Integrating Quantum Metaheuristics with Stackelberg Game and Auction Mechanism for Cooperative Data Clustering in Adhoc Networks

Data clustering in adhoc networks is challenging due to their dynamic and decentralised nature. Traditional methods often struggle to adapt to the constant changes in such environments. This paper introduces a novel approach that com-bines game theory with Quantum Particle Swarm Optimisation (QPSO) to enhance resource utilisation, network stability, and clustering accuracy. In this setup, leader nodes employ QPSO, a faster and more accurate optimization method, to predict follower node responses and develop optimal clustering strategies. Follower nodes then participate in an auction-based resource allocation system, where bids are ranked based on resource needs, energy levels, and connectivity. Simulation results demonstrate that the proposed framework outperforms traditional methods, achieving a 25% improvement in clustering efficiency, 30% better inter-cluster separation, and a 20% increase in fairness in resource allocation. This study highlights the potential of integrating game theory, auction mechanisms, and QPSO to achieve more effective and adaptable clustering solutions in dynamic network environments.