DIWGAN-WBSN: A novel health monitoring approach for wireless body sensor networks

被引:0
|
作者
Jayasutha, D. [1 ]
Hemamalini, V. [2 ]
Sangeetha, S. [3 ]
Yeruva, Ajay Reddy [4 ]
机构
[1] Hindusthan Inst Technol, Dept Comp Sci & Engn, Coimbatore 641032, Tamil Nadu, India
[2] SRM Inst Sci & Technol, Dept Networking & Commun, Chennai, India
[3] Karpagam Coll Engn, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India
[4] 5744 Owens Dr, Pleasanton, CA USA
关键词
health monitoring; volcano eruption algorithm; war strategy optimization; wireless body sensor networks; OPTIMIZATION; ALGORITHM; SYSTEM;
D O I
10.1002/dac.5934
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low-cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN-WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network-based risk detection in WBSN for health monitoring, random forest algorithm-based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively. In this manuscript, a Novel Health Monitoring Approach for Wireless Body Sensor Networks (DIWGAN-WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network optimized with War Strategy Optimization Algorithm. After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. image
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页数:17
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