A Comparative Analysis of Swarm Algorithms for Enhancing Communication in Drone Networks

The introduction of drone technology has transformed a variety of businesses, from surveillance and monitoring to delivery services. However, effective communication among drones is critical for guaranteeing smooth operations, particularly in tough areas. This research study provides a comparative analysis of swarm intelligence algorithms for improving communication in drone networks. The study focuses on comparing the performance of various swarm algorithms in terms of communication count, with impediments represented by forest density ranging from 0 to 50 percent. Performance indicators, notably communication counts, are used to evaluate each algorithm’s success in facilitating drone communication. The major performance statistic is communication count, which is defined as the number of drone interactions. The study tries to determine the most effective swarm intelligence algorithm for drone communication in forested situations by examining how each method’s communication count changes as forest density increases. The discoveries will help to construct stable and communication-efficient drone networks for a variety of applications including forestry, search and rescue, and environmental monitoring. Understanding the impact of forest density on communication and the efficiency of various swarm algorithms provides a way to optimize drone network communication strategies for real-world circumstances.