Open Access

ARTICLE

Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

by Nguyen A. Tuan¹, D. Akila², Souvik Pal³, Bikramjit Sarkar⁴, Thien Khai Tran¹, G. Mothilal Nehru², Dac-Nhuong Le^5,6,*

1 Faculty of Information Technology, Ho Chi Minh City University of Foreign Languages-Information Technology, Ho Chi Minh City, 70000, Vietnam
2 Department of Information Technology, Vels Institute of Science, Technology & Advanced Studies, Chennai, 600117, India
3 Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, 700156, India
4 Department of Computer Science and Engineering, JIS College of Engineering, Nadia, 741235, India
6 Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam
7 School of Computer Science, Duy Tan University, Danang, 550000, Vietnam

* Corresponding Author: Dac-Nhuong Le. Email:

(This article belongs to the Special Issue: Future Generation of Artificial Intelligence and Intelligent Internet of Things)

Computers, Materials & Continua 2022, 72(1), 1357-1372. https://doi.org/10.32604/cmc.2022.024096

Received 04 October 2021; Accepted 10 January 2022; Issue published 24 February 2022

Abstract

This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio, coverage ratio and sensing error. Data optimization was performed based on the availability of cloud resource, sensor energy, data flow volume and the centroid of each state. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.

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Keywords

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