Self-attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN |
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Authors: | S Soundararajan B R Tapas Bapu S Sargunavathi I Poonguzhali |
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Affiliation: | 1. Department of Computer Science and Engineering, Velammal Institute of Technology, Chennai, Tamil Nadu, India;2. Faculty of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India;3. Department of Electronics and Communication Engineering, Sriram Engineering College, Thiruvallur District, India;4. Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India |
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Abstract: | WSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self-attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi-Objective Cluster Head Selection and Energy Aware Routing (SabGAN-AqOA-EgAwR-WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN-AqOA-EgAwR-WSN method is activated in NS2 simulator. Finally, the proposed SabGAN-AqOA-EgAwR-WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods. |
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Keywords: | Aquila optimization algorithm (AqOA) self-attention based generative adversarial network (SabGAN) wireless sensor networks |
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