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TGN-MCDS: A Temporal Graph Network-Based Algorithm for Cluster-Head Optimization in Large-Scale FANETs.

Xiangrui Fan1,2, Yuxuan Yang3, Shuo Zhang2

  • 1Department of Aerospace Science and Technology, Space Engineering University, Beijing 101400, China.

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|January 10, 2026
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Summary
This summary is machine-generated.

This study introduces TGN-MCDS, a novel algorithm for optimizing cluster heads in Flying Ad hoc Networks (FANETs). It efficiently selects stable and connected cluster heads, overcoming limitations of existing methods for dynamic networks.

Keywords:
Flying Ad hoc Networks (FANETs)Minimum Connected Dominating Set (MCDS)Temporal Graph Networks (TGN)cluster-head selectiondynamic network optimization

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Flying Ad hoc Networks (FANETs) are increasingly deployed in critical military and civilian applications, necessitating robust communication infrastructure.
  • Establishing a stable and efficient communication backbone in large-scale, dynamic FANETs presents significant challenges.
  • The Cluster Head (CH) optimization problem is crucial for FANETs but is computationally complex, often leading to suboptimal solutions with existing algorithms.

Purpose of the Study:

  • To address the limitations of current algorithms in solving the Minimum Connected Dominating Set (MCDS) problem for CH optimization in FANETs.
  • To propose a novel algorithm, TGN-MCDS, leveraging Temporal Graph Networks (TGNs) for efficient and stable CH selection in dynamic network topologies.
  • To enhance network performance by improving coverage, connectivity, and cluster stability while maintaining computational efficiency.

Main Methods:

  • Formulating the CH optimization problem as a Minimum Connected Dominating Set (MCDS) problem.
  • Developing the TGN-MCDS algorithm utilizing Temporal Graph Networks (TGNs) to learn time-varying network topologies for CH selection.
  • Employing a multi-objective loss function during model training, considering coverage, connectivity, size control, centrality, edge penalty, temporal smoothness, and information entropy.

Main Results:

  • TGN-MCDS rapidly identifies near-optimal Cluster Head (CH) sets with comprehensive node coverage and strong network connectivity.
  • The proposed algorithm generates fewer and more stable CHs compared to traditional methods like Greedy, Integer Linear Programming (ILP), and Branch-and-Bound (BnB).
  • Simulation results validate significant improvements in cluster stability and high computational efficiency suitable for real-time FANET operations.

Conclusions:

  • TGN-MCDS offers a superior approach to CH optimization in large-scale, dynamic FANETs, outperforming existing methods.
  • The algorithm effectively balances multiple objectives, leading to enhanced network performance and stability.
  • TGN-MCDS demonstrates the potential of graph neural networks, specifically TGNs, in addressing complex network optimization challenges in real-time applications.