TY - GEN
T1 - Digital Twin Enhanced Data Protection Based on Cloud-Edge Collaboration in Healthcare System
AU - Feng, Xinzheng
AU - Wu, Jun
AU - Pan, Qianqian
AU - Li, Jianhua
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Empowered with sensing networks and edge computing, the Healthcare system represented by the health monitoring system (HMS) gradually becomes an important part of future medical technology. HMS has been gradually applied to practice scenarios, occurring that the resource-restricted Internet of Things (IoT) nodes and heterogeneous networks' structure make data protection more difficult. Since anomalies of HMS commonly cause extensive loss even human safety, timely anomaly prediction schemes are regarded as valuable to ensure the data protection of HMS. Most of the existing solutions are based on historical logs, which makes it difficult to identify new faults and easily causes extensive security threats. To solve this problem, we proposed a distributed digital twins (DT) enhanced abnormal prediction scheme based on cloud-edge collaboration to improve the availability and data protection of the HMS. This work proposed an HMS framework based on distributed DT to take full advantage of the parallel simulating capabilities of DT. In addition, considering the limitation of user demand and marginal server resources, we further proposed customized DT simulation operating mechanisms for medical devices in the HMS environment. The feasibility and efficiency of this work were discussed and proved in the analysis and performance evaluation.
AB - Empowered with sensing networks and edge computing, the Healthcare system represented by the health monitoring system (HMS) gradually becomes an important part of future medical technology. HMS has been gradually applied to practice scenarios, occurring that the resource-restricted Internet of Things (IoT) nodes and heterogeneous networks' structure make data protection more difficult. Since anomalies of HMS commonly cause extensive loss even human safety, timely anomaly prediction schemes are regarded as valuable to ensure the data protection of HMS. Most of the existing solutions are based on historical logs, which makes it difficult to identify new faults and easily causes extensive security threats. To solve this problem, we proposed a distributed digital twins (DT) enhanced abnormal prediction scheme based on cloud-edge collaboration to improve the availability and data protection of the HMS. This work proposed an HMS framework based on distributed DT to take full advantage of the parallel simulating capabilities of DT. In addition, considering the limitation of user demand and marginal server resources, we further proposed customized DT simulation operating mechanisms for medical devices in the HMS environment. The feasibility and efficiency of this work were discussed and proved in the analysis and performance evaluation.
KW - Anomaly prediction
KW - cloud-edge collaboration
KW - data protection
KW - digital twin
KW - healthy monitoring system
UR - http://www.scopus.com/inward/record.url?scp=85198026224&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85198026224&partnerID=8YFLogxK
U2 - 10.1109/SmartCloud62736.2024.00008
DO - 10.1109/SmartCloud62736.2024.00008
M3 - Conference contribution
AN - SCOPUS:85198026224
T3 - Proceedings - 2024 IEEE 9th International Conference on Smart Cloud, SmartCloud 2024
SP - 1
EP - 6
BT - Proceedings - 2024 IEEE 9th International Conference on Smart Cloud, SmartCloud 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Smart Cloud, SmartCloud 2024
Y2 - 10 May 2024 through 12 May 2024
ER -