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      Designing a Healthcare-Enabled Software-Defined Wireless Body Area Network Architecture for Secure Medical Data and Efficient Diagnosis

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          Abstract

          In the struggle against population aging, chronic diseases, and a lack of medical facilities, the emergence of Wireless Body Area Networks (WBANs) technology has ushered in optimism. WBANs use a variety of wearable and implanted biosensor nodes to constantly monitor physiological parameters such as oxygen saturation (SpO2), electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), blood pressure, respiration rate, body temperature, and pulse rate. Importantly, these vital signs are communicated to a doctor over a public network, who can diagnose ailments remotely and efficiently. Among these communications, the security and privacy of patients are the prime concerns while transferring data over an open wireless channel from biosensor nodes to a Medical Server (MS) through a Base Station (BS) for efficient medical diagnosis. Finding an effective security strategy for patients which rely on WBANs to monitor their health information is a huge challenge due to the confined nature of the WBANs environment. To tackle the above challenges, in this research, a new, efficient, and secure healthcare-enabled software-defined WBANs architecture based on Schnorr signcryption and Hyperelliptic Curve Cryptography (HECC) is suggested in which the SDN technology is integrated into WBANs. By separating the control and data planes in an efferent manner, SDN technology allows you to control and manage the network in a programmable manner. The main features of SDN, such as its programmability, flexibility, and centralized control, make it a simple and scalable network. In this research, first, a Software-Defined Wireless Body Area Networks (SD-WBANs) architecture has been designed, and then a lightweight Schnorr signcryption with Hyperelliptic Curve Cryptography (HECC) has been proposed to preserve sensitive patient data security during transmission on public networks. Moreover, a well-known Multicriteria Decision-Making (MCDM) approach known as Evaluation Based on Distance from Average Solution (EDAS) is also used to demonstrate the success of the suggested system. According to the performance analysis, the suggested approach beats previous state-of-the-art techniques in terms of computation cost, communication overhead, storage cost, and energy usage.

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          Most cited references36

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          Software-Defined Networking: A Comprehensive Survey

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            Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS)

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              Extended EDAS Method for Fuzzy Multi-criteria Decision-making: An Application to Supplier Selection

              In the real-world problems, we are likely confronted with some alternatives that eed to be evaluated with respect to multiple conflicting criteria. Multi-criteria ecision-making (MCDM) refers to making decisions in such a situation. There are any methods and techniques available for solving MCDM problems. The evaluation ased on distance from average solution (EDAS) method is an efficient multi-criteria ecision-making method. Because the uncertainty is usually an inevitable part of he MCDM problems, fuzzy MCDM methods can be very useful for dealing with the eal-world decision-making problems. In this study, we extend the EDAS method o handle the MCDM problems in the fuzzy environment. A case study of supplier election is used to show the procedure of the proposed method and applicability of t. Also, we perform a sensitivity analysis by using simulated weights for criteria to xamine the stability and validity of the results of the proposed method. The results f this study show that the extended fuzzy EDAS method is efficient and has good tability for solving MCDM problems.
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                Author and article information

                Contributors
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                Journal
                Journal of Healthcare Engineering
                Journal of Healthcare Engineering
                Hindawi Limited
                2040-2309
                2040-2295
                February 4 2022
                February 4 2022
                : 2022
                : 1-19
                Affiliations
                [1 ]Department of Computer Science Capital University of Science and Technology, Islamabad, Pakistan
                [2 ]Department of Information Technology Hazara University, Mansehra, Pakistan
                [3 ]Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
                [4 ]School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Brunei Darussalam
                [5 ]Department of Electrical and Computer Engineering, Villanova University, Villanova, PA 19085, USA
                [6 ]Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen
                Article
                10.1155/2022/9210761
                387bff19-def6-41b4-ba1f-a240bb936d87
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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