Multibiosensor Data Sampling and Transmission Reduction With Decision-Making for Remote Patient Monitoring in IoMT Networks

Abstract

The rise in chronic diseases and the aging of the population led to an increase in the demand for remote healthcare systems that employ biosensors to monitor people’s health status. The increasing need for these automated systems has led to the emergence of the Internet of Medical Things (IoMT) networks. In the IoMT networks, the biosensor devices collect vital signs and transmit them to the gateway for further analysis and fusion. In light of the limited biosensor device resources (power, storage, and computation) and the periodical transmission of a large amount of data, it is necessary to optimize the transmission of data in order to conserve power while maintaining data quality at the gateway. Also, it became important to have a decision-making-based machine learning model at the gateway to evaluate a patient’s health and make a quick, accurate decision in case of an emergency. This paper proposes Multibiosensor Data Sampling and Transmission Reduction with Decision-making (MuDaSaTReD) for Remote Patient Monitoring in the IoMTs Networks. The MuDaSaTReD achieves this goal on two levels: biosensors and a fog gateway. It uses an Energy-saving Lightweight Data Transmission (ELiDaT) algorithm to get rid of the repeated data and then adapts the sampling rate of each biosensor using an Adaptive Data Sampling (ADaS) algorithm. The MuDaFuDeC implements the machine learning model at the fog gateway to learn and decide the situation of the patient according to the received data from the biosensors. The performance evaluation shows that the MuDaFuDeC outperforms other approaches in terms of the data reduction percentage and energy consumption. It keeps a good representation of all the scores at the fog gateway and makes automated, fast, and accurate decisions based on the patient’s condition.