Mist Data: Leveraging Mist Computing for Secure and Scalable Architecture for Smart and Connected Health

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Abstract

The smart health paradigms employ Internet-connected wearables for tele-monitoring, diagnosis providing inexpensive healthcare solutions. Mist computing reduces latency and increases throughput by processing data near the edge of the network. In the present paper, we proposed a secure mist Computing architecture that is validated on recently released public geospatial health dataset. Results and discussion support the efficacy of proposed architecture for smart geospatial health applications. The present research paper proposed SoA-Mist i.e. a three-tier secure framework for efficient management of geospatial health data with the use of mist devices. It proposed the security aspects in client layer, mist layer, fog layer and cloud layer. It has defined the prototype development by using win-win spiral model with use case and sequence diagram. Overlay analysis has been performed with the developed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014 in mobile clients as test case. Finally, It concludes with the comparison analysis of cloud based framework and proposed SoA-Mist framework. © 2018 The Authors. Published by Elsevier B.V.

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Keywords

Big data, Computer network architectures, Computer architecture, Computer networks--Scalability

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Rights

CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives), ©2018 The Authors

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