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Automatika
Journal for Control, Measurement, Electronics, Computing and Communications
Volume 65, 2024 - Issue 3
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Regular Paper

A novel blockchain enabled resource allocation and task offloading strategy in cloud computing environment

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Pages 973-982 | Received 02 Jan 2024, Accepted 31 Jan 2024, Published online: 12 Mar 2024

References

  • Jehangiri AI, Maqsood T, Ahmad Z, et al. Mobility-aware computational offloading in mobile edge networks: a survey. Cluster Comput. 2021;24(4):2735–2756. doi:10.1007/s10586-021-03268-6
  • Hong Z, Chen W, Huang H, et al. Multihop cooperative computation offloading for industrial IoTedge-cloud computing environments. IEEE Trans Parall Distribut Syst. 2023;30(4):2759–2774.
  • Pan J, McElhannon J. Future edge cloud and edge computing for Internet of things applications. IEEE Internet Things J. 2018;5(1):439–449. doi:10.1109/JIOT.2017.2767608
  • Mao Y, You C, Zhang J, et al. A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutorials. 2017;19(4):2322–2358. doi:10.1109/COMST.2017.2745201
  • Barbarossa S, Sardellitti S, Lorenzo PD. Communicating while computing: distributed mobile cloud computing over 5G heterogeneous networks. IEEE Signal Process Mag. 2014;31(6):45–55. doi:10.1109/MSP.2014.2334709
  • Marei M, Zaatari SE, Li W. Transfer learning enabled convolutional neural networks for estimating health state of cutting tools. Robot Comput Integr Manuf. 2021;71:1–20. doi:10.1016/j.rcim.2021.102145
  • Arya G, Bagwari A, Chauhan DS. Performance analysis of deep learning-based routing protocol for an efficient data transmission in 5G WSN communication. IEEE Access. 2022;10:9340–9356. doi:10.1109/ACCESS.2022.3142082
  • Lenka RK, Kolhar M, Mohapatra H, et al. Cluster-based routing protocol with static hub (CRPSH) for WSN-assisted IoT networks. Sustainability. 2022;14(12):1–17. doi:10.3390/su14127304
  • Zhao Q, Gao Z, Sun C. Q-learning solution for optimal consensus control of discrete-time multi agent systems using reinforcement learning. J Franklin Inst. 2023;356(13):6946–6967.
  • Sengan S, Subramaniyaswamy V, Indragandhi V, et al. Detection of false data cyberattacks for the assessment of security in smart grid using deep learning. Comput Electr Eng. 2021;93:1–13. doi:10.1016/j.compeleceng.2021.107211
  • Ding Y, Ma K, Pu T, et al. A Deep learning-based classification scheme for false data injection attack detection in power system. Electronics (Basel). 2021;10(12):1–17. doi:10.3390/electronics10121459
  • Chen Y, Gu W, Li K. Dynamic task offloading for internet of things in mobile edge computing via deep reinforcement learning. Int J Commun Syst. 2022;5154:1–16.
  • Al Homssi B, Al-Hourani A, Chandrasekharan S, et al. On the bound of energy consumption in cellular IoT networks. IEEE trans green commun netw. 2019;4(2):355–364.
  • G P, T S, S R. Deep learning approaches for ensuring secure task scheduling in IoT systems. Int J Comput Eng Res Trend. 2023;8(5):102–110.
  • Jinaporn N, Saengudomlert P. Impact of gateway placement and energy consumption for data processing on lifetime of IoT networks, in 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2021.
  • Wagih M, Weddell AS, Beeby S. Rectennas for radio-frequency energy harvesting and wireless power transfer: a review of antenna design [Antenna Applications Corner]. IEEE Antennas Propag Mag. 2020;62(5):95–107. doi: 10.1109/MAP.2020.3012872.
  • Farahani B, Firouzi F, Luecking M. The convergence of IoT and distributed ledger technologies (DLT): opportunities, challenges, and solutions. J Netw Comput Appl. 2021;177:1–13. doi:10.1016/j.jnca.2020.102936
  • Endale Mitiku A. Blockchain in healthcare and IoT: a systematic literature review. Array. 2022;14:1–11. doi:10.1016/j.array.2022.100139
  • Song C, Sun Y, Han G, et al. Intrusion detection based on hybrid classifiers for smart grid. Comput Electr Eng. 2021;93:1–15. doi:10.1016/j.compeleceng.2021.107212
  • Xiang C, Yang P, Wu X, et al. A step-aware sampling approach for diffusion profiling in mobile sensor networks. IEEE Trans Veh Technol. 2016;65:8616–8628. doi:10.1109/TVT.2015.2502321
  • Kong L, Wang L, Gong W, et al. LSH-aware multitype health data prediction with privacy preservation in edge environment. World Wide Web. 2022;25:1793–1808. doi:10.1007/s11280-021-00941-z
  • Qi L, Lin W, Zhang X, et al. A correlation graph based approach for personalized and compatible web apis recommendation in mobile app development. IEEE Trans Knowl Data Eng. 2022;11:5444–5457. doi:10.1109/TKDE
  • Qi L, Yang Y, Zhou X, et al. Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0. IEEE Trans Ind Inform. 2021;18(9):6503–6511.
  • Teng T, Ma L. Deep learning-based risk management of financial market in smart grid. Comput Electr Eng. 2022;99:1–16. doi:10.1016/j.compeleceng.2022.107844
  • Majidi SH, Hadayeghparast S, Karimipour H. FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid. Int J Crit Infrastruct Prot. 2022;37:1–22. doi:10.1016/j.ijcip.2022.100508
  • Tian Y, Wang Q, Guo Z, et al. A hybrid deep learning and ensemble learning mechanism for damaged power line detection in smart grids. Soft comput. 2022;26(20):10553–10561. doi:10.1007/s00500-021-06482-x
  • Saxena S, Bhushan B, Ahad MA. Blockchain based solutions to secure IoT: background, integration trends and a way forward. J Netw Comput Appl. 2021;181:1–18. doi:10.1016/j.jnca.2021.103050
  • Pal S, Dorri A, Jurdak R. Blockchain for IoT access control: recent trends and future research directions. J Netw Comput Appl. 2022;203:1–19. doi:10.1016/j.jnca.2022.103371
  • Zappone A, Di Renzo M, Fotock RK. Surface-based techniques for IoT networks: opportunities and challenges. IEEE Inter Thing Mag 2022;5(4):72–77. doi:10.1109/IOTM.001.2200170