TY - GEN
T1 - TwinSync
T2 - 32nd International Conference on Computer Communications and Networks, ICCCN 2023
AU - Kalasapura, Deepti
AU - Li, Jinyang
AU - Liu, Shengzhong
AU - Chen, Yizhuo
AU - Wang, Ruijie
AU - Abdelzaher, Tarek
AU - Caesar, Matthew
AU - Bhattacharyya, Joydeep
AU - Kim, Jae
AU - Wang, Guijun
AU - Kimberly, Greg
AU - Eckhardt, Josh
AU - Osipychev, Denis
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Digital Twins are evolving as a key component in modern systems with diverse applications like remote prognostics, optimizing run-time operation, anomaly detection, and more. The essential elements of a digital twin are a virtual representation, a physical asset, and the transfer of data/information between the two. IoT deployments are generally characterized by resource constraints, making synchronization of digital twins with IoT devices more challenging. There is a pressing need to optimize the bandwidth of the data transferred between the system and the twin, while ensuring that the twin is able to capture selected key aspects of the current operational state accurately. In this paper, we present TwinSync, a framework that can be utilized to construct flexible real-time representations of deployed IoT systems and efficiently synchronize relevant system states with the twin, over a communication bottleneck, within a configurable application-specific notion of error (henceforth referred to as approximate synchronization). Our approach is optimized to achieve data transfers utilizing less bandwidth without compromising the ability of the twin to replicate real-time system states within the specified approximate synchronization semantics. We evaluate the efficacy of TwinSync's synchronization by conducting both a synthetic analysis and a case study based on a real-life application prototype. Our evaluation indicates that using TwinSync can provide the same or greater accuracy (in many cases) while sending significantly fewer bytes than a bandwidth-insensitive synchronization approach. The result is attributed to a more judicial selection of data to transmit over bottlenecks, compared to bandwidth-insensitive approaches.
AB - Digital Twins are evolving as a key component in modern systems with diverse applications like remote prognostics, optimizing run-time operation, anomaly detection, and more. The essential elements of a digital twin are a virtual representation, a physical asset, and the transfer of data/information between the two. IoT deployments are generally characterized by resource constraints, making synchronization of digital twins with IoT devices more challenging. There is a pressing need to optimize the bandwidth of the data transferred between the system and the twin, while ensuring that the twin is able to capture selected key aspects of the current operational state accurately. In this paper, we present TwinSync, a framework that can be utilized to construct flexible real-time representations of deployed IoT systems and efficiently synchronize relevant system states with the twin, over a communication bottleneck, within a configurable application-specific notion of error (henceforth referred to as approximate synchronization). Our approach is optimized to achieve data transfers utilizing less bandwidth without compromising the ability of the twin to replicate real-time system states within the specified approximate synchronization semantics. We evaluate the efficacy of TwinSync's synchronization by conducting both a synthetic analysis and a case study based on a real-life application prototype. Our evaluation indicates that using TwinSync can provide the same or greater accuracy (in many cases) while sending significantly fewer bytes than a bandwidth-insensitive synchronization approach. The result is attributed to a more judicial selection of data to transmit over bottlenecks, compared to bandwidth-insensitive approaches.
KW - Data Synchronization
KW - Digital Twinning
KW - Internet of Things
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85173578881&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85173578881&partnerID=8YFLogxK
U2 - 10.1109/ICCCN58024.2023.10230154
DO - 10.1109/ICCCN58024.2023.10230154
M3 - Conference contribution
AN - SCOPUS:85173578881
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2023 - 2023 32nd International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2023 through 27 July 2023
ER -