Improving the Security and Confidentiality in the Internet of Medical Things Based on Edge Computing Using Clustering
Families, physicians, and hospital environments use remote patient monitoring (RPM) technologies to remotely monitor a patient’s vital signs, reduce visit time, reduce hospital costs, and improve the quality of care. The Internet of Medical Things (IoMT) is provided by applications that provide remo...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi Limited
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/2479500be54f479a8a462a546f661dfc |
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| Sumario: | Families, physicians, and hospital environments use remote patient monitoring (RPM) technologies to remotely monitor a patient’s vital signs, reduce visit time, reduce hospital costs, and improve the quality of care. The Internet of Medical Things (IoMT) is provided by applications that provide remote access to patient’s physiological data. The Internet of Medical Things (IoMT) tools basically have a user interface, biosensor, and Internet connectivity. Accordingly, it is possible to record, transfer, store, and process medical data in a short time by integrating IoMT with the data communication infrastructure in edge computing. (Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. A common misconception is that edge and IoT are synonymous.) But, this approach faces problems with security and intrusion into users’ medical data that are confidential. Accordingly, this study presents a secure solution in order to be used in the IoT infrastructure in edge computing. In the proposed method, first the clustering process is performed effectively using information about the characteristics and interests of users. Then, the people in each cluster evaluated by using edge computing and people with higher scores are considered as influential people in their cluster, and since users with high user interaction can publish information on a large scale, it can be concluded that, by increasing user interaction, information can be disseminated on a larger scale without any intrusion and thus in a safe way in the network. In the proposed method, the average of user interactions and user scores are used as a criterion for identifying influential people in each cluster. If there is a desired number of people who are considered to start disseminating information, it is possible to select people in each cluster with a higher degree of influence to start disseminating information. According to the research results, the accuracy has increased by 0.2 and more information is published in the proposed method than the previous methods. |
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