Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

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.

Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems

“…It is believed that iFogSim [40] is the most effective tool for simulating fog computingenabled applications. Several researchers [31,32,36,29,37,30] also simulated their proposed architectures in iFogSim.…”

“…Hassan et.al. [32] proposed three-layer remote pain monitoring system architecture where the fog node detects the pain with the help of the digital signal processing techniques. Compared with cloud-based implementation, the proposed approach resulted in low latency.…”

Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition

“…A smart home is a promising technology for elderly care, which can be realized by Internet of things (IoT) , to improve the quality of life of the elderly, prevent and detect their accidents, and enable the elderly to live in a safe and comfortable environment [ 1 , 2 , 3 , 4 ]. As the number of elderly people is increasing, Ambient Assistant Living (AAL) [ 5 , 6 ] has been fully applied for home care.…”

Timely Reliability Analysis of Virtual Machines Considering Migration and Recovery in an Edge Server

“…To satisfy the strict delay requirements of such IoT services, edge computing emerged. Edge computing provides computing and storage resources at the edge of the network, so that latency-sensitive data and private data can be handled nearby the users, such as applications, e.g., driverless cars, real-time traffic management, virtual reality (VR), augmented reality (AR), and healthcare IoT [ 1 , 2 ]. Regardless of the application scenario, the success of the task relies on the powerful and reliable processing capability of edge computing servers, especially for latency-sensitive applications.…”