Abstract
The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.
Highlights
There are to be about 237.1 million wearable body devices available on the market by 2020 with an estimated reach of market share related to the healthcare industry of USD 117 billion by 2020 [1].The data flow by healthcare applications based on such a large number of bio-sensors is approximatedSensors 2020, 20, 6574; doi:10.3390/s20226574 www.mdpi.com/journal/sensorsSensors 2020, 20, 6574 to be 507.5 zettabytes [2]
The cloud server has to process the signals sent by all the sensors so, subsequently, the latency in the cloud increases with an increase in the number of sensors
The results of the simulations performed on different scales validate the effectiveness of the proposed architecture for the implementation of remote pain monitoring applications as compared to the cloud
Summary
There are to be about 237.1 million wearable body devices available on the market by 2020 with an estimated reach of market share related to the healthcare industry of USD 117 billion by 2020 [1]. Several researchers have proposed remote pain monitoring systems using cloud computing and IoT devices. High latency and network usage are the key factors limiting the large-scale implementation of cloud-based remote pain monitoring systems. The cloud server receives biopotential signals from sensors and after processing and displays pain-related information in a web application for real-time monitoring. Connecting sensor nodes directly to the cloud server results in a long delay, which is not suitable for such time-sensitive healthcare applications. The proposed architecture reduces these factors, making the proposed system most suitable for health-related applications It ensures real-time monitoring of patients and rapid medical assistance provisioning by minimizing the time spent from pain detection to display in the web application.
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