A dynamic preserving-based WSN framework for medical disease prediction. (14th June 2022) - Madhuravani, B.; Murthy, D.S.R.; Raju, S. Viswanadha
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.