An Adaptive Biomimetic Ant Colony Optimization With 6G Integration for IoT Network Communication

Abstract

With the advent of the sixth-generation (6G) network and the expanding realm of the Internet of Things (IoT), the challenge of optimizing network communication within these rapidly evolving landscapes has become paramount. This article explores the domain of biomimetic algorithms, recognizing their potential as sophisticated solutions in these complex and transient environments. We introduce an innovative framework that enhances network communication by adapting intelligently to the rapidly changing IoT landscape and emerging 6G network infrastructure. Specifically, we present a method that leverages and extends the Ant Colony Optimization (ACO) algorithm in a manner uniquely suited to the challenges posed by the integration of IoT and 6G technologies. This enhanced algorithm incorporates a dynamic pheromone evaporation mechanism, a specialized module for multi-objective optimization, and a seamless integration of reinforcement learning principles. Together, these elements contribute to the increased robustness and efficacy of the algorithm, providing a powerful solution to the intricate problem of routing within the high-frequency, high-capacity environment of 6G networks, accommodating a diverse array of IoT devices. This methodological synthesis establishes a holistic strategy with the potential to revolutionize network optimization, laying the groundwork for future advances in wireless communication technology.

Keywords

• Ant colony optimization
• 6G
• Internet of Things
• adaptive pheromone evaporation
• multi-objective optimization
• reinforcement learning