Mixed Game-Based AoI Optimization for Combating COVID-19 With AI Bots

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.