Citation

Abdulhae, Omer T. and Mandeep, Jit Singh and Islam, Mohammad Tariqul and Islam, Md. Shabiul (2023) Reinforcement-Based Clustering in Flying Ad-Hoc Networks for Serving Vertical and Horizontal Routing. IEEE Access, 11. pp. 143881-143895. ISSN 2169-3536

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Abstract

Unmanned aerial vehicles (UAVs) are receiving increasing attention due to their wide range of applications. Flying ad-hoc networks (FANETs) enable inter-communication between UAVs, but their dynamics and fast changes in mobility and topology make it challenging to achieve stable routing. Clustering based FANETs provide a self-organizing approach for routing, but clustering is a decentralized process based on different utilities that can affect performance. In this work, we propose a centralized agent placed in a remote ground station to adjust the role of each utility in the cluster-head arbitration. Our approach uses six utilities, including node centrality, residual energy, link holding time, velocity similarity, buffer occupancy, and diversity. The developed reinforcement learning (RL)-clustering approach enables routing over multiple planes, with each plane representing a network cell with a different coverage radius of nodes, including Femto, Pico, and Micro planes. We propose a novel reward formulation for effective learning of agents that includes indicators of stability based on role change, energy consumption, and confirmation message through the selected node. For evaluation, the developed RL based agent was evaluated on three types of agents, namely, Q-learning, DQN, and DDPG and it was compared with random agent. Results have shown the superiority of our developed RL based clustering in terms of stability, energy consumption and most network metrics.

Item Type:	Article
Uncontrolled Keywords:	Routing, Ad hoc networks, Clustering algorithms, Q-learning, Routing protocols, Autonomous aerial vehicles, Vehicle dynamics, Deep learning, Reinforcement learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	23 Feb 2024 03:38
Last Modified:	23 Feb 2024 03:38
URII:	http://shdl.mmu.edu.my/id/eprint/12133

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