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An Effective Signcryption with Optimization Algorithm for IoT-enabled Secure Data Transmission

A. Chinnappa*, C. Vijayakumaran

Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamilnadu, India

* Corresponding Author: A. Chinnappa. Email: email

Computers, Materials & Continua 2022, 73(2), 4017-4031. https://doi.org/10.32604/cmc.2022.027858

Abstract

Internet of Things (IoT) allows several low resources and controlled devices to interconnect, calculate processes and make decisions in the communication network. In the heterogeneous environment for IoT devices, several challenging issues such as energy, storage, efficiency, and security. The design of encryption techniques enables the transmission of the data in the IoT environment in a secured way. The proper selection of optimal keys helps to boost the encryption performance. With this motivation, the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission (SCQCKH-SDT) in IoT environment. The proposed SCQCKH-SDT technique aims to effectively encrypts the data by the use of optimal keys generated by the CQKH algorithm. The proposed SCQCKH-SDT technique initially employs the signcryption technique for the encryption of data. In order to optimize the secrecy, the optimal key generation process is carried out using Chaotic Krill Herd (CQKH) algorithm. The CQKH algorithm incorporates the concept of quantum computing and chaotic theory into the traditional KH algorithm. The performance validation of the SCQCKH-SDT technique is performed using benchmark dataset. An extensive comparative analysis reported the superior performance of the SCQCKH-SDT technique over the recent approaches.

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Cite This Article

A. Chinnappa and C. Vijayakumaran, "An effective signcryption with optimization algorithm for iot-enabled secure data transmission," Computers, Materials & Continua, vol. 73, no.2, pp. 4017–4031, 2022. https://doi.org/10.32604/cmc.2022.027858



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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