Improve medical IoT based on DFC algorithm and machine learning-Reference-Cited by-同舟云学术

Improve medical IoT based on DFC algorithm and machine learning

Published:2022-12-03 Issue: Volume: Page:
ISSN:
Container-title:
language:
Short-container-title:

Author:

Shao Wei¹,Ding Yihang²,Wen Jinghao³,Zhu Pengxu⁴,Luo Meng⁵,Ou Lisong⁶

Affiliation:

1. Anhui University of Technology

2. Henan Normal University

3. Central China Normal University

4. University of North Carolina at Greensboro

5. Wuhan University

6. Guilin University of Technology

Abstract

Abstract With the rapid development of IoT technology, medical IoT is one of the current research focuses and hotspots. In the practical application of medical IoT, achieving accurate localization and effective data fusion is conducive to improving the operational efficiency of medical IoT and reducing the power consumption of the network. Therefore, this paper uses SVM and KNN algorithms to improve the localization accuracy, and proposes an event-driven cluster-tree data fusion algorithm DFC based on the special characteristics of data in medical IoT.The DFC algorithm uses a combined cluster-tree topology to dynamically partition clusters and decide the decision range of the corresponding cluster based on the severity of the event, and then constructs the data fusion within the cluster from the current cluster head The data propagation along the fusion tree can improve the accuracy of the data and extend the lifetime of the network. In this paper, we analyze the fusion delay problem in the network and propose a minimum fusion delay method by calculating the fusion waiting time of nodes. Experiments demonstrate that the DFC algorithm has better accuracy and fusion through machine learning, an improvement of the medical IoT. This is of great importance in real-life applications

Publisher

Research Square Platform LLC

Reference31 articles.

1. An Energy-Balanced Data Gathering Algorithm for Linear Wireless Sensor Networks;Liu An-Feng;International Journal of Wireless Information Networks,2010

2. Angelopoulos A, Bourbakis N. G. Prognosis-A Wearable Health-Monitoring System for People at Risk: Methodology and Modeling. Information Technology in Biomedicine. IEEE Transactions on. 2010, 14(3): 613–621.Wen-Hwa Liao; Yucheng Kaoa and Chien-Ming Fana. Data aggregation in wireless sensor networks using ant colony algorithm. Journal of Network and Computer Applications.2008: 2842–2852.

3. Fenglin Bu. Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services;Boyi Xu Li Da;IEEE Trans. Industrial Informatics,2014

4. Cao Ke, Tan Chong, Liu Hong, Zheng Min. Data fusion algorithm for wireless sensor networks based on improved grey wolf algorithm with optimized BP neural network. Journal of the University of Chinese Academy of Sciences,2022,39(02):232–239. (In Chinese) Ye Jin, Xu Feng, Yang Juan, Wang Jiawei. A multi-sensor-based composite measurement IMM-EKF data fusion algorithm. Journal of Electronics,2020,48(12):2326–2330. (In Chinese) WANG Hong, XU Youyu, TAN Chong, LIU Hong, ZHENG Min. Improved particle swarm-based BP neural network WSN data fusion algorithm. Journal of the University of Chinese Academy of Sciences,2020,37(05):673–680. (In Chinese)

5. Collett C. Belief based reinforcement learning for data fusion. New York: State University of New York at Buffalo, 2009.