research-article

A 3-Tier Architecture for Network Latency Reduction in Healthcare Internet-of-Things Using Fog Computing and Machine Learning

Authors:

Saurabh Shukla,

Mohd Fadzil Hassan,

Muhammad Khalid KhanAuthors Info & Claims

ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer Applications

Pages 522 - 528

https://doi.org/10.1145/3316615.3318222

Published: 19 February 2019 Publication History

Abstract

Healthcare Internet-of-things comprises a huge number of wearable sensors and interconnected computers. The high volume of IoT data is transacted over servers leading to servers overloading with high traffic causing network congestion. These cloud servers are typically for analyzing, retrieving and storing the large data generated from IoT devices. There exist challenges regarding sending real-time healthcare data from cloud servers to end-users. These challenges include the high computational latency, high communication latency, and high network latency. Due to these challenges, IoTs may not be able to send data in real-time to end-users. Fog nodes can be used to play a major role in reducing the high delay and high traffic. It can be a solution to increase system performance. In this paper, we proposed a 3-tier architecture, an analytical model for healthcare IoT using a hybrid approach consisting of fuzzy logic and reinforcement learning in a fog computing environment. The aim is to minimize network latency. The proposed model and 3-tier architecture are simulated using iFogSim simulator.

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Cited By

Villar EMartín Toral ICalvo IBarambones OFernández-Bustamante P(2024)Architectures for Industrial AIoT ApplicationsSensors10.3390/s2415492924:15(4929)Online publication date: 30-Jul-2024
https://doi.org/10.3390/s24154929
Mishra S(2024)Growing Importance of Machine Learning in Healthcare to Determine Potential RiskPrediction in Medicine: The Impact of Machine Learning on Healthcare10.2174/9789815305128124010011(136-158)Online publication date: 10-Oct-2024
https://doi.org/10.2174/9789815305128124010011
Sharief FIjaz HShojafar MNaeem M(2024)Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/368962757:1(1-48)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3689627
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Index Terms

A 3-Tier Architecture for Network Latency Reduction in Healthcare Internet-of-Things Using Fog Computing and Machine Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Markov decision processes

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ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer Applications

February 2019

611 pages

ISBN:9781450365734

DOI:10.1145/3316615

Copyright © 2019 ACM.

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University of New Brunswick: University of New Brunswick

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Published: 19 February 2019

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ICSCA '19

ICSCA '19: 2019 8th International Conference on Software and Computer Applications

February 19 - 21, 2019

Penang, Malaysia

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Cited By

Villar EMartín Toral ICalvo IBarambones OFernández-Bustamante P(2024)Architectures for Industrial AIoT ApplicationsSensors10.3390/s2415492924:15(4929)Online publication date: 30-Jul-2024
https://doi.org/10.3390/s24154929
Mishra S(2024)Growing Importance of Machine Learning in Healthcare to Determine Potential RiskPrediction in Medicine: The Impact of Machine Learning on Healthcare10.2174/9789815305128124010011(136-158)Online publication date: 10-Oct-2024
https://doi.org/10.2174/9789815305128124010011
Sharief FIjaz HShojafar MNaeem M(2024)Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/368962757:1(1-48)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3689627
Mohan JRanganathan PReza H(2024)Hierarchical Software Framework for Safe Unmanned Aerial Systems Integration into National Airspace (NAS)2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC60891.2024.10427776(0198-0206)Online publication date: 8-Jan-2024
https://doi.org/10.1109/CCWC60891.2024.10427776
Alturki BAbu Al-Haija QAlsemmeari RAlsulami AAlqahtani AAlghamdi BBakhsh SShaikh R(2024)IoMT landscape: navigating current challenges and pioneering future research trendsDiscover Applied Sciences10.1007/s42452-024-06351-w7:1Online publication date: 18-Dec-2024
https://doi.org/10.1007/s42452-024-06351-w
Kumhar MBhatia J(2023)Software‐defined networks‐enabled fog computing for IoT‐based healthcareSecurity and Privacy10.1002/spy2.2916:5Online publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1002/spy2.291
S ARamesh KS SSheema DB P(2022)Internet of Health Things: A Fog computing Paradigm2022 6th International Conference on Trends in Electronics and Informatics (ICOEI)10.1109/ICOEI53556.2022.9776777(598-604)Online publication date: 28-Apr-2022
https://doi.org/10.1109/ICOEI53556.2022.9776777
Al-Araji ZAhmad SKausar NFarhani AOzbilge ECagin T(2022)Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open IssuesIEEE Access10.1109/ACCESS.2022.322546210(126931-126956)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3225462
Mishra NPandya SPatel CCholli NModi KShah PChopade MPatel SKotecha K(2021)Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT ApplicationsSensors10.3390/s2123807121:23(8071)Online publication date: 2-Dec-2021
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Maiti PSahoo BTuruk A(2021)Low Latency Aware Fog Nodes Placement in Internet of Things Service InfrastructureJournal of Circuits, Systems and Computers10.1142/S021812662250017731:01Online publication date: 9-Sep-2021
https://doi.org/10.1142/S0218126622500177
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