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Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

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Handbook of Large-Scale Distributed Computing in Smart Healthcare

Part of the book series: Scalable Computing and Communications ((SCC))

Abstract

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: (1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; (2) The data, when communicated, are vulnerable to security and privacy issues; (3) The communication of the continuously collected data is not only costly but also energy hungry; (4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.

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Acknowledgements

Authors would like to thank the patients with Parkinson’s disease for their co-operation during validation studies reported in this chapter. This work was supported by a grant (No: 20144261) from Rhode Island Foundation Medical Research and NSF grants CCF-1421823, CCF-1439011 and NSF CAREER CPS 1652538. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Rhode Island Foundation Medical Research. Authors would like to thank Alyssa Zisk for proofreading the manuscript. Authors would like to thank Manob Saikia, and Dr. Amir Mohammad Amiri for helpful discussions and suggestions for preparation of this chapter.

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Authors and Affiliations

  1. Center for Robust Speech Systems, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX, 75080, USA

    Harishchandra Dubey

  2. Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA

    Admir Monteiro, Nicholas Constant, Mohammadreza Abtahi, Debanjan Borthakur, Yan Sun, Qing Yang & Kunal Mankodiya

  3. Department of Communicative Disorders, University of Rhode Island, Kingston, RI, USA

    Leslie Mahler

  4. Wearable Biosensing Lab, University of Rhode Island, Kingston, RI, USA

    Admir Monteiro, Nicholas Constant, Mohammadreza Abtahi, Debanjan Borthakur & Kunal Mankodiya

  5. Movement Disorders Program, Rhode Island Hospital, Providence, RI, USA

    Umer Akbar

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  1. Harishchandra Dubey
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Correspondence to Harishchandra Dubey .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, North Dakota State University , Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Assad Abbas

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Dubey, H. et al. (2017). Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications. In: Khan, S., Zomaya, A., Abbas, A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-58280-1_11

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  • Online ISBN: 978-3-319-58280-1

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