Networked systems composed of heterogeneous and distributed agents – such as sensors, actuators, robots, and vehicles – play a critical role in modern infrastructures, including smart grids, industrial automation, and intelligent transportation systems. However, resource constraints, dynamic uncertainties, and cyber threats pose significant challenges to their scalability, resilience, and secure coordination. Game theory provides a rigorous framework for modelling strategic interactions among agents, capturing both competitive and cooperative behaviours under diverse constraints. This survey systematically reviews game-theoretic applications across five representative domains: resource allocation and scheduling, cybersecurity and adversarial defence, network topology and routeing optimization, coalition formation and cooperative strategies, and distributed multi-agent decision-making. Despite notable progress, open challenges remain in achieving scalability for large-scale heterogeneous systems, ensuring robustness under uncertainty, and integrating game-theoretic models with data-driven learning approaches. Collectively, game-theoretic methods demonstrate strong potential for enabling scalable, secure, and adaptive networked systems.
Wang et al. (2026) studied this question.