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Mr. Victor Leung
Shenzhen University, College of Computer Science and Software Engineering

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Research Keywords & Expertise

0 Mobile Systems
0 wireless networks
0 Network Management
0 network protocols
0 Intelligent networks and e-services

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wireless networks
Mobile Systems
Network Management
network protocols

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Short Biography

Victor C. M. Leung is a Distinguished Professor of Computer Science and Software Engineering at Shenzhen University, China. He is also an Emeritus Professor of Electrical and Computer Engineering and Director of the Laboratory for Wireless Networks and Mobile Systems at the University of British Columbia (UBC), Canada. His research is in the broad areas of wireless networks and mobile systems, and he has published widely in these areas. Dr. Leung is serving on the editorial boards of the IEEE Transactions on Green Communications and Networking, IEEE Transactions on Cloud Computing, IEEE Access, IEEE Network, and several other journals. He received the 1977 APEBC Gold Medal, 1977-1981 NSERC Postgraduate Scholarships, IEEE Vancouver Section Centennial Award, 2011 UBC Killam Research Prize, 2017 Canadian Award for Telecommunications Research,2018 IEEE TCGCC Distinguished Technical Achievement Recognition Award, and 2018 ACM MSWiM Reginald Fessenden Award. He co-authored papers that won the 2017 IEEE ComSoc Fred W. Ellersick Prize, 2017 IEEE Systems Journal Best Paper Award, 2018IEEE CSIM Best Journal Paper Award, and 2019 IEEE TCGCC Best Journal Paper Award. He is a Professional Member of ACM. He is a Life Fellow of IEEE, and a Fellow of the Royal Society of Canada (Academy of Science), Canadian Academy of Engineering, and Engineering Institute of Canada. He is named in the current Clarivate Analytics list of Highly Cited Researchers.

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Journal article
Published: 26 July 2021 in IEEE Transactions on Vehicular Technology
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While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to lack of effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, concerning on how to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced.

ACS Style

Yinglei Teng; Yuanyuan Cao; Mengting Liu; Richard Yu; Victor C. M. Leung. Efficient Blockchain-enabled Large Scale Parked Vehicular Computing with Green Energy Supply. IEEE Transactions on Vehicular Technology 2021, PP, 1 -1.

AMA Style

Yinglei Teng, Yuanyuan Cao, Mengting Liu, Richard Yu, Victor C. M. Leung. Efficient Blockchain-enabled Large Scale Parked Vehicular Computing with Green Energy Supply. IEEE Transactions on Vehicular Technology. 2021; PP (99):1-1.

Chicago/Turabian Style

Yinglei Teng; Yuanyuan Cao; Mengting Liu; Richard Yu; Victor C. M. Leung. 2021. "Efficient Blockchain-enabled Large Scale Parked Vehicular Computing with Green Energy Supply." IEEE Transactions on Vehicular Technology PP, no. 99: 1-1.

Journal article
Published: 30 June 2021 in Applied Soft Computing
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Due to its portability and maneuverability, Unmanned Aerial Vehicles (UAVs) are increasingly used in industrial fields. Our work aims to develop a framework of person counting executed by an UAV, which can count the total number of persons accurately. To solve the problem of multiple counts for the same person, this work proposes a novel Graph Similarity-based Person Counting Network (GSPCN) which consists of three modules: person detection network, person re-identification module, and person counting module. To begin with, we detect the bounding boxes and the corresponding images for each object in image sequence. Secondly, we calculate the visual similarity between persons. And then, each person is taken as the root node to construct a graph, then we calculate the similarity between different graphs as the graph similarity. After that, we comprehensively consider the visual similarity and graph similarity to determine whether the person is repeated. Finally, by removing all duplicate persons, we can get the total number of persons. The proposed framework is tested in a real-world scenario and it empirically outperforms the existing state-of-the-art methods.

ACS Style

Zun Liu; Xiaonan Hu; Jianqiang Li; Jie Chen; WenLian Huang; Xiaoyu Zhao; Victor C.M. Leung. Graph relation network for person counting in construction site using UAV. Applied Soft Computing 2021, 110, 107562 .

AMA Style

Zun Liu, Xiaonan Hu, Jianqiang Li, Jie Chen, WenLian Huang, Xiaoyu Zhao, Victor C.M. Leung. Graph relation network for person counting in construction site using UAV. Applied Soft Computing. 2021; 110 ():107562.

Chicago/Turabian Style

Zun Liu; Xiaonan Hu; Jianqiang Li; Jie Chen; WenLian Huang; Xiaoyu Zhao; Victor C.M. Leung. 2021. "Graph relation network for person counting in construction site using UAV." Applied Soft Computing 110, no. : 107562.

Journal article
Published: 29 June 2021 in IEEE Access
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With its rapid development recently, edge computing with processing, storage, and networking capabilities has become an important solution to break through the bottleneck of emerging technology development by virtue of its advantages in reducing data transmission, decreasing service latency, and easing cloud computing pressure. Among several application scenarios such as network optimization, intelligent manufacturing, and real-time video analytics, edge computing can work with artificial intelligence (AI) synergistically. Therefore, many researchers are investigating edge computing with AI from two perspectives. One is that the emergence of AI solves the optimization problem of edge computing. For example, when network devices need to process some complex and fuzzy information, the powerful learning and reasoning ability of AI can help to extract valuable information from the massive data and realize intelligent management. Another is how edge computing supports AI in a networking environment. For example, AI training and inference can be efficiently enabled by a multitude of computing resources from edge computing. Therefore, edge computing and AI are mutually beneficial in networking.

ACS Style

Victor C. M. Leung; Xiaofei Wang; Abbas Jamalipour; Xu Chen; Samia Bouzefrane. IEEE Access Special Section Editorial: Edge Computing and Networking for Ubiquitous AI. IEEE Access 2021, 9, 90933 -90936.

AMA Style

Victor C. M. Leung, Xiaofei Wang, Abbas Jamalipour, Xu Chen, Samia Bouzefrane. IEEE Access Special Section Editorial: Edge Computing and Networking for Ubiquitous AI. IEEE Access. 2021; 9 ():90933-90936.

Chicago/Turabian Style

Victor C. M. Leung; Xiaofei Wang; Abbas Jamalipour; Xu Chen; Samia Bouzefrane. 2021. "IEEE Access Special Section Editorial: Edge Computing and Networking for Ubiquitous AI." IEEE Access 9, no. : 90933-90936.

Journal article
Published: 24 June 2021 in Symmetry
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To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.

ACS Style

Lingxia Liao; Victor Leung; Zhi Li; Han-Chieh Chao. Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks. Symmetry 2021, 13, 1133 .

AMA Style

Lingxia Liao, Victor Leung, Zhi Li, Han-Chieh Chao. Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks. Symmetry. 2021; 13 (7):1133.

Chicago/Turabian Style

Lingxia Liao; Victor Leung; Zhi Li; Han-Chieh Chao. 2021. "Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks." Symmetry 13, no. 7: 1133.

Journal article
Published: 17 June 2021 in IEEE Transactions on Vehicular Technology
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Rate-splitting multiple access (RSMA) and reinforcement-learning (RL) based user clustering are leveraged to manage interference of a downlink fog radio access network. In particular, clustering of the user devices (UDs) is considered as such UDs of a given cluster can receive data from a certain fog access point (F-AP) over the same radio resource blocks (RRBs) simultaneously. To this end, RSMA enabled data transmission is exploited at each UD cluster. To manage intra-cluster and inter-cluster interference of the network, we focus on the resource allocation to maximize the sum-rate and to minimize total transmission power of the F-APs in each transmission slot. Towards this goal, we optimize jointly the F-APs' transmit power allocation for RSMA enabled data transmission, clustering of UDs, and assignment of RRBs among the F-APs. The proposed optimization problem is NP-hard, and as a result, a global optimal solution is computationally intractable even with the centralized implementation. To obtain an efficient solution without having the global network information, the proposed optimization problem is decomposed into two sub-problems, namely, UD clustering and resource allocation among the F-APs. Specifically, the UD clusters are obtained by applying a multi-agent RL technique, and the resource allocation among the F-APs is obtained by applying the fractional programming, Lagrangian duality, and alternating optimization techniques. A distributed user clustering-power allocation-RRB assignment (UC-PA-RA) algorithm is proposed, and its convergence to the near-optimal solution is proved. Through extensive simulations, the superiority of the proposed UC-PA-RA algorithm over the contemporary multiple access schemes, UD clustering technique, and RL method is verified.

ACS Style

Zoheb Hassan; Jahangir Hossain; Julian Cheng; Victor C. M. Leung. Joint Throughput-Power Optimization of Fog-RAN Using Rate-Splitting Multiple Access and Reinforcement-Learning Based User Clustering. IEEE Transactions on Vehicular Technology 2021, 70, 8019 -8036.

AMA Style

Zoheb Hassan, Jahangir Hossain, Julian Cheng, Victor C. M. Leung. Joint Throughput-Power Optimization of Fog-RAN Using Rate-Splitting Multiple Access and Reinforcement-Learning Based User Clustering. IEEE Transactions on Vehicular Technology. 2021; 70 (8):8019-8036.

Chicago/Turabian Style

Zoheb Hassan; Jahangir Hossain; Julian Cheng; Victor C. M. Leung. 2021. "Joint Throughput-Power Optimization of Fog-RAN Using Rate-Splitting Multiple Access and Reinforcement-Learning Based User Clustering." IEEE Transactions on Vehicular Technology 70, no. 8: 8019-8036.

Journal article
Published: 04 June 2021 in IEEE Transactions on Intelligent Transportation Systems
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Connected and autonomous vehicles (CAVs) are recently envisioned to provide a tremendous social impact, while they put forward a much higher requirement for both vehicular communication and computation capacities to process resource-intensive applications. In this paper, we study unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) for a platoon of wireless power transmission (WPT)-enabled vehicles. Our objective is to maximize the system-wide computation capacity under both communication and computation resource constraints. We incorporate the coupled effects of the platooning vehicles and the flying UAV, air-to-ground (A2G) and ground-to-air (G2A) communications, onboard computing and energy harvesting into a joint scheduling optimization model of communication and computation resources. To tackle the resulting optimization problem, we propose a successive convex programming method based on a second-order convex approximation, in which feasible search directions are obtained by solving a sequence of quadratic programming subproblems and used to generate feasible points that can approach a local optimum. We also theoretically prove the feasibility and convergence of the proposed method. Moreover, simulation results are provided to validate the effectiveness of our proposed method and demonstrate its superior performance over other conventional schemes.

ACS Style

Yang Liu; Jianshan Zhou; Daxin Tian; Zhengguo Sheng; Xuting Duan; Guixian Qu; Victor C. M. Leung. Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -16.

AMA Style

Yang Liu, Jianshan Zhou, Daxin Tian, Zhengguo Sheng, Xuting Duan, Guixian Qu, Victor C. M. Leung. Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-16.

Chicago/Turabian Style

Yang Liu; Jianshan Zhou; Daxin Tian; Zhengguo Sheng; Xuting Duan; Guixian Qu; Victor C. M. Leung. 2021. "Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-16.

Journal article
Published: 01 May 2021 in IEEE Transactions on Vehicular Technology
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ACS Style

Kai Sun; Jiarun Yu; Wei Huang; Haijun Zhang; Victor C. M. Leung. A Multi-Attribute Handover Algorithm for QoS Enhancement in Ultra Dense Network. IEEE Transactions on Vehicular Technology 2021, 70, 4557 -4568.

AMA Style

Kai Sun, Jiarun Yu, Wei Huang, Haijun Zhang, Victor C. M. Leung. A Multi-Attribute Handover Algorithm for QoS Enhancement in Ultra Dense Network. IEEE Transactions on Vehicular Technology. 2021; 70 (5):4557-4568.

Chicago/Turabian Style

Kai Sun; Jiarun Yu; Wei Huang; Haijun Zhang; Victor C. M. Leung. 2021. "A Multi-Attribute Handover Algorithm for QoS Enhancement in Ultra Dense Network." IEEE Transactions on Vehicular Technology 70, no. 5: 4557-4568.

Journal article
Published: 30 April 2021 in IEEE Transactions on Network Science and Engineering
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Driven by numerous emerging services and applications of mobile devices, multi-access edge computing (MEC) is regarded as a promising technique for massive Internet of Things (IoT) with 6G mobile networks to alleviate core network congestion and reduce service latency. However, the conventional MEC suffers from the infrastructure without the cloud server (CS) and cooperation of multiple edge servers (ESs), which cannot deal with the large-scale computation tasks in the ultra-dense smart environments. This paper investigates the issue of the cooperative computation offloading for MEC in the 6G era. The proposed MEC system allows the cooperation of edge-cloud and the cooperation of edge-edge to address the limitation of single ES and the nonuniform distribution of computation task arrival among multiple ESs. To support low-latency services, we model the cooperative computation offloading problem as a Markov decision process, and propose two intelligent computation offloading algorithms based on Soft Actor Critic (SAC), i.e., centralized SAC offloading and decentralized SAC offloading. Evaluation results show that the proposed algorithms outperform the existing computation offloading algorithms in terms of service latency.

ACS Style

Chuan Sun; Xiongwei Wu; Xiuhua Li; Qilin Fan; Junhao Wen; Victor C.M. Leung. Cooperative Computation Offloading for Multi-Access Edge Computing in 6G Mobile Networks via Soft Actor Critic. IEEE Transactions on Network Science and Engineering 2021, PP, 1 -1.

AMA Style

Chuan Sun, Xiongwei Wu, Xiuhua Li, Qilin Fan, Junhao Wen, Victor C.M. Leung. Cooperative Computation Offloading for Multi-Access Edge Computing in 6G Mobile Networks via Soft Actor Critic. IEEE Transactions on Network Science and Engineering. 2021; PP (99):1-1.

Chicago/Turabian Style

Chuan Sun; Xiongwei Wu; Xiuhua Li; Qilin Fan; Junhao Wen; Victor C.M. Leung. 2021. "Cooperative Computation Offloading for Multi-Access Edge Computing in 6G Mobile Networks via Soft Actor Critic." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.

Journal article
Published: 24 March 2021 in IEEE Transactions on Intelligent Transportation Systems
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In intelligent transportation systems (ITS), the vehicular ad-hoc network (VANET) is an enabling technology that can provide information exchange services among connected and autonomous vehicles (CAVs). Video streaming over VANETs is a potential application to ensure the safety of drivers and passengers and improve infotainment services. However, owing to the dynamic network topology, video transmission in VANETs is very challenging in terms of latency, reliability, and security. Therefore, a comprehensive summary of the state-of-art video streaming over VANETs is surveyed in this work. Firstly, related works and background knowledge are introduced. Then, a systematic survey on resource allocation (RA) scheme for video streaming in VANETs is provided, and some prevailing and feasible optimization tools are elaborated. Furthermore, enabling technologies of video streaming over VANETs are summarized with a special focus on the integration of video communication, caching, and computing. Finally, we give some challenges and future research directions.

ACS Style

Xiantao Jiang; F. Richard Yu; Tian Song; Victor C. M. Leung. Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -21.

AMA Style

Xiantao Jiang, F. Richard Yu, Tian Song, Victor C. M. Leung. Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-21.

Chicago/Turabian Style

Xiantao Jiang; F. Richard Yu; Tian Song; Victor C. M. Leung. 2021. "Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-21.

Journal article
Published: 18 March 2021 in Computer Networks
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With the ambitious plans of renewal and expansion of industrialization in many countries, the efficiency, agility and cost savings potentially resulting from the application of industrial Internet of Things (IIoT) are drawing attentions. Although blockchain and machine learning technologies (especially deep learning and deep reinforcement learning) may provide the next promising use case for IIoT, they are working in an adversarial way to some extent. While blockchain facilitates the data collection that is critical for machine learning under the premise of data regulation rules like privacy protection, it may suffer from privacy leakage due to big data analytics with the help of machine learning. To make machine learning and blockchain useful and practical for diversified services in industrial sectors, it is of paramount importance to have a comprehensive understanding of the development of both technologies in the context of IIoT. To this end, in this paper we summarize and analyze the applications of blockchain and machine learning in IIoT from three important aspects, i.e., consensus mechanism, storage and communication. This survey provides a deeper understanding on the security and privacy risks of the key components of a blockchain from the perspective of machine learning, which is useful in the design of practical blockchain solutions for IIoT. In addition, we provide useful guidance for future research in the area by identifying interesting open problems that need to be addressed before large-scale deployments of IIoT applications are practicable.

ACS Style

Yulei Wu; Zehua Wang; Yuxiang Ma; Victor C.M. Leung. Deep reinforcement learning for blockchain in industrial IoT: A survey. Computer Networks 2021, 191, 108004 .

AMA Style

Yulei Wu, Zehua Wang, Yuxiang Ma, Victor C.M. Leung. Deep reinforcement learning for blockchain in industrial IoT: A survey. Computer Networks. 2021; 191 ():108004.

Chicago/Turabian Style

Yulei Wu; Zehua Wang; Yuxiang Ma; Victor C.M. Leung. 2021. "Deep reinforcement learning for blockchain in industrial IoT: A survey." Computer Networks 191, no. : 108004.

Journal article
Published: 10 March 2021 in IEEE Transactions on Automation Science and Engineering
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The studies of Unmanned Air/Ground Vehicle (UAV/UGV) cooperative detection systems have received much attention due to their wide applications in the disaster rescue, target tracking, intelligent surveillance, and automatical package delivery missions. UAVs provide a broad view and have a fast speed in the air, while UGVs have sufficient load capacity and can serve as repeater stations on the ground. The path planning of a UAV/UGV cooperative system is an important but difficult issue, which aims to plan paths for both the UAVs and the UGVs in the system to cooperatively complete a mission. In this article, we consider the path planning problem of the UAV/UGV cooperative system for illegal urban building detection, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. To solve this problem, we first model the path planning problem as a constraint optimization problem which tries to minimize an overall execution time for completing the illegal urban building detection tasks, and then propose a two-level memetic algorithm (called Two-MA) to solve the path planning problems of both the UAV and the UGV. Experiments on both synthetic and real-world data sets show the superiority of the proposed Two-MA over several states-of-the-art algorithms in solving the path planning problems of the UAV and UGV for illegal urban building detection tasks.

ACS Style

Jianqiang Li; Tao Sun; Xiaopeng Huang; Lijia Ma; Qiuzhen Lin; Jie Chen; Victor C. M. Leung. A Memetic Path Planning Algorithm for Unmanned Air/Ground Vehicle Cooperative Detection Systems. IEEE Transactions on Automation Science and Engineering 2021, PP, 1 -14.

AMA Style

Jianqiang Li, Tao Sun, Xiaopeng Huang, Lijia Ma, Qiuzhen Lin, Jie Chen, Victor C. M. Leung. A Memetic Path Planning Algorithm for Unmanned Air/Ground Vehicle Cooperative Detection Systems. IEEE Transactions on Automation Science and Engineering. 2021; PP (99):1-14.

Chicago/Turabian Style

Jianqiang Li; Tao Sun; Xiaopeng Huang; Lijia Ma; Qiuzhen Lin; Jie Chen; Victor C. M. Leung. 2021. "A Memetic Path Planning Algorithm for Unmanned Air/Ground Vehicle Cooperative Detection Systems." IEEE Transactions on Automation Science and Engineering PP, no. 99: 1-14.

Journal article
Published: 10 March 2021 in IEEE Communications Surveys & Tutorials
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Driven by the quality of experience (QoE) requirement of video streaming applications in the smart city, smart education, immersive service, and connected vehicle scenarios, the existing network poses significant challenges, including ultra-high bandwidth, ultra-large storage, and ultra-low latency requirements, etc. Multi-access edge computing (MEC) is a potential technology, which can provide computation-intensive and caching-intensive services for video streaming applications to satisfy the requirement of QoE. Thus, focusing on video streaming schemes, a comprehensive summary of the state of the art applying MEC to video streaming is surveyed. Firstly, the related overview and background knowledge are reviewed. Secondly, resource allocation issues have been discussed. Thirdly, the enabling technologies for video streaming are summarized by taking account of caching, computing, and networking. Then, a taxonomy of MEC enabled video streaming applications is classified. Finally, challenges and future research directions are given.

ACS Style

Xiantao Jiang; F. Richard Yu; Tian Song; Victor C. M. Leung. A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges. IEEE Communications Surveys & Tutorials 2021, 23, 871 -903.

AMA Style

Xiantao Jiang, F. Richard Yu, Tian Song, Victor C. M. Leung. A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges. IEEE Communications Surveys & Tutorials. 2021; 23 (2):871-903.

Chicago/Turabian Style

Xiantao Jiang; F. Richard Yu; Tian Song; Victor C. M. Leung. 2021. "A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges." IEEE Communications Surveys & Tutorials 23, no. 2: 871-903.

Journal article
Published: 08 March 2021 in IEEE Transactions on Cloud Computing
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Inappropriate service migrations can lead to undesirable situations, such as high traffic overhead, long service latency, and service disruption. In this paper, we propose an application-aware migration algorithm (AMA) with prefetching. In AMA, a mobile device sends a service offloading request to the controller. After receiving this request, the controller determines the initial service cloud where virtual machine (VM) of the service initially operates by considering the application type. In addition, it periodically decides where to migrate VM and prefetch its core part considering the mobility of the mobile device and application type. To minimize the generated traffic volume while satisfying the requirements of the application, a constraint Markov decision process (CMDP) is formulated and its optimal policy is obtained via linear programming. Evaluation results demonstrate that AMA with the optimal policy can reduce the generated traffic volume while satisfying the requirements of the application (i.e., service latency and probability of service disruption).

ACS Style

Haneul Ko; Minho Jo; Victor C.M. Leung. Application-Aware Migration Algorithm with Prefetching in Heterogeneous Cloud Environments. IEEE Transactions on Cloud Computing 2021, PP, 1 -1.

AMA Style

Haneul Ko, Minho Jo, Victor C.M. Leung. Application-Aware Migration Algorithm with Prefetching in Heterogeneous Cloud Environments. IEEE Transactions on Cloud Computing. 2021; PP (99):1-1.

Chicago/Turabian Style

Haneul Ko; Minho Jo; Victor C.M. Leung. 2021. "Application-Aware Migration Algorithm with Prefetching in Heterogeneous Cloud Environments." IEEE Transactions on Cloud Computing PP, no. 99: 1-1.

Journal article
Published: 23 February 2021 in IEEE Transactions on Industrial Informatics
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Wireless virtual reality (VR) is increasingly used in industrial Internet of Things (IIoTs). However, ultra-high viewport rendering demands and excessive terminal energy consumption restrict the application of wireless VR. Pervasive edge computing (PEC) emerges as a promising method for wireless VR. In this paper, we propose an energy-aware resource management scheme for wireless VR-supported IIoTs. To reduce the energy consumption of VR equipments (VEs) while ensuring a smooth immersive VR experience, we formulate the viewport rendering offloading, computing and spectrum resources allocation to be a joint optimization problem, considering content correlation between VEs, fluctuating channel conditions, and VR quality of experience (QoE). By applying dual approximation, the original problem is transformed to be a Markov Decision Process (MDP) and an RL-based online learning algorithm is designed to find the optimal policy. To improve the learning efficiency, the quantum parallelism is integrated into the RL to overcome ``curse of dimensionality". In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme.

ACS Style

Peng Lin; Qingyang Song; Dan Wang; Richard Yu; Lei Guo; Victor Leung. Resource Management for Pervasive-Edge-Computing-Assisted Wireless VR Streaming in Industrial Internet of Things. IEEE Transactions on Industrial Informatics 2021, 17, 7607 -7617.

AMA Style

Peng Lin, Qingyang Song, Dan Wang, Richard Yu, Lei Guo, Victor Leung. Resource Management for Pervasive-Edge-Computing-Assisted Wireless VR Streaming in Industrial Internet of Things. IEEE Transactions on Industrial Informatics. 2021; 17 (11):7607-7617.

Chicago/Turabian Style

Peng Lin; Qingyang Song; Dan Wang; Richard Yu; Lei Guo; Victor Leung. 2021. "Resource Management for Pervasive-Edge-Computing-Assisted Wireless VR Streaming in Industrial Internet of Things." IEEE Transactions on Industrial Informatics 17, no. 11: 7607-7617.

Article
Published: 19 February 2021 in Peer-to-Peer Networking and Applications
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Selfish mining is an attack on the integrity of a blockchain network. Inspired by J. Göbel’s selfish mining model in the presence of propagation delay, we propose a competition model based on spatial Poisson process to study how delay influences the revenue distribution when there are more than one selfish miners. Based on our model, we derive the exact expression of the revenue distribution, and prove that the difference of propagation delays between two selfish miners significantly affects the distribution of mining rewards. Additionally, we find that the required threshold of the mining power for multiple selfish miners is larger than that for a single one. Finally, we discuss how the propagation delay impacts the state transfer machine of each selfish miner.

ACS Style

Heli Wang; Qiao Yan; Victor C. M. Leung. The impact of propagation delay to different selfish miners in proof-of-work blockchains. Peer-to-Peer Networking and Applications 2021, 14, 2735 -2742.

AMA Style

Heli Wang, Qiao Yan, Victor C. M. Leung. The impact of propagation delay to different selfish miners in proof-of-work blockchains. Peer-to-Peer Networking and Applications. 2021; 14 (5):2735-2742.

Chicago/Turabian Style

Heli Wang; Qiao Yan; Victor C. M. Leung. 2021. "The impact of propagation delay to different selfish miners in proof-of-work blockchains." Peer-to-Peer Networking and Applications 14, no. 5: 2735-2742.

Review
Published: 17 February 2021 in Future Internet
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Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

ACS Style

Yao Du; Zehua Wang; Victor Leung. Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues. Future Internet 2021, 13, 48 .

AMA Style

Yao Du, Zehua Wang, Victor Leung. Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues. Future Internet. 2021; 13 (2):48.

Chicago/Turabian Style

Yao Du; Zehua Wang; Victor Leung. 2021. "Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues." Future Internet 13, no. 2: 48.

Journal article
Published: 12 February 2021 in IEEE Transactions on Communications
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The combination of non-orthogonal multiple access (NOMA) and multi-access edge computing (MEC) can significantly improve the system performance including communication coverage, spectrum efficiency, etc. In this paper, we focus on energy-efficient resource allocation for a multi-user multi-BS NOMA-MEC network with imperfect channel state information (CSI), where each user can upload its tasks to multiple base stations (BSs) for remote executions. We propose an optimization scheme, including task assignment, power allocation and user association, to minimize energy consumption. Specifically, we transform the probabilistic problem into a non-probabilistic one. To efficiently solve this nonconvex energy minimization problem, we first investigate the one-user two-BS case and derive the optimal closed-form expressions of task assignment and power allocation via the bilevel programming method. Subsequently, based on the derived optimal solution, we propose a low complexity algorithm for the user association in the multi-user multi-BS scenario. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme and the identical results with lower complexity from the exhaustive search with the small number of BSs.

ACS Style

Fang Fang; Kaidi Wang; Zhiguo Ding; Victor C. M. Leung. Energy-Efficient Resource Allocation for NOMA-MEC Networks With Imperfect CSI. IEEE Transactions on Communications 2021, 69, 3436 -3449.

AMA Style

Fang Fang, Kaidi Wang, Zhiguo Ding, Victor C. M. Leung. Energy-Efficient Resource Allocation for NOMA-MEC Networks With Imperfect CSI. IEEE Transactions on Communications. 2021; 69 (5):3436-3449.

Chicago/Turabian Style

Fang Fang; Kaidi Wang; Zhiguo Ding; Victor C. M. Leung. 2021. "Energy-Efficient Resource Allocation for NOMA-MEC Networks With Imperfect CSI." IEEE Transactions on Communications 69, no. 5: 3436-3449.

Journal article
Published: 01 February 2021 in IEEE Transactions on Communications
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Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell network, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes.

ACS Style

Haijun Zhang; Yanan Duan; Keping Long; Victor C. M. Leung. Energy Efficient Resource Allocation in Terahertz Downlink NOMA Systems. IEEE Transactions on Communications 2021, 69, 1375 -1384.

AMA Style

Haijun Zhang, Yanan Duan, Keping Long, Victor C. M. Leung. Energy Efficient Resource Allocation in Terahertz Downlink NOMA Systems. IEEE Transactions on Communications. 2021; 69 (2):1375-1384.

Chicago/Turabian Style

Haijun Zhang; Yanan Duan; Keping Long; Victor C. M. Leung. 2021. "Energy Efficient Resource Allocation in Terahertz Downlink NOMA Systems." IEEE Transactions on Communications 69, no. 2: 1375-1384.

Journal article
Published: 19 January 2021 in IEEE Wireless Communications Letters
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This letter proposes non-orthogonal multiple access (NOMA) based coordinated direct and relay transmission (CDRT) and hybrid multiple access (HMA) protocols for a general CDRT system, where a base station directly serves cell-center users (CCUs), while it communicates with cell-edge users (CEUs) via a relay. The ergodic sum capacity (ESC), capacity scaling and the number of successive interference cancellation (SIC) operations are derived correspondingly. If the number of CCUs is less than that of CEUs and the interference level of imperfect SIC is small, the proposed NOMA-based CDRT can achieve much better ESC than HMA. Otherwise, the proposed HMA achieves a better performance-complexity tradeoff. Numerical results verify the effectiveness and superiority of the proposed protocols.

ACS Style

Yao Xu; Julian Cheng; Gang Wang; Victor C. M. Leung. Coordinated Direct and Relay Transmission for Multiuser Networks: NOMA or Hybrid Multiple Access? IEEE Wireless Communications Letters 2021, 10, 976 -980.

AMA Style

Yao Xu, Julian Cheng, Gang Wang, Victor C. M. Leung. Coordinated Direct and Relay Transmission for Multiuser Networks: NOMA or Hybrid Multiple Access? IEEE Wireless Communications Letters. 2021; 10 (5):976-980.

Chicago/Turabian Style

Yao Xu; Julian Cheng; Gang Wang; Victor C. M. Leung. 2021. "Coordinated Direct and Relay Transmission for Multiuser Networks: NOMA or Hybrid Multiple Access?" IEEE Wireless Communications Letters 10, no. 5: 976-980.

Journal article
Published: 13 January 2021 in IEEE Transactions on Vehicular Technology
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The Internet of vehicles (IoV) provides strong support for ensuring the diversification of urban transportation as the core of next generation intelligent transportation. However, with the rapid growth of vehicle numbers and mobile data, traditional IoV fails to meet the real-time and reliable communication requirements of modern intelligent transportation due to its singleness and low flexibility. In this paper, we study the resource management issues in the IoV, which aim to optimize the energy efficiency of the system. Meanwhile, a non-orthogonal multiple access (NOMA)-based fog computing vehicular (FCV) network architecture is proposed. By splitting the resource management problem into two subproblems of subchannel and power allocation, chemical reaction optimization (CRO) algorithm and real-coded chemical reaction optimization (RCCRO) algorithm are utilized to solve subchannel and power allocation problem, respectively. Also fog computing (FC) technology is introduced to improve local storage and computing capabilities in IoV. Simulation results prove the effectiveness of the proposed scheme.

ACS Style

Yupei Liu; Haijun Zhang; Keping Long; Huan Zhou; Victor C. M. Leung. Fog Computing Vehicular Network Resource Management Based on Chemical Reaction Optimization. IEEE Transactions on Vehicular Technology 2021, 70, 1770 -1781.

AMA Style

Yupei Liu, Haijun Zhang, Keping Long, Huan Zhou, Victor C. M. Leung. Fog Computing Vehicular Network Resource Management Based on Chemical Reaction Optimization. IEEE Transactions on Vehicular Technology. 2021; 70 (2):1770-1781.

Chicago/Turabian Style

Yupei Liu; Haijun Zhang; Keping Long; Huan Zhou; Victor C. M. Leung. 2021. "Fog Computing Vehicular Network Resource Management Based on Chemical Reaction Optimization." IEEE Transactions on Vehicular Technology 70, no. 2: 1770-1781.