Service Deployment Strategy for Predictive Analysis of FinTech IoT Applications in Edge Networks

dc.contributor.author Munusamy, Ambigavathi
dc.contributor.author Adhikari, Mainak
dc.contributor.author Balasubramanian, Venki
dc.contributor.author Khan, Mohammad Ayoub
dc.contributor.author Menon, Varun G.
dc.contributor.author Rawat, Danda
dc.contributor.author Srirama, Satish Narayana
dc.date.accessioned 2022-03-27T06:03:34Z
dc.date.available 2022-03-27T06:03:34Z
dc.date.issued 2021-01-01
dc.description.abstract The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for Financial Technology (FinTech). The emergence of the Next-Generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyse the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this paper, we develop a ranked-based service deployment strategy and an Artificial Intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users & #x2019; satisfaction ratio. Besides that, an efficient service deployment strategy is developed using Hall & #x2019;s theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVM) algorithm is used at edge networks for analysing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.
dc.identifier.citation IEEE Internet of Things Journal
dc.identifier.uri 10.1109/JIOT.2021.3078148
dc.identifier.uri https://ieeexplore.ieee.org/document/9425579/
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/9227
dc.subject Analytical models
dc.subject Cloud computing
dc.subject Computational modeling
dc.subject Delays
dc.subject Edge networks.
dc.subject FinTech applications
dc.subject Internet of Things
dc.subject IoT
dc.subject Servers
dc.subject Service deployment
dc.subject Support Vector Machines
dc.subject Task analysis
dc.subject Task classification
dc.title Service Deployment Strategy for Predictive Analysis of FinTech IoT Applications in Edge Networks
dc.type Journal. Article
dspace.entity.type
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