Search

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 article proposes multibiosensor data sampling and transmission reduction with decision-making (MuDaSaTReD) for remote patient monitoring in the IoMT 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 Multibiosensor Data Sampling and Transmission Reduction with Decision-making (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.

The rapid evolution of the Internet of Things (IoT) leads to emerging several applications such as smart home, ehealth, smart agriculture and farming, etc. The edge of the IoT networks will receive a huge amount of data generated by these smart applications which regularly require to be delivered to the servers of the remote data centers for additional real-time treatment. However, transmitting the whole of these IoT data across the network toward the data center of the cloud will impose a high overhead on the IoT network. The data exchange and processing long delay have a significant impact on the response speed of the real-time IoT applications. It can reduce the time responsiveness of these applications. The fog computing has provided for the IoT applications as an intermediate layer between the smart end devices and the cloud to reduce IoT data and improve the response time for IoT applications. In this paper, new fog computing enabled lossless EEG data compression scheme is proposed to minimize the amount of IoT EEG data uploaded to the cloud. The EEG data compression scheme consists of two phases: clustering and compression. First, the received data is clustered into clusters using agglomerative hierarchical clustering. The Huffman encoding is applied to each resulted cluster in the second phase. Finally, the compressed files of smaller clusters are combined and uploaded from fog to cloud. Several experiments have been implemented and the comparison results show that the suggested combined Hierarchical Clustering and Huffman Encoding (HCHE) method significantly decreases the volume of EEG data uploaded to the cloud platform. The proposed HCHE method achieves 4.33 of average compression power that is more than twice as much the compression power of some existing methods.