Mobility and Privacy-Aware Offloading of AR Applications for Healthcare Cyber-Physical Systems in Edge Computing

Cyber-physical systems (CPSs) can be regarded as a new generation of systems which have been widely used for healthcare system. The introduction of Augmented Reality (AR) can further enhance the effectiveness of healthcare CPSs. AR applications can provide a better user experience in the health treatment process for both patients and clinicians. However, AR applications are computation-intensive, putting a substantial computational burden on AR devices. Fortunately, offloading AR applications to edge nodes can enable AR to be suitable for real-time applications. Nevertheless, AR applications deal with the patient’s private information; placing it on edge raises serious privacy concerns. Besides, the network structure of AR applications has spatio-temporal uncertainty. To tackle these issues, we jointly investigate the computation offloading for AR applications in the healthcare CPSs in edge computing considering user privacy protection and mobility. We propose a novel multi-objective meta-heuristic method based on the R2 indicator-II, which preserves privacy, and minimizes the Motion-to-photon latency, energy consumption, and maintain load balancing. Eventually, it verifies the efficiency and superiority of our proposed approach based on a certain scale of the experiments.