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A Secure IoT Applications Allocation Framework for Integrated Fog-Cloud Environment

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Abstract

Applications of the Internet of Things (IoT) are used in several areas to create a smart environment such as healthcare, smart agriculture, smart cities, transportation, and water management, etc. Due to the high pace of IoT technology adoption, Big Data generation is increasing excessively, requiring an efficient platform like cloud computing to process a large amount of data. On the other hand, time/delay-sensitive and real-time applications cannot be processed in the cloud due to high latency and energy consumption. Hence, a new emerging computing model named fog has emerged to address the mentioned issues and provide a complementary solution. However, Fog nodes provide limited cloud services in minimum delay and energy at the local node, but they cannot process the highly computation-oriented IoT applications. Furthermore, an adaptive cloud-fog integrated framework is proposed to process entire IoT applications and significantly improve the latency, computation cost, load balancing, and energy consumption by accommodating the resources in the form of virtual machine instances. This article exploited the features of two metaheuristic-based techniques Cuckoo Search Optimization (CSO) and Partial Swarm Optimization (PSO). We have developed a secure framework to solve the allocation of the IoT services in the cloud-fog environment while minimizing the mentioned influential parameters. The performance of the proposed framework is rigorously evaluated at synthetic datasets and heterogeneity of resources in fog as well as cloud simulation environment. The simulation results proved that the proposed hybrid metaheuristic algorithm outperforms other baseline policies and improves the various influential parameters.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Mansouri, N., Zade, B.M.H., Javidi, M.M.: Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Comput. Ind. Eng. 130, 597–633 (2019)

    Article  Google Scholar 

  2. Bansal, M., Malik, S.K.: A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing. Sustain. Comput. Inform. Syst. 28, 100429 (2020)

    Google Scholar 

  3. Souza, V.B., Masip-Bruin, X., Marín-Tordera, E., Ramírez, W., Sanchez, S.: "Towards distributed service allocation in fog-to-cloud (f2c) scenarios." In: 2016 IEEE global communications conference (GLOBECOM), pp. 1–6. IEEE, (2016)

  4. Li, W., Santos, I., Delicato, F.C., Pires, P.F., Pirmez, L., Wei, W., Song, H., Zomaya, A., Khan, S.: System modelling and performance evaluation of a three-tier cloud of things. Futur. Gener. Comput. Syst. 70, 104–125 (2017)

    Article  Google Scholar 

  5. Maddikunta, P.K.R., Gadekallu, T.R., Kaluri, R., Srivastava, G., Parizi, R.M., Khan, M.S.: Green communication in IoT networks using a hybrid optimization algorithm. Comput. Commun. 159, 97–107 (2020)

    Article  Google Scholar 

  6. Ahmed, U., Lin, J.C.-W., Srivastava, G., Aleem, M.: A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster. Soft. Comput. 25(1), 407–420 (2021)

    Article  Google Scholar 

  7. Khalid, M., Yousaf, M.M., Iftikhar, Y., Fatima, N.: "Establishing the state of the art knowledge domain of cloud computing." In: Advanced Computer and Communication Engineering Technology, pp. 1001–1014. Springer, Cham, (2016)

  8. Mahmud, R., Srirama, S.N., Ramamohanarao, K., Buyya, R.: Profit-aware application placement for integrated fog–cloud computing environments. J. Parallel Distrib. Comput. 135, 177–190 (2020)

    Article  Google Scholar 

  9. Azimi, I., Anzanpour, A., Rahmani, A.M., Liljeberg, P., Salakoski, T.: "Medical warning system based on Internet of Things using fog computing." In: 2016 International Workshop on Big Data and Information Security (IWBIS), pp. 19–24. IEEE, (2016)

  10. Seth, B., Dalal, S., Jaglan, V., Le, D.-N., Mohan, S., Srivastava, G.: Integrating encryption techniques for secure data storage in the cloud. Trans. Emerg. Telecommun. Technol. e4108 (2020)

  11. Vilela, P.H., Rodrigues, J.J.P.C., Solic, P., Saleem, K., Furtado, V.: Performance evaluation of a fog-assisted IoT solution for e-health applications. Futur. Gener. Comput. Syst. 97, 379–386 (2019)

    Article  Google Scholar 

  12. Thirumalai, C., Mohan, S., Srivastava, G.: An efficient public key secure scheme for cloud and IoT security. Comput. Commun. 150, 634–643 (2020)

    Article  Google Scholar 

  13. Adhikari, M., Gianey, H.: Energy efficient offloading strategy in fog-cloud environment for IoT applications. Internet Things. 6, 100053 (2019)

    Article  Google Scholar 

  14. Aburukba, R.O., AliKarrar, M., Landolsi, T., El-Fakih, K.: Scheduling internet of things requests to minimize latency in hybrid fog–cloud​ computing. Futur. Gener. Comput. Syst. 111, 539–551 (2020)

    Article  Google Scholar 

  15. Yadav, V., Natesha, B.V., Guddeti, R.M.R.. "GA-PSO: Service Allocation in Fog Computing Environment Using Hybrid Bio-Inspired Algorithm." In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), pp. 1280–1285. IEEE (2019)

  16. Alli, A.A., Alam, M.M.: SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications. Internet Things. 7, 100070 (2019)

    Article  Google Scholar 

  17. M. Abdelmoneem et al., "A Cloud-Fog Based Architecture for IoT Applications Dedicated to Healthcare," In: IEEE International Conference on Communications (ICC), Pp. 1–6 (2019)

  18. Yasmeen, A., Javaid, N., Rehman, O.U., Iftikhar, H., Malik, M.F., Muhammad, F.J. "Efficient resource provisioning for smart buildings utilizing fog and cloud based environment." In: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 811-816. IEEE (2018)

  19. Naha, R., et al.: deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment. Futur. Gener. Comput. Syst. 104, 131–141 (2020)

    Article  Google Scholar 

  20. Shah-Mansouri, H., Wong, V.W.S.: Hierarchical fog-cloud computing for IoT systems: A computation offloading game. IEEE Internet Things J. 5(4), 3246–3257 (2018)

    Article  Google Scholar 

  21. Siasi, N., Jasim, M., Aldalbahi, A., Ghani, N.: Delay-aware SFC provisioning in hybrid fog-cloud computing architectures. IEEE Access. 8, 167383–167396 (2020)

    Article  Google Scholar 

  22. Tang, Z., Srivastava, G., Liu, S.: Swarm intelligence and ant colony optimization in accounting model choices. J. Intell. Fuzzy Syst. 38(3), 2415–2423 (2020)

    Article  Google Scholar 

  23. Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016)

    Google Scholar 

  24. Chen, X., Zhou, Y., Yang, L., Lu, L.: Hybrid fog/cloud computing resource allocation: joint consideration of limited communication resources and user credibility. Comput. Commun. 169, 48–58 (2021)

    Article  Google Scholar 

  25. Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy. 21(9), 902 (2019)

    Article  MathSciNet  Google Scholar 

  26. Gad-Elrab, A.A.A., Noaman, A.Y.: A two-tier bipartite graph task allocation approach based on fuzzy clustering in cloud–fog environment. Futur. Gener. Comput. Syst. 103, 79–90 (2020)

    Article  Google Scholar 

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

  28. Yang, X.-S., Deb, S.: Cuckoo search: recent advances and applications. Neural Comput. & Applic. 24(1), 169–174 (2014)

    Article  Google Scholar 

  29. Bouyer, A., Hatamlou, A.: An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms. Appl. Soft Comput. 67, 172–182 (2018)

    Article  Google Scholar 

  30. Dash, J., Dam, B., Swain, R.: Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization. Appl. Soft Comput. 52, 435–445 (2017)

    Article  Google Scholar 

  31. Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: iFogSim: a toolkit formodeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw. Pract. Experience. 47(9), 1275–1296 (2017)

    Article  Google Scholar 

  32. Buyya, R., Ranjan, R., Calheiros, R.N.: "Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities." In: 2009 international conference on high performance computing & simulation, pp. 1–11. IEEE (2009)

  33. Rafique, H., Shah, M.A., Islam, S.U., Maqsood, T., Khan, S., Maple, C.: A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing. IEEE Access. 7, 115760–115773 (2019)

    Article  Google Scholar 

  34. Mulani, K., Talukdar, P., Das, A., Alagirusamy, R.: Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems. Int. J. Heat Mass Transf. 89, 359–378 (2015)

    Article  Google Scholar 

  35. Kumar, M., Sharma, S.C.. "PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing." Neural Comput. & Applic. 1–24 (2019)

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Authors and Affiliations

  1. Cloud Computing and Wireless Sensor Lab, Indian Institute of Technology Roorkee, Roorkee, India

    Kalka Dubey & S. C. Sharma

  2. Department of Information Technology, B R Ambedkar National Institute of Technology, Jalandhar, India

    Mohit Kumar

Authors
  1. Kalka Dubey
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  2. S. C. Sharma
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  3. Mohit Kumar
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Correspondence to Kalka Dubey.

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Dubey, K., Sharma, S.C. & Kumar, M. A Secure IoT Applications Allocation Framework for Integrated Fog-Cloud Environment. J Grid Computing 20, 5 (2022). https://doi.org/10.1007/s10723-021-09591-x

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