A hybrid MGO-JAYA based clustered routing for FANETs

Abstract

In recent years, Flying Ad-hoc Networks (FANETs) have gained significant attention among researchers due to the widespread applications and increasing popularity of Unmanned Aerial Vehicles (UAVs). As technology advances and more research is undertaken, FANETs are expected to become a vital aspect of modern times, allowing for more effective and creative applications in different domains. However, FANETs also face several challenges, including high mobility, dynamic topology, energy constraints, and communication reliability. Addressing these challenges is essential to unlock the full potential of FANETs and to ensure reliable and timely delivery of data. In this paper, we propose HMGOC, a novel clustered routing model for FANETs, utilizing a hybrid approach that combines the Mountain Gazelle Optimizer (MGO) and Jaya Algorithms. The dynamic flying behavior of UAVs demands an adaptive and efficient clustering strategy to maintain network stability and ensure robust and reliable communication among UAVs. In this context, MGO, one of the most recent swarm-based optimization methods, is enhanced and employed for FANET clustering process. Also, we design a routing mechanism based on conditional Bayes' theorem which adapts to changing network conditions, reduces packet losses, and ensures timely data delivery. HMGOC offers several advantages over other competitive techniques, including improved load balancing, minimized energy consumption and latency, and enhanced network throughput and lifespan. The simulation results demonstrate that the HMGOC technique beats the existing methods in terms of enhanced cluster stability and lifetime, increased packet deliverability, energy efficiency, reduced latency, and minimized overhead.