A Software-Defined UAV Swarm Communication Network Utilizing Multi-Agent Reinforcement Learning for Dynamic Routing

International Journal of Computer Science and Technology | Vol. 2, No. 7, July 2021 | pp. 49–56

Research Article

Title: A Software-Defined UAV Swarm Communication Network Utilizing Multi-Agent Reinforcement Learning for Dynamic Routing

Names of Authors: Sanjay Nair¹, Priya Pillai², and Anand Krishnan³

Authors’ Affiliations:
¹Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India
²Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India
³Department of Electronics and Communications, National Institute of Technology, Calicut, India

Abstract: Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in disaster monitoring, environmental surveillance, and search-and-rescue operations due to their mobility and flexible deployment capabilities. However, maintaining reliable ad-hoc communication links within a flying swarm is difficult because high-speed three-dimensional node mobility causes frequent topological disruptions and link disconnects. Traditional routing protocols face scalability issues and slow convergence, leading to severe packet drops and routing loop overheads. This research introduces a software-defined UAV swarm networking architecture that utilizes a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for adaptive routing control. The framework uses a centralized Software-Defined Networking (SDN) controller on a ground base station to maintain global topology states, while individual UAV nodes run localized packet forwarding policies. The multi-agent learning algorithm models routing as an optimization problem where each UAV learns to select the optimal next-hop neighbor based on node positions, residual energy levels, link qualities, and packet queue constraints. Simulation experiments conducted in dynamic 3D flying corridors show that the MADDPG routing protocol reduces end-to-end delay by 39.4% and improves the overall packet delivery ratio by 22.8% over traditional Ad-hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR) protocols. The system ensures robust link stability in high-mobility deployment contexts.

Keywords: UAV swarms, Software-defined networking, Multi-agent reinforcement learning, Dynamic routing, Ad-hoc networks, Wireless communication

Manuscript Timeline: Received: April 15, 2021; Revised: May 22, 2021; Accepted: June 16, 2021; Published: July 1, 2021

Citation: Nair, S., Pillai, P., & Krishnan, A. (2021). A software-defined UAV swarm communication network utilizing multi-agent reinforcement learning for dynamic routing. International Journal of Computer Science and Technology, 2(7), 49–56. DOI: 10.46882/2021/IJCST/000019

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