A Dynamic Step-wise Tiny Encryption Algorithm with Fruit Fly Optimization for Quality of Service improvement in healthcare
Healthcare Analytics, ISSN: 2772-4425, Vol: 3, Page: 100177
2023
- 27Citations
- 119Captures
- 1Mentions
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Article Description
Healthcare highly depends on accessing, processing, analyzing, sharing, and storing electronic data. Cloud computing has evolved in various healthcare fields, such as bioinformatics, health management systems, and telemedicine. The Internet of Medical Things (IoMT), a subset of IoT, is an essential element of a healthcare management system. The expeditious development in IoMT, intelligent medical devices, healthcare applications, and high-speed networks has enabled medical systems to efficiently diagnose, treat, manage diseases, and remotely monitor and improve the Quality of Service (QoS). To achieve this high data, high speed network, longer battery life and reliable connectivity are basic requirements. 5G conformed these needs with its numerous and distinct potential. However, the shortcomings of these medical devices in the IoMT are privacy concerns and direct physical threats. We propose a new algorithm to enhance the QoS of the IoMT devices/systems. Initially, the dataset is collected and the 5G network is operationalized. We propose a Dynamic Step-wise Tiny Encryption Algorithm with Fruit Fly Optimization Algorithm to encrypt data. Encrypted data is transmitted using the Energy Efficient Routing Protocol. When the user requests to access the data, the data is decrypted. We show that the proposed system has higher QoS attributes and is more effective. Our experimental results show that our proposed solution considerably improves QoS and network performance in terms of lifetime, communication overhead, storage footprint, and power consumption compared to the competing methods.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2772442523000448; http://dx.doi.org/10.1016/j.health.2023.100177; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85153594018&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2772442523000448; https://dx.doi.org/10.1016/j.health.2023.100177
Elsevier BV
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