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Load Balancing Cloud Storage Data Distribution Strategy of Internet of Things Terminal Nodes considering Access Cost.

Authors :
Wu J
Xu W
Xia J
Source :
Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jan 24; Vol. 2022, pp. 7849726. Date of Electronic Publication: 2022 Jan 24 (Print Publication: 2022).
Publication Year :
2022

Abstract

With the rapid development of Internet of Things (IoT) technology, IoT terminal nodes are facing many challenges in data storage, distribution, and data management. In particular, in the IoT terminal nodes considering access cost, the corresponding data distribution and storage are professional, complex, and miscellaneous. Based on the abovementioned current situation, this article innovatively proposes a complex sensor data placement algorithm based on the cloud storage distribution of IoT terminal nodes. Under this algorithm, the accurate division of IoT data I/O methods is realized through reasonable configuration. Through the adaptive sensing algorithm, while fully considering the access cost of the algorithm, the performance of the IoT data storage system is further optimized. In the corresponding terminal node load balancing problem, this article innovatively proposes the terminal node data sorting and distribution algorithm through the node data. The sorting and distribution algorithm realizes the precise segmentation of the IoT data to be processed, thereby realizing the improvement of data reading and processing speed. Based on the proposed algorithm, this article designs a load balancing cloud storage data distribution optimization system of IoT terminal nodes considering access cost and carries out experimental verification in a real environment. The experimental results show that the data pattern division accuracy corresponding to the proposed distribution strategy is improved to 97.13% and the corresponding data access efficiency is improved to 98.3%, compared with the traditional distribution strategy. Therefore, the data distribution strategy proposed in this article has obvious performance advantages and further promotion value.<br />Competing Interests: The authors declare that they have no conflicts of interest.<br /> (Copyright © 2022 Jiansheng Wu et al.)

Details

Language :
English
ISSN :
1687-5273
Volume :
2022
Database :
MEDLINE
Journal :
Computational intelligence and neuroscience
Publication Type :
Academic Journal
Accession number :
35111212
Full Text :
https://doi.org/10.1155/2022/7849726