Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Unmanned aerial vehicles (UAVs) in flying ad hoc networks (FANETs) present unique mobility challenges.
• Traditional clustering and routing methods struggle with stability, resource efficiency, and latency in dynamic FANET environments.

Purpose of the Study:

• To develop a novel hybrid bio-inspired algorithm, HMAO, for improved clustering and routing in FANETs.
• To enhance network stability, data delivery reliability, and resource utilization in dynamic UAV networks.

Main Methods:

• Proposed HMAO algorithm, combining Mountain Gazelle Optimizer (MGO) for cluster head (CH) selection and Aquila Optimizer (AO) for routing.
• MGO considers UAV energy, mobility, intra-cluster distance, and neighbor density for stable CH selection.
• AO utilizes predictive mobility, load balancing, fault tolerance, and ferry nodes for reliable data transmission.

Main Results:

• HMAO demonstrated superior cluster stability compared to existing methods.
• Significant improvements observed in packet delivery ratio and reduced network delay.
• Lower energy consumption and reduced overhead were achieved with the HMAO technique.

Conclusions:

• The hybrid HMAO algorithm effectively addresses the challenges of clustering and routing in FANETs.
• HMAO offers a robust solution for stable, efficient, and reliable data communication in dynamic UAV networks.
• Simulation results validate HMAO's performance advantages over conventional approaches.