LAUSR

Industrial Internet of Things (IIoT) devices are widely used for monitoring and controlling the process of automated manufacturing. Owing to the limited computing capacity of the IIoT sensors in the production line, the scheduling task in the production line needs to be offloaded to the edge computing server (ECS). To obtain the desired quality of service (QoS) during offloading scheduling tasks, the precise interaction information between the production line and ECSs has to be uploaded to the cloud platform, which poses privacy issues. The existing works mostly assume that all the interaction information, i.e., the offloading decision for the subtask in a scheduling task, has same privacy level, which cannot meet the various privacy requirements of the offloading decision for the subtask. Hence, we propose a local-differential-privacy-based deep reinforcement learning (LDP-DRL) approach in the edge-cloud-assisted IIoT to provide personalized privacy guarantee. The LDP mechanism can generate different levels of noise to satisfy the various privacy requirements of the offloading decision for the subtask. The prioritized experience replay is integrated in DRL to reduce the impact of noise on the QoS performance of task offloading. The formal analysis of LDP-DRL is provided in terms of privacy level and convergence. Finally, extensive experiments are conducted to evaluate the effectiveness, the capacity of privacy protection, the impact of discount factor on the convergence, and the cost efficiency of the LDP-DRL approach.