DRAGON: Data and Resource Allocation in ISAC Systems Based on Game Theory and Learning

Integrated Sensing and Communication (ISAC) has become critical in public safety and disaster response operations given its ability to jointly support real-time data collection and efficient communication. In this paper, the DRAGON framework is introduced to address existing research gaps by introducing a reinforcement learning-based approach that enables the users to autonomously select UAVs for communication and optimize the resource allocation and task incentives allocation. A Stackelberg game-theoretic approach enables the DRAGON framework to ensure the users' efficient sensing and communication in disaster areas by prioritizing both the users' utility and the UAVs' operational energy efficiency. Comprehensive experiments show the superior performance of DRAGON over other existing models and also validate its scalability and real-world applicability.