Energy-efficient multisensor adaptive sampling and aggregation for patient monitoring in edge computing based IoHT networks

The need for remote healthcare monitoring systems that utilize limited resources' biosensors is growing. These biosensors increase the amount of transmitted data across the Internet of Healthcare Things (IoHT) network. Therefore, it is necessary to decrease the transmitted data and make a decision at the edge gateway to save the energy of the biosensors and produce a quick response for the medical staff. This paper proposes an energy-efficient multisensor adaptive sampling and aggregation (EMASA) for patient monitoring in edge computing-based IoHT networks. In the edge-based IoHT network, EMASA operates on two levels: biosensors and the edge gateway. Each biosensor removes the redundant sensed data using the local emergency detection and sampling rate adaptation algorithms. In the edge gateway, it implements a machine learning-based Support Vector Machine (SVM) model to provide a suitable decision about the status of the monitored patient. We accomplished various examinations using real data from the patients' biosensors. According to the simulation results, EMASA reduced the size of transmitted data from 93.5% to 99% and saved 78.35% of energy when compared to a previous study. It keeps the whole score with a good representation at the Edge gateway and provides accurate and fast decisions based on the patient's condition.