[1] Hassan, N., Aazam, M., Tahir, M., & Yau, K. L. A. (2023). Floating Fog: extending fog computing to vast waters for aerial users. Cluster computing, 26(1), 181–195. DOI:10.1007/s10586-022-03567-6
[2] Magotra, B., Malhotra, D., & Dogra, A. K. (2023). Adaptive computational solutions to energy efficiency in cloud computing environment using VM consolidation. Archives of computational methods in engineering, 30(3), 1789–1818. DOI:10.1007/s11831-022-09852-2
[3] Whaiduzzaman, M., Haque, M. N., Rejaul Karim Chowdhury, M., & Gani, A. (2014). A study on strategic provisioning of cloud computing services. Scientific world journal, 2014. DOI:10.1155/2014/894362
[4] Gao, F., Thiebes, S., & Sunyaev, A. (2018). Rethinking the meaning of cloud computing for health care: A taxonomic perspective and future research directions. Journal of medical internet research, 20(7), e10041. DOI:10.2196/10041
[5] Sun, Y., & Zhang, N. (2017). A resource-sharing model based on a repeated game in fog computing. Saudi journal of biological sciences, 24(3), 687–694. DOI:10.1016/j.sjbs.2017.01.043
[6] Liu, H., Li, S., & Sun, W. (2020). Resource allocation for edge computing without using cloud center in smart home environment: A pricing approach. Sensors (Switzerland), 20(22), 1–28. DOI:10.3390/s20226545
[7] Khani, H., & Khanmirza, H. (2019). Randomized routing of virtual machines in IaaS data centers. PeerJ computer science, 2019(9), e211. DOI:10.7717/peerj-cs.211
[8] Yu, S., Gui, X., Lin, J., Tian, F., Zhao, J., & Dai, M. (2014). A security-awareness virtual machine management scheme based on Chinese wall policy in cloud computing. The scientific world journal, 2014. DOI:10.1155/2014/805923
[9] Mohapatra, H., & Rath, A. K. (2022). IoE based framework for smart agriculture: Networking among all agricultural attributes. Journal of ambient intelligence and humanized computing, 13(1), 407–424. DOI:10.1007/s12652-021-02908-4
[10] Detti, A., Nakazato, H., Navarro, J. A. M., Tropea, G., Funari, L., Petrucci, L., … Kanai, K. (2021). Viriot: A cloud of things that offers iot infrastructures as a service. Sensors, 21(19), 6546. DOI:10.3390/s21196546
[11] Ala’anzy, M. A., Othman, M., Hanapi, Z. M., & Alrshah, M. A. (2021). Locust inspired algorithm for cloudlet scheduling in cloud computing environments. Sensors, 21(21), 7308. DOI:10.3390/s21217308
[12] Isazadeh, A., Ziviani, D., & Claridge, D. E. (2023). Global trends, performance metrics, and energy reduction measures in datacom facilities. Renewable and sustainable energy reviews, 174, 113149. DOI:10.1016/j.rser.2023.113149
[13] Tarafdar, A., Debnath, M., Khatua, S., & Das, R. K. (2020). Energy and quality of service-aware virtual machine consolidation in a cloud data center. Journal of supercomputing, 76(11), 9095–9126. DOI:10.1007/s11227-020-03203-3
[14] Panda, S. K., & Sen, S. (2023). SRRA: a novel skewness-based algorithm for cloudlet scheduling. 2023 IEEE 23rd international conference on software quality, reliability, and security (QRS) (pp. 772–781). IEEE. DOI: 10.1109/qrs60937.2023.00080
[15] Mohapatra, H., & Rath, A. K. (2021). A fault tolerant routing scheme for advanced metering infrastructure: an approach towards smart grid. Cluster computing, 24(3), 2193–2211. DOI:10.1007/s10586-021-03255-x
[16] Perumal, K., Mohan, S., Frnda, J., & Divakarachari, P. B. (2022). Dynamic resource provisioning and secured file sharing using virtualization in cloud azure. Journal of cloud computing, 11(1), 1–12. DOI:10.1186/s13677-022-00326-1