@article {28078,
	title = {Adaptive Edge Association for Wireless Digital Twin Networks in 6G},
	journal = { IEEE Internet of Things Journal},
	volume = {8},
	year = {2021},
	month = {07/2021},
	pages = {16219-16230},
	publisher = { IEEE},
	abstract = {6G is envisioned to be characterized by ubiquitous connectivity, extremely low latency, and enhanced edge intelligence. However, enriching 6G with these features requires addressing new, unique and complex challenges specifically at the edge of the network. In this paper, we propose a wireless digital twin edge network model by integrating digital twin with edge networks to enable new functionalities such as hyper-connected experience and low-latency edge computing. To efficiently construct and maintain digital twins in the wireless digital twin network, we formulate the edge association problem with respect to the dynamic network states and varying network topology. Furthermore, according to the different running stages, we decompose the problem into two sub-problems, including digital twin placement and digital twin migration. Moreover, we develop a Deep Reinforcement Learning (DRL) based algorithm to find the optimal solution to the digital twin placement problem, and then use transfer learning to solve the digital twin migration problem. Numerical results show that the proposed scheme provides reduced system cost and enhanced convergence rate for dynamic network states.},
	doi = {10.1109/JIOT.2021.3098508},
	author = {Lu, Yunlong and Maharjan, Sabita and Zhang, Yan}
}
@article {28070,
	title = {Communication-efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks},
	journal = { IEEE Internet of Things Journal},
	volume = {8},
	year = {2021},
	month = {02/2021},
	publisher = { IEEE},
	abstract = {Emerging technologies, such as mobile-edge computing (MEC) and next-generation communications are crucial for enabling rapid development and deployment of the Internet of Things (IoT). With the increasing scale of IoT networks, how to optimize the network and allocate the limited resources to provide high-quality services remains a major concern. The existing work in this direction mainly relies on models that are of less practical value for resource-limited IoT networks, and can hardly simulate the dynamic systems in real time. In this article, we integrate digital twins with edge networks and propose the digital twin edge networks (DITENs) to fill the gap between physical edge networks and digital systems. Then, we propose a blockchain-empowered federated learning scheme to strengthen communication security and data privacy protection in DITEN. Furthermore, to improve the efficiency of the integrated scheme, we propose an asynchronous aggregation scheme and use digital twin empowered reinforcement learning to schedule relaying users and allocate spectrum resources. Theoretical analysis and numerical results confirm that the proposed scheme can considerably enhance both communication efficiency and data security for IoT applications.},
	author = {Lu, Yunlong and Huang, Xiaohong and Zhang, Ke and Maharjan, Sabita and Zhang, Yan}
}
@article {28074,
	title = {Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {17},
	year = {2021},
	month = {08/2021},
	publisher = { IEEE},
	abstract = {The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.},
	author = {Lu, Yunlong and Huang, Xiaohong and Zhang, Ke and Maharjan, Sabita and Zhang, Yan}
}
@article {28075,
	title = {Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {17},
	year = {2021},
	month = {07/2021},
	pages = {5098-5107},
	publisher = { IEEE},
	abstract = {Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.},
	doi = {10.1109/TII.2020.3017668},
	author = {Lu, Yunlong and Huang, Xiaohong and Zhang, Ke and Maharjan, Sabita and Zhang, Yan}
}
