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With real-time monitoring of the personalized healthcare condition, the IoTwearables collect the health data and transfer it to the healthcare informationplatform. The platform processes the data into healthcare recommendations andthen delivers them to the users. The IoT structures in the personalizedhealthcare information service allows the users to engage in the loop inservitization more convenient in the COVID-19 pandemic. However, theuncertainty of the engagement behavior among the individual may result ininefficient of the service resource allocation. This paper seeks an efficientway to allocate the service resource by controlling the service capacity andpushing the service to the active users automatically. In this study, wepropose a deep reinforcement learning method to solve the service resourceallocation problem based on the proximal policy optimization (PPO) algorithm.Experimental results using the real world (open source) sport dataset revealthat our proposed proximal policy optimization adapts well to the users'changing behavior and with improved performance over fixed service resourcepolicies.

Service resource allocation problem in the IoT driven personalized healthcare information platform