A dynamic preserving-based WSN framework for medical disease prediction. (14th June 2022)

• Record Type: Journal Article
• Authors: Madhuravani, B.; Murthy, D.S.R.; Raju, S. Viswanadha
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 40:Number 1/3(2022)
• Page Start: 10
• Page End: 19
• Publication Date: 2022-06-14
• Subjects: machine learning -- medical disease prediction -- sensor network; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

Wireless sensor networks (WSNs) play a vital role in the real-time data communication process. Cloud-based WSNs is used to improve the processing speed and the storage capacity in the real-time applications. Most of the conventional approaches are based on sensor resources with limited cloud services due to high computational cost. Also, these models are not efficient in processing large volumes of data in real-time applications due to computational memory and time. In order to improve these limitations, a hybrid machine learning-based sensor network is developed to predict the medical disease patterns using the cloud servers. In this work, a hybrid PSO, support vector machine and sensor security algorithms are implemented on the real-time medical sensor devices. Experimental results show that the machine learning-based sensor framework has better efficiency in terms of sensor processing time, accuracy and error rate than the conventional approaches.