ICTACT Journal on Communication Technology

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Wireless Traffic and Routing Enhancement Using Emperor Penguin Optimizer Guided by Conditional Generative Adversarial Nets


Affiliations
1 Department of Electronics and Communication Engineering, Prathyusha Engineering College, India
2 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, India
3 Department of Information Technology, Vardhaman College of Engineering, India
4 Department of Computer Science and Engineering, A J Institute of Engineering and Technology, India
     

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The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.

Keywords

Wireless Communication, Emperor Penguin Optimizer, Conditional Generative Adversarial Nets, Traffic Optimization, Routing Enhancement.
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  • Wireless Traffic and Routing Enhancement Using Emperor Penguin Optimizer Guided by Conditional Generative Adversarial Nets

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Authors

K. Prabhu Chandran
Department of Electronics and Communication Engineering, Prathyusha Engineering College, India
P. T. Kalaivaani
Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, India
Venkatesh Kavididevi
Department of Information Technology, Vardhaman College of Engineering, India
M. Ganesha
Department of Computer Science and Engineering, A J Institute of Engineering and Technology, India

Abstract


The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.

Keywords


Wireless Communication, Emperor Penguin Optimizer, Conditional Generative Adversarial Nets, Traffic Optimization, Routing Enhancement.

References