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

Computers, materials & continua, 2022, Vol.72 (1), p.1357-1372

2022. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1546-2226 ;ISSN: 1546-2218 ;EISSN: 1546-2226 ;DOI: 10.32604/cmc.2022.024096

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  • Title:
    Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform
  • Author: A. Tuan, Nguyen ; Akila, D. ; Pal, Souvik ; Sarkar, Bikramjit ; Khai Tran, Thien ; Mothilal Nehru, G. ; Le, Dac-Nhuong
  • Subjects: Algorithms ; Centroids ; Internet of Things ; Networks ; Optimization ; Reliability analysis ; Sensors
  • Is Part Of: Computers, materials & continua, 2022, Vol.72 (1), p.1357-1372
  • Description: 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.
  • Publisher: Henderson: Tech Science Press
  • Language: English
  • Identifier: ISSN: 1546-2226
    ISSN: 1546-2218
    EISSN: 1546-2226
    DOI: 10.32604/cmc.2022.024096
  • Source: Coronavirus Research Database
    ProQuest Central
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