AQUILA AFRICAN VULTURE OPTIMIZED FUZZY DEEP BELIEF NETWORK FOR SECURE DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS
DOI:
https://doi.org/10.59277/RRST-EE.2025.2.16Keywords:
Aggregator, Aquilla optimizerAbstract
A wireless sensor network (WSN) comprises several individual sensor nodes (SNs) that can perceive, analyze, and interact with data. Energy constraints and security are widely acknowledged as the two most challenging problems with WSNs. To overcome these drawbacks, a novel African aquila-optimized fuzzy deep belief network (AAVO-FDBN) framework is introduced in this paper. The AAVO model chooses the most reliable aggregator node based on ideal node selection criteria. After selecting the aggregator node, the cluster head (CH) data will be encrypted using the novel Crystal Kyber encryption (CKE) technique. An optimal routing path is established using a fuzzy deep belief network (Fuzzy-DBN), which considers the network lifespan, aggregate degree, aggregate coverage, and the distance between the aggregate and the sink. By using the NS2 simulator, we evaluate the suggested architecture based on parameters such as network lifetime (NL), energy consumption (EC), packet delivery ratio (PDR), and end-to-end delay (E2ED). According to experimental findings, AAVO-FDBN outperforms SEPC, REDAA, and SCDAP in terms of NL, with improvements of 22.80%, 12.50%, and 17.65%, respectively. The proposed AAVO-FDBN approach is more efficient and secure for real-time applications.
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