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Title: SMS : a secure healthcare model for smart cities
Authors: Tripathi, Gautami
Ahad, Mohd Abdul
Paiva, Sara
Keywords: Healthcare
Mobile edge computing
Issue Date: Jul-2020
Citation: Tripathi, G., Ahad, M. A., & Paiva, S. (2020). SMS: a secure healthcare model for smart cities. Electronics, 9(7), 1135.
Abstract: Technological innovations have enabled the realization of a utopian world where all objects of everydaylife, as well as humans, areinterconnected to form an “Internet of Things (IoT).” These connected technologies and IoT solutions have led to the emergence of smart cities where all components are converted into a connected smart ecosystem. IoT has envisioned several areas of smart cities including the modern healthcare environment like real-time monitoring, patient information management, ambient-assisted living, ambient-intelligence, anomaly detection, and accelerated sensing. IoT has also brought a breakthrough in the medical domain by integrating stake holders, medical components, and hospitals to bring about holistic healthcare management. The healthcare domain is already witnessing promising IoT-based solutions ranging from embedded mobile applications to wearable devices and implantable gadgets. However, with all these exemplary benefits, there is a need to ensure the safety and privacy of the patient’s personal and medical data communicated to and from the connected devices and systems. For a smart city, it is pertinent to have an accessible, effective, and secure healthcare system for its inhabitants. This paper discusses the various elements of technology-enabled healthcare and presents a privacy-preserved and secure “Smart Medical System (SMS)” framework for the smart city ecosystem. For providing real-time analysis and responses, this paper proposes to use the concept of secured Mobile Edge Computing (MEC) for performing critical time-bound computations on the edge itself. In order to protect the medical and personal data of the patients and to make the data tamper-proof, the concept of blockchain has been used. Finally, this paper highlights the ways to capture and store the medical big data generated from IoT devices and sensors.
ISSN: 2079-9292
Appears in Collections:ESTG - Artigos indexados à WoS/Scopus

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