Real-time health monitoring in WBANs using hybrid Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP)

被引:13
|
作者
Aryai, Pouya [1 ]
Khademzadeh, Ahmad [2 ]
Jassbi, Somayyeh Jafarali [1 ,3 ]
Hosseinzadeh, Mehdi [3 ,4 ]
Hashemzadeh, Omid [5 ]
Shokouhifar, Mohammad [6 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Sci & Res Branch, Tehran 1477893855, Iran
[2] ITRC, ICT Res Inst, Tehran 1439955471, Iran
[3] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[4] Univ Human Dev, Comp Sci, Sulaymaniyah 07786, Iraq
[5] Islamic Azad Univ, Dept Business Management, Sci & Res Branch, Tehran 1477893855, Iran
[6] Shahid Beheshti Univ, Dept Elect & Comp Engn, Tehran 1983969411, Iran
关键词
Wireless body area networks; Health monitoring; Adaptive real-time routing; Machine learning; Support vector regression; Whale optimization algorithm; ENERGY-EFFICIENT; CLUSTERING-ALGORITHM; WIRELESS; NETWORKS;
D O I
10.1016/j.aeue.2023.154723
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 area networks (WBANs) are helpful for remote health monitoring, especially during the COVID-19 pandemic. Due to the limited batteries of bio-sensors, energy-efficient routing is vital to achieve load-balancing and prolong the network's lifetime. Although many routing techniques have been presented for WBANs, they were designed for an application, and their performance may be degraded in other applications. In this paper, an ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP) is introduced as an adaptive real-time remote health monitoring in WBANs. The motivation behind this technique is to utilize the superior route optimization solutions offered by metaheuristics and to integrate them with the real-time routing capability of machine learning. The proposed method involves two phases: offline model tuning and online routing. During the offline pre-processing step, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is used to optimize routes across various WBAN configurations. By applying WOA for multiple WBANs, a comprehensive dataset is generated. This dataset is then used to train and test a machine learning regressor that is based on support vector regression (SVR). Next, the optimized MDML-RP model is applied as an adaptive real-time protocol, which can efficiently respond to just-in-time requests in new, previously unseen WBANs. Simu-lation results in various WBANs demonstrate the superiority of the MDML-RP model in terms of application-specific performance measures when compared with the existing heuristic, metaheuristic, and machine learning protocols. The findings indicate that the proposed MDML-RP model achieves noteworthy improvement rates across various performance metrics when compared to the existing techniques, with an average improvement of 42.3% for the network lifetime, 15.4% for reliability, 31.3% for path loss, and 31.7% for hot-spot temperature.
引用
收藏
页数:15
相关论文
共 50 条
  • [11] Real-Time Structure Generation Based on Data-Driven Using Machine Learning
    Wang, Ying
    Shi, Feifei
    Chen, Bingbing
    PROCESSES, 2023, 11 (03)
  • [12] Real-Time Healthcare Monitoring System using Online Machine Learning and Spark Streaming
    Hassan, Fawzya
    Shaheen, Masoud E.
    Sahal, Radhya
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (09) : 650 - 658
  • [13] Real-time machine learning for in situ quality control in hybrid manufacturing: a data-driven approach
    Mavaluru, Dinesh
    Tipparti, Akanksha
    Tipparti, Anil Kumar
    Ameenuddin, Mohammed
    Ramakrishnan, Jayabrabu
    Samrin, Rafath
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2025,
  • [14] Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data
    Entezami, Alireza
    Arslan, Ali Nadir
    De Michele, Carlo
    Behkamal, Bahareh
    REMOTE SENSING, 2022, 14 (14)
  • [15] Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization
    Kim, Junghyun
    Justin, Cedric
    Mavris, Dimitri
    Briceno, Simon
    JOURNAL OF AEROSPACE INFORMATION SYSTEMS, 2021, 19 (01): : 3 - 21
  • [16] Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning
    Mishra, Priya
    Boopal, Balaji
    Mishra, Naveen
    EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, 2024, 11 (06): : 1 - 8
  • [17] Intelligent Detection and Real-time Monitoring of Engine Oil Aeration Using a Machine Learning Model
    Kulkarni, Vainatey
    Han, Xiaoye
    Tjong, Jimi
    APPLIED ARTIFICIAL INTELLIGENCE, 2021, 35 (15) : 1869 - 1886
  • [18] Real-time monitoring of fat crystallization using pulsed acoustic spectroscopy and supervised machine learning
    Metilli, Lorenzo
    Morris, Liam
    Lazidis, Aris
    Marty-Terrade, Stephanie
    Holmes, Melvin
    Povey, Megan
    Simone, Elena
    JOURNAL OF FOOD ENGINEERING, 2022, 335
  • [19] Real-Time Power Consumption Monitoring and Forecasting Using Regression Techniques and Machine Learning Algorithms
    Arce, Jose Mari M.
    Macabebe, Erees Queen B.
    2019 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND INTELLIGENCE SYSTEM (IOTAIS), 2019, : 135 - 140
  • [20] Condition monitoring of axial piston pumps based on machine learning-driven real-time CFD simulation
    Wang, Wentao
    Chao, Qun
    Shi, Junjie
    Liu, Chengliang
    ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS, 2025, 19 (01)