Optimal service resource management strategy for IoT-based health information system considering value co-creation of users
This work addresses resource management for health information services, but it appears incremental as it applies existing deep reinforcement learning methods to a specific domain.
The paper tackled the challenge of optimizing service resource management in IoT-based health information systems by developing an adaptive strategy using deep reinforcement learning, which improved service performance and resource utilization through simulation experiments.
This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service. An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.