Deciphering the Implications of Swarm Intelligence Algorithms in Efficiently Managing Drone Swarms

Title: Deciphering the Implications of Swarm Intelligence Algorithms in Efficiently Managing Drone Swarms
Abstract: Background: Introducing drone technology has
considerably improved data collecting and logistics. Despite these
advances, the difficulty of effectively operating drone swarms in
dynamic and diverse situations still needs to be addressed.
Inspired by the collective behavior of social insects, Swarm
Intelligence (SI) provides potential solutions for improving drone
network performance, decision-making, and resilience.
Objective: The purpose of the article is to investigate the use of
Swarm Intelligence principles in drone networks, focusing on their
potential to transform future drone-based data systems through
increased communication, cooperation, and coordinated decision
making capabilities.
Methods: The article uses interdisciplinary computer science,
robotics, and behavioral ecology methodologies to conduct
extensive tests on drone swarms. Using a computational model, the
study compares SI-based drone networks against standard drone
management frameworks to assess their efficiency, dependability,
and flexibility in various operational settings.
Results: Findings show that SI-enhanced drone networks are
more flexible, fault-tolerant, and operationally efficient across
various activities and environmental circumstances. SI-based
solutions outperform traditional methods in data relay, resource
utilization,
and adaptive maneuverings, especially in
circumstances that need decentralized control and autonomous
coordination.
Conclusion: Integrating Swarm Intelligence into drone
networks significantly enhances functionality, making them more
adaptive, resilient, and efficient. This achievement provides the
path for creating extremely scalable and networked computer
systems and highlights the importance of biologically inspired
algorithms in the refinement of autonomous systems. The
ramifications of this research go beyond drone technology,
providing insights into the broader use of SI in complex, dynamic
systems.
Scientific Type: غير معروف
Link: https://fruct.org/publications/volume-35/fruct35/
Journal volume: vol.35,No.1
Collaboration type: مشترك
Publish Date: April 26, 2024
Participated Universities (Publication): Alnoor University
Scopus status: In Scopus
Scopus index year: 2 024
Scopus quarter: 5
Scopus citation score: 0
Clarivate status: Not In Clarivate
Pub. Med. status: Not In PubMed
Author (Publication): سلامة ادريس مصطفى ؟
Journal (Publication): PROCEEDING OF THE 35TH CONFERENCE OF FRUCT ASSOCIATION
Publisher (Publication): IEEE Computer Society
ISSN: 2305-7254
Country (Publication): Russian Federation
Country type: عالمية
College (Publication): College of Engineering
Departement (Publication): Department of AI engineering

Media: Academic paper

Part of Deciphering the Implications of Swarm Intelligence Algorithms in Efficiently Managing Drone Swarms