Experimental assessment the possibility of using the ant colony algorithm – AntHocNet to solving the routing

DOI: 10.31673/2412-9070.2024.060501

Abstract

FANET (Flying Ad-Hoc Networks) represents a type of wireless network composed of a group of unmanned aerial vehicles (UAVs) that can interact with each other without centralized control.
One of the most important characteristics of FANET is self-organization, which enables the network to adapt to topology changes and maintain continuous communication between UAVs. Such networks are capable of dynamically adjusting data transmission routes based on the positions of nodes. This feature is highly relevant in scenarios such as search-and-rescue missions, environmental monitoring, intelligence gathering, or other cases requiring the coverage of large areas and rapid data processing. FANET can also ensure communication in emergency situations where traditional infrastructure is unavailable or damaged. Integrating artificial intelligence and machine learning algorithms can further enhance routing efficiency and provide realtime adaptation to changing conditions. This opens opportunities for developing more autonomous and effective solutions that enhance the resilience and performance of such networks.
The high mobility of nodes and the dynamic nature of topology make routing in FANET a challenging task. Conventional routing protocols designed for stationary or terrestrial networks are inefficient in FANET due to their inability to account for constant changes in node coordinates and velocities. To ensure reliable and uninterrupted data transmission, specialized algorithms have been developed that consider the unique aspects of three-dimensional movement and can maintain high performance even at high node speeds.
This paper presents the results of research aimed at improving routing efficiency in FANET. In particular, it examines the potential of ant colony optimization algorithms for solving this problem. Experimental results demonstrate that the AntHocNet protocol, based on the principles of ant colonies, significantly improves the quality of data transmission in FANET. The findings
open new perspectives for developing smarter and more adaptive wireless communication systems.
Keywords: UAV peer-to-peer network, FANET, unmanned aerial vehicle, UAV, routing protocols, swarm intelligence, ant colony optimization algorithm, network simulation