@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 {28076,
	title = {Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {17},
	year = {2021},
	month = {07/2021},
	publisher = { IEEE},
	abstract = {The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.},
	author = {Dai, Yueyue and Zhang, Ke and Maharjan, Sabita and Zhang, Yan}
}
@article {28077,
	title = {Digital Twin Empowered Content Caching in Social-aware Vehicular Edge Networks},
	journal = {IEEE Transactions on Computational Social Systems},
	volume = {9},
	year = {2021},
	month = {04/2021},
	pages = {239-251},
	publisher = { IEEE},
	abstract = {The rapid proliferation of smart vehicles along with the advent of powerful applications bring stringent requirements on massive content delivery. Although vehicular edge caching can facilitate delay-bounded content transmission, constrained storage capacity and limited serving range of an individual cache server as well as highly dynamic topology of vehicular networks may degrade the efficiency of content delivery. To address the problem, in this article, we propose a social-aware vehicular edge caching mechanism that dynamically orchestrates the cache capability of roadside units (RSUs) and smart vehicles according to user preference similarity and service availability. Furthermore, catering to the complexity and variability of vehicular social characteristics, we leverage the digital twin technology to map the edge caching system into virtual space, which facilitates constructing the social relation model. Based on the social model, a new concept of vehicular cache cloud is developed to incorporate the correlation of content storing between multiple cache-enabled vehicles in diverse traffic environments. Then, we propose deep learning empowered optimal caching schemes, jointly considering the social model construction, cache cloud formation, and cache resource allocation. We evaluate the proposed schemes based on real traffic data. Numerical results demonstrate that our edge caching schemes have great advantages in optimizing caching utility.},
	doi = {10.1109/TCSS.2021.3068369},
	author = {Zhang, Ke and Cao, Jiayu 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}
}
@article {28067,
	title = {Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {69},
	year = {2020},
	month = {10/2020},
	publisher = { IEEE},
	abstract = {5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRL-based algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.},
	author = {Dai, Yueyue and Zhang, Ke and Maharjan, Sabita and Zhang, Yan}
}
@inproceedings {25273,
	title = {Optimal Delay Constrained Offloading for Vehicular Edge Computing Networks},
	journal = {2017 IEEE International Conference on Communications (ICC)},
	year = {2017},
	publisher = {IEEE},
	abstract = {The increasing number of smart vehicles and their resource hungry applications pose new challenges in terms of computation and processing for providing reliable and efficient vehicular services. Mobile Edge Computing (MEC) is a new paradigm with potential to improve vehicular services through computation offloading in close proximity to mobile vehicles. However, in the road with dense traffic flow, the computation limitation of these MEC servers may endanger the quality of offloading service. To address the problem, we propose a hierarchical cloud-based Vehicular Edge Computing (VEC) offloading framework, where a backup computing server in the neighborhood is introduced to make up for the deficit computing resources of MEC servers. Based on this framework, we adopt a Stackelberg game theoretic approach to design an optimal multilevel offloading scheme, which maximizes the utilities of both the vehicles and the computing servers. Furthermore, to obtain the optimal offloading strategies, we present an iterative distributed algorithm and prove its convergence. Numerical results indicate that our proposed scheme greatly enhances the utility of the offloading service providers.},
	isbn = {978-1-4673-8999-0},
	issn = {1938-1883},
	doi = {10.1109/ICC.2017.7997360},
	author = {Zhang, Ke and Mao, Yuming and Leng, Supeng and Maharjan, Sabita and Zhang, Yan},
	editor = {IEEE, }
}
@article {24611,
	title = {Energy-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks},
	journal = {IEEE Access},
	volume = {4},
	year = {2016},
	pages = {5896-5907},
	publisher = {IEEE},
	abstract = {Mobile Edge Computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading mechanisms for MEC in 5G heterogenous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogenous network, we then design an Energy-Efficient Computation Offloading (EECO) scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.},
	doi = {10.1109/ACCESS.2016.2597169},
	author = {Zhang, Ke and Mao, Yuming and Leng, Supeng and Zhao, Quanxin and Li, Longjiang and Peng, Xin and Pan, Li and Zhang, Guoyun and Maharjan, Sabita and Zhang, Yan}
}
