An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm.

Hai, Tao; Zhou, Jincheng; Lu, Ye; Jawawi, Dayang N.A.; Wang, Dan; Selvarajan, Shitharth; Manoharan, Hariprasath; Ibeke, Ebuka

Abstract

Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%.

Citation

HAI, T., ZHOU, J., LU, Y., JAWAWI, D., WANG, D., SELVARAJAN, S., MANOHARAN, H. and IBEKE, E. 2023. An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm. Journal of cloud computing [online], 12, article 73. Available from: https://doi.org/10.1186/s13677-023-00449-z

• Journal Article Type: Article
• Acceptance Date: Sep 29, 2022
• Online Publication Date: May 9, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Sep 30, 2022
• Publicly Available Date: Sep 30, 2022
• Journal: Journal of cloud computing
• Electronic ISSN: 2192-113X
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Article Number: 73
• DOI: https://doi.org/10.1186/s13677-023-00449-z
• Keywords: Cloud computing; Mobile nodes; Emergency applications; Gain; Energy
• Public URL: https://rgu-repository.worktribe.com/output/1764717