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

被引:38
作者
Hassan, Syed Rizwan [1 ]
Ahmad, Ishtiaq [1 ]
Ahmad, Shafiq [2 ]
Alfaify, Abdullah [2 ]
Shafiq, Muhammad [3 ]
机构
[1] Univ Lahore, Dept Elect Engn, Lahore 54000, Pakistan
[2] King Saud Univ, Coll Engn, Dept Ind Engn, POB 800, Riyadh 11421, Saudi Arabia
[3] Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan 38541, South Korea
关键词
fog computing; cloud computing; remote pain monitoring; e-healthcare; IoT; INTERNET; THINGS; CLOUD; EDGE;
D O I
10.3390/s20226574
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页码:1 / 21
页数:21
相关论文
共 62 条
  • [31] An Online Optimization Framework for Distributed Fog Network Formation With Minimal Latency
    Lee, Gilsoo
    Saad, Walid
    Bennis, Mehdi
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2019, 18 (04) : 2244 - 2258
  • [32] A Framework of Fog Computing: Architecture, Challenges, and Optimization
    Liu, Yang
    Fieldsend, Jonathan E.
    Min, Geyong
    [J]. IEEE ACCESS, 2017, 5 : 25445 - 25454
  • [33] Automatically Detecting Pain in Video Through Facial Action Units
    Lucey, Patrick
    Cohn, Jeffrey F.
    Matthews, Iain
    Lucey, Simon
    Sridharan, Sridha
    Howlett, Jessica
    Prkachin, Kenneth M.
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2011, 41 (03): : 664 - 674
  • [34] An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks
    Ma, Mimi
    He, Debiao
    Wang, Huaqun
    Kumar, Neeraj
    Choo, Kim-Kwang Raymond
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (05) : 8065 - 8075
  • [35] Mobility aware autonomic approach for the migration of application modules in fog computing environment
    Martin, John Paul
    Kandasamy, A.
    Chandrasekaran, K.
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 11 (11) : 5259 - 5278
  • [36] UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities
    Muhammed, Thaha
    Mehmood, Rashid
    Albeshri, Aiiad
    Katib, Iyad
    [J]. IEEE ACCESS, 2018, 6 : 32258 - 32285
  • [37] Context-Aware, Accurate, and Real Time Fall Detection System for Elderly People
    Muheidat, Fadi
    Tawalbeh, Lo'ai
    Tyrer, Harry
    [J]. 2018 IEEE 12TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC), 2018, : 329 - 333
  • [38] Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges
    Mukherjee, Mithun
    Shu, Lei
    Wang, Di
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (03): : 1826 - 1857
  • [39] Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain
    Muthanna, Ammar
    Ateya, Abdelhamied A.
    Khakimov, Abdukodir
    Gudkova, Irina
    Abuarqoub, Abdelrahman
    Samouylov, Konstantin
    Koucheryavy, Andrey
    [J]. JOURNAL OF SENSOR AND ACTUATOR NETWORKS, 2019, 8 (01)
  • [40] Enabling technologies for fog computing in healthcare IoT systems
    Mutlag, Ammar Awad
    Abd Ghani, Mohd Khanapi
    Arunkumar, N.
    Mohammed, Mazin Abed
    Mohd, Othman
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 90 : 62 - 78