google Scholar 

  64. Jafari, V., Rezvani, M.H.: Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm,". J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03388-2

    Article  Google Scholar 

  65. Jia, M., Yin, Z., Li, D., Guo, Q., Gu, X.: Toward improved offloading efficiency of data transmission in the IoT-cloud by leveraging secure truncating OFDM. IEEE Internet Things J. 6(3), 4252–4261 (2019)

    Google Scholar 

  66. Kandan, M., Krishnamurthy, A., Selvi, S.A.M., Sikkandar, M.Y., Aboamer, M.A., Tamilvizhi, T.: Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment,". J. Supercomput. 78(7), 10176–10190 (2022)

    Google Scholar 

  67. Lu, S., Liu, M., Yin, L., Yin, Z., Liu, X., Zheng, W.: The multi-modal fusion in visual question answering: a review of attention mechanisms. PeerJ Comput. Sci. 9, e1400 (2023)

    Google Scholar 

  68. 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 and Netw. 34, 101004 (2023)

    Google Scholar 

  69. Shen, Y., Ding, N., Zheng, H.-T., Li, Y., Yang, M.: Modeling relation paths for knowledge graph completion. IEEE Trans. Knowl. Data Eng. 33(11), 3607–3617 (2020)

    Google Scholar 

  70. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Google Scholar 

  71. Lv, Z., Kumar, N.: Software defined solutions for sensors in 6G/IoE. Comput. Commun. 153, 42–47 (2020)

    Google Scholar 

  72. Li, R., Wu, X., Tian, H., Yu, N., Wang, C.: Hybrid memetic pretrained factor analysis-based deep belief networks for transient electromagnetic inversion. IEEE Trans. Geosci. Remote Sens. 60, 1–20 (2022)

    Google Scholar 

  73. Li, B., Li, Q., Zeng, Y., Rong, Y., Zhang, R.: 3D trajectory optimization for energy-efficient UAV communication: a control design perspective. IEEE Trans. Wireless Commun. 21(6), 4579–4593 (2021)

    Google Scholar 

  74. Zhu, H., Xue, M., Wang, Y., Yuan, G., Li, X.: Fast visual tracking with siamese oriented region proposal network. IEEE Signal Process. Lett. 29, 1437–1441 (2022)

    Google Scholar 

  75. Zhao, Z., Xu, G., Zhang, N., Zhang, Q.: Performance analysis of the hybrid satellite-terrestrial relay network with opportunistic scheduling over generalized fading channels. IEEE Trans. Veh. Technol. 71(3), 2914–2924 (2022)

    Google Scholar 

  76. Kennedy, J., Eberhart, R.: Particle swarm optimization," In: Proceedings of ICNN'95 - International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948 vol.4.

  77. Yang, X.-S.: Firefly algorithms for multimodal optimization," In: Stochastic Algorithms: Foundations and Applications, Berlin, Heidelberg, 2009, pp. 169–178: Springer Berlin Heidelberg.

  78. Babar, M., Din, A., Alzamzami, O., Karamti, H., Khan, A., Nawaz, M.: A bacterial foraging based smart offloading for Iot sensors in edge computing,". Comput. Electrical Engineering 102(1), 108123 (2022)

    Google Scholar 

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Authors and Affiliations

  1. Department of Management Information System, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Masoud Nematollahi

  2. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Ali Ghaffari

  3. Computer Engineering Department, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey

    Ali Ghaffari

  4. Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran

    Abbas Mirzaei

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  1. Masoud Nematollahi
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  2. Ali Ghaffari
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M.N. wrote the manuscript and carried out the experiment. A.G. and A.M. reviewed and supervised the manuscript.

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Correspondence to Ali Ghaffari.

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Nematollahi, M., Ghaffari, A. & Mirzaei, A. Task offloading in Internet of Things based on the improved multi-objective aquila optimizer. SIViP 18, 545–552 (2024). https://doi.org/10.1007/s11760-023-02761-2

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