Deep Reinforcement Learning for Mobility-Aware Digital Twin Migrations in Edge Computing

The past decade witnessed an explosive growth on the number of IoT devices (objects/suppliers), including portable mobile devices, autonomous vehicles, sensors and intelligence appliances. To realize the digital representations of objects, Digital Twins (DTs) are key enablers to provide real-time mo...

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Veröffentlicht in:	IEEE transactions on services computing Jg. 18; H. 2; S. 704 - 717
Hauptverfasser:	Zhang, Yuncan, Wang, Luying, Liang, Weifa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.03.2025
Schlagworte:	Computational modeling cost modeling of DT migration Costs Deep reinforcement learning deep reinforcement learning algorithm Delays Digital twin synchronization Digital twins Hands Heuristic algorithms Internet of Things mobile edge computing mobility-aware DT migration Real-time systems Synchronization
ISSN:	1939-1374, 2372-0204
Online-Zugang:	Volltext
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