Energy-Aware Routing in WSN Utilizing Self-Attention-Based Cycle-Consistent Generative Adversarial Network With Honey Badger Algorithm

Energy efficiency and secure data transmission are considered as the main design targets of wireless sensor network (WSN). Previous energy-aware cluster head (CH) selection approaches could not provide secured data transmission. To address this problem, a self-attention-based cycle-consistent generative adversarial network (SaCyCsGAN) with honey badger algorithm (HyBA)-adopted energy-aware routing (EgAR) in WSN is proposed. Initially, the proposed system uses a CH to carry out the routing practice. Accordingly, SaCyCsGAN classifier is exploited for CH selection under firm fitness function: delay, distance, energy, cluster density, and traffic rate. Afterward CH selection, a spiteful node may enter the cluster and acts as a CH. Hence, best path selection is needed. For that, HyBA is utilized, it selects the best path depending upon triplet parameter: trust, connectivity, and service degree. Finally, data are conveying into base station (BS) and vice versa under best path. Finally, the proposed EgAR-WSN-SaCyCsGAN-HyBA method attains 24.13%, 28.57%, 5.88%, and 20.37% higher throughput and 18.50%, 21.67%, 13.33%, and 22.22% lesser energy consumption evaluated to the existing methods such as multiple-objective CH utilizing self-attention basis progressive generative adversarial network for safe data aggregation (EgAR-WSN-SAPrGAN-ArVOA), reinforcement learning-based energy-efficient optimized routing protocol in WSN (EgAR-WSN-RLGT-BCSA), data aggregation including clustering protocol in WSN under machine learning (EgAR-WSN-AfNN), and optimum cluster along trusted path for routing formation with categorization of intrusion under machine learning categorization (EgAR-WSN-RsBPDT-HoCSOA).