Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Mobility and Privacy-Aware Offloading of AR Applications for Healthcare Cyber-Physical Systems in Edge Computing
AU - Peng, Kai
AU - Liu, Peichen
AU - Bilal, Muhammad
AU - Xu, Xiaolong
AU - Prezioso, Edoardo
PY - 2023/10/31
Y1 - 2023/10/31
N2 - 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.
AB - 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.
KW - Augmented Reality
KW - Edge Computing
KW - Edge computing
KW - Energy consumption
KW - Gold
KW - Healthcare System
KW - Medical diagnostic imaging
KW - Medical services
KW - Mobility
KW - Privacy
KW - Task analysis
U2 - 10.1109/TNSE.2022.3185092
DO - 10.1109/TNSE.2022.3185092
M3 - Journal article
AN - SCOPUS:85133709564
VL - 10
SP - 2662
EP - 2673
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
SN - 2327-4697
IS - 5
ER -