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Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN.

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Title: Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN.
Authors: Jenice Prabhu, A.1 (AUTHOR) Jeniceprabhu@gmail.com, Ahilan, A.2 (AUTHOR) akhilanappathurai@psncet.ac.in, Vijayaraj, Alwarsamy3 (AUTHOR) satturvijay@gmail.com, Gururama Senthilvel, P.4 (AUTHOR) gururamasenthilvelp.sse@saveetha.com
Source: IETE Journal of Research. Nov2024, Vol. 70 Issue 11, p8018-8030. 13p.
Subjects: Wireless sensor network security, Optimization algorithms, Energy consumption, Energy security, Detectors
Abstract: A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications. [ABSTRACT FROM AUTHOR]
ISSN:03772063
DOI:10.1080/03772063.2024.2369722