Energy-Efficient Hybrid Bio-Inspired Approach for Low-Latency Collision-Aware UAV Networks

The pervasive integration of Unmanned Aerial Vehicle (UAV) networks across various applications underscores the imperative for sophisticated communication and collision avoidance strategies to optimize their operational prowess. Traditional UAV network optimization methodologies grapple with inherent challenges related to collision minimization and channel utilization, resulting in detrimental outcomes such as elevated communication delays, increased energy consumption, and compromised throughput alongside diminished packet delivery ratios. This study addresses these shortcomings through the introduction of an innovative optimization model that synergizes the robust characteristics of the Teacher Learner-based Grey Wolf Optimizer (TLGWO) and the Bat Firefly Optimizer (BFFO), thereby significantly elevating the overall performance of UAV networks. The TLGWO component of the pro-posed model is intricately designed to minimize collisions among UAV nodes by analytically assessing temporal and spatial performance metrics. This includes a nuanced examination of communication delay dynamics and the historical context of avoided collisions. Simultaneously, the BFFO module is engineered to maximize channel utilization, leveraging the same performance metrics for a holistic optimization approach. The dual application of TLGWO and BFFO ensures a comprehensive enhancement of UAV network efficiency. Empirical validation demonstrates the superiority of the proposed model over existing methods, showcasing a remarkable 10.4% reduction in communication delay, an 8.5% improvement in energy efficiency, a 3.5% increase in packet delivery ratio, a 9.5% enhancement in throughput, and a 4.9% reduction in collision occurrences. The significant impact of this research is far-reaching, providing a robust and versatile framework for fortifying UAV network efficiency across diverse applications, thereby propelling the field towards more dependable and efficient UAV deployments in critical sectors.