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Yan R, Gu Y, Zhang Z, Jiao S. Vehicle Trajectory Prediction Method for Task Offloading in Vehicular Edge Computing. Sensors (Basel) 2023; 23:7954. [PMID: 37766013 PMCID: PMC10536581 DOI: 10.3390/s23187954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 09/15/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023]
Abstract
Real-time computation tasks in vehicular edge computing (VEC) provide convenience for vehicle users. However, the efficiency of task offloading seriously affects the quality of service (QoS). The predictive-mode task offloading is limited by computation resources, storage resources and the timeliness of vehicle trajectory data. Meanwhile, machine learning is difficult to deploy on edge servers. In this paper, we propose a vehicle trajectory prediction method based on the vehicle frequent pattern for task offloading in VEC. First, in the initialization stage, a T-pattern prediction tree (TPPT) is constructed based on the historical vehicle trajectory data. Secondly, when predicting the vehicle trajectory, the vehicle frequent itemset with the largest vehicle trajectory support is found in the vehicle frequent itemset of the TPPT. Finally, in the update stage, the TPPT is updated in real time with the predicted vehicle trajectory results. Meanwhile, based on the proposed prediction method, the strategies of task offloading and optimization algorithm are designed to minimize energy consumption with time constraints. The experiments are carried out on real-vehicle datasets and the Capital Bikeshare datasets. The results show that compared with the baseline T-pattern method, the accuracy of the prediction method is improved by more than 10% and the prediction efficiency is improved by more than 6.5 times. The vehicle trajectory prediction method based on the vehicle frequent pattern has high accuracy and prediction efficiency, which can solve the problem of vehicle trajectory prediction for task offloading.
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Affiliation(s)
- Ruibin Yan
- College of Information and Cyber Security, People's Public Security University of China, Beijing 102600, China
| | - Yijun Gu
- College of Information and Cyber Security, People's Public Security University of China, Beijing 102600, China
| | - Zeyu Zhang
- College of Information and Cyber Security, People's Public Security University of China, Beijing 102600, China
| | - Shouzhong Jiao
- College of Information and Cyber Security, People's Public Security University of China, Beijing 102600, China
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2
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Samir R, El-Hennawy H, Elbadawy H. Cluster-Based Multi-User Multi-Server Caching Mechanism in Beyond 5G/6G MEC. Sensors (Basel) 2023; 23:996. [PMID: 36679793 PMCID: PMC9861789 DOI: 10.3390/s23020996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/05/2023] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
The work on perfecting the rapid proliferation of wireless technologies resulted in the development of wireless modeling standards, protocols, and control of wireless manipulators. Several mobile communication technology applications in different fields are dramatically revolutionized to deliver more value at less cost. Multiple-access Edge Computing (MEC) offers excellent advantages for Beyond 5G (B5G) and Sixth-Generation (6G) networks, reducing latency and bandwidth usage while increasing the capability of the edge to deliver multiple services to end users in real time. We propose a Cluster-based Multi-User Multi-Server (CMUMS) caching algorithm to optimize the MEC content caching mechanism and control the distribution of high-popular tasks. As part of our work, we address the problem of integer optimization of the content that will be cached and the list of hosting servers. Therefore, a higher direct hit rate will be achieved, a lower indirect hit rate will be achieved, and the overall time delay will be reduced. As a result of the implementation of this system model, maximum utilization of resources and development of a completely new level of services and innovative approaches will be possible.
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Affiliation(s)
- Rasha Samir
- Department of Electronics and Communications, Faculty of Engineering, Ain Shams University, Cairo 11566, Egypt
| | - Hadia El-Hennawy
- Department of Electronics and Communications, Faculty of Engineering, Ain Shams University, Cairo 11566, Egypt
| | - Hesham Elbadawy
- Network Planning Department, National Telecommunications Institute, Cairo 11768, Egypt
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Zhang X, Tian S, Liu Y, Cao Z. User location-aware edge services selection based on generative adversarial network and improved ant colony algorithm. APPL INTELL. [DOI: 10.1007/s10489-022-04093-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Jain V, Kumar B, Gupta A. Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment. Journal of King Saud University - Computer and Information Sciences 2022. [DOI: 10.1016/j.jksuci.2022.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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He W, Wang Y, Zhou M, Wang B. A novel parameters correction and multivariable decision tree method for edge computing enabled HGR system. Neurocomputing 2022; 487:203-13. [DOI: 10.1016/j.neucom.2021.08.147] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Jahandar S, Kouhalvandi L, Shayea I, Ergen M, Azmi MH, Mohamad H. Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks. Sensors (Basel) 2022; 22:2692. [PMID: 35408309 PMCID: PMC9002615 DOI: 10.3390/s22072692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/18/2022] [Accepted: 03/24/2022] [Indexed: 06/14/2023]
Abstract
Multi-access edge computing (MEC) is a key technology in the fifth generation (5G) of mobile networks. MEC optimizes communication and computation resources by hosting the application process close to the user equipment (UE) in network edges. The key characteristics of MEC are its ultra-low latency response and real-time applications in emerging 5G networks. However, one of the main challenges in MEC-enabled 5G networks is that MEC servers are distributed within the ultra-dense network. Hence, it is an issue to manage user mobility within ultra-dense MEC coverage, which causes frequent handover. In this study, our purposed algorithms include the handover cost while having optimum offloading decisions. The contribution of this research is to choose optimum parameters in optimization function while considering handover, delay, and energy costs. In this study, it assumed that the upcoming future tasks are unknown and online task offloading (TO) decisions are considered. Generally, two scenarios are considered. In the first one, called the online UE-BS algorithm, the users have both user-side and base station-side (BS) information. Because the BS information is available, it is possible to calculate the optimum BS for offloading and there would be no handover. However, in the second one, called the BS-learning algorithm, the users only have user-side information. This means the users need to learn time and energy costs throughout the observation and select optimum BS based on it. In the results section, we compare our proposed algorithm with recently published literature. Additionally, to evaluate the performance it is compared with the optimum offline solution and two baseline scenarios. The simulation results indicate that the proposed methods outperform the overall system performance.
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Affiliation(s)
- Saeid Jahandar
- Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul 34467, Turkey; (S.J.); (M.E.)
| | - Lida Kouhalvandi
- Department of Electrical and Electronics Engineering, Dogus University, Istanbul 34775, Turkey;
| | - Ibraheem Shayea
- Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul 34467, Turkey; (S.J.); (M.E.)
| | - Mustafa Ergen
- Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul 34467, Turkey; (S.J.); (M.E.)
| | - Marwan Hadri Azmi
- School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia;
| | - Hafizal Mohamad
- Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Malaysia;
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Hasan T, Ahmad F, Rizwan M, Alshammari N, Alanazi SA, Hussain I, Naseem S, Gong D. Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework. Computational Intelligence and Neuroscience 2022; 2022:1-17. [PMID: 35035461 PMCID: PMC8759837 DOI: 10.1155/2022/6138434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/01/2021] [Accepted: 11/03/2021] [Indexed: 11/25/2022]
Abstract
Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm's training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM's Quantum Experience is considered to simulate the quantum phenomena.
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On Cost Aware Heterogeneous Cloudlet Deployment for Mobile Edge Computing: . International Journal of Information Technology and Web Engineering 2022; 17:0-0. [DOI: 10.4018/ijitwe.297968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Edge computing undertakes downlink cloud services and uplink terminal computing tasks, data interaction latency and network transmission cost are thus significantly reduced. Although a lot of research has been conducted in mobile edge computing (MEC), which assumed that all homogeneous cloudlets are placed in WMAN and user mobility is also ignored, little attention is paid to how to place heterogeneous cloudlets in wireless metropolitan area network (WMAN) to minimize the deployment cost of cloudlets. Meanwhile, the method of selecting an optimal access point (AP) for deployment, modeling and heuristic algorithm (HA) needs to be improved. Therefore, this paper design a new heterogeneous cloudlet deployment model considering the quality of service (QoS) and mobility of users, and the Improved Heuristic Algorithm (IHA) is proposed to minimize cloudlet deployment cost. The extensive simulations demonstrate that IHA is more efficient than HA and the designed model is superior to the existing work.
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Zhao Z, Pacheco L, Santos H, Liu M, Maio AD, Rosari D, Cerqueira E, Braun T, Cao X. Predictive UAV Base Station Deployment and Service Offloading With Distributed Edge Learning. IEEE Trans Netw Serv Manage 2021. [DOI: 10.1109/tnsm.2021.3123216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Masri A, Al-Jabi M. Toward IoT fog computing-enabled system energy consumption modeling and optimization by adaptive TCP/IP protocol. PeerJ Comput Sci 2021; 7:e653. [PMID: 34435098 PMCID: PMC8356661 DOI: 10.7717/peerj-cs.653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/07/2021] [Indexed: 06/13/2023]
Abstract
Nowadays, due to the fast-growing wireless technologies and delay-sensitive applications, Internet of things (IoT) and fog computing will assemble the paradigm Fog of IoT. Since the spread of fog computing, the optimum design of networking and computing resources over the wireless access network would play a vital role in the empower of computing-intensive and delay-sensitive applications under the extent of the energy-limited wireless Fog of IoT. Such applications consume considarable amount of energy when sending and receiving data. Although there many approaches to attain energy efficiency already exist, few of them address the TCP protocol or the MTU size. In this work, we present an effective model to reduce energy consumption. Initially, we measured the consumed energy based on the actual parameters and real traffic for different values of MTU. After that, the work is generalized to estimate the energy consumption for the whole network for different values of its parameters. The experiments were made on different devices and by using different techniques. The results show clearly an inverse proportional relationship between the MTU size and the amount of the consumed energy. The results are promising and can be merged with the existing work to get the optimal solution to reduce the energy consumption in IoT and wireless networks.
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Affiliation(s)
- Aladdin Masri
- Computer Engineering Department, An-Najah National University, Nablus, Palestine
| | - Muhannad Al-Jabi
- Computer Engineering Department, An-Najah National University, Nablus, Palestine
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Narayana VCL, Moharir S, Karamchandani N. On Renting Edge Resources for Service Hosting. ACM Trans Model Perform Eval Comput Syst 2021. [DOI: 10.1145/3478433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The rapid proliferation of shared edge computing platforms has enabled application service providers to deploy a wide variety of services with stringent latency and high bandwidth requirements. A key advantage of these platforms is that they provide pay-as-you-go flexibility by charging clients in proportion to their resource usage through short-term contracts. This affords the client significant cost-saving opportunities by dynamically deciding when to host its service on the platform, depending on the changing intensity of requests. A natural policy for our setting is the Time-To-Live (TTL) policy. We show that TTL performs poorly both in the adversarial arrival setting, i.e., in terms of the competitive ratio, and for i.i.d. stochastic arrivals with low arrival rates, irrespective of the value of the TTL timer. We propose an online policy called RetroRenting (RR) and characterize its performance in terms of the competitive ratio. Our results show that RR overcomes the limitations of TTL. In addition, we provide performance guarantees for RR for i.i.d. stochastic arrival processes coupled with negatively associated rent cost sequences and prove that it compares well with the optimal online policy. Further, we conduct simulations using both synthetic and real-world traces to compare the performance of RR with the optimal offline and online policies. The simulations show that the performance of RR is near optimal for all settings considered. Our results illustrate the universality of RR.
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Abstract
Internet of Things (IoT) networks dependent on cloud services usually fail in supporting real-time applications as there is no response time guarantees. The fog computing paradigm has been used to alleviate this problem by executing tasks at the edge of the network, where it is possible to provide time bounds. One of the challenging topics in a fog-assisted architecture is to task placement on edge devices in order to obtain a good performance. The process of task mapping into computational devices is known as Service Placement Problem (SPP). In this paper, we present a heuristic algorithm to solve SPP, dubbed as clustering of fog devices and requirement-sensitive service first (SCATTER). We provide simulations using iFogSim toolkit and experimental evaluations using real hardware to verify the feasibility of the SCATTER algorithm by considering a smart home application. We compared the SCATTER with two existing works: edge-ward and cloud-only approaches, in terms of Quality of Service (QoS) metrics. Our experimental results have demonstrated that SCATTER approach has better performance compared with the edge-ward and cloud-only, 42.1% and 60.2% less application response times, 22% and 27.8% less network usage, 45% and 65.7% less average application loop delays, and 2.33% and 3.2% less energy consumption.
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Ge S, Cheng M, He X, Zhou X. A Two-Stage Service Migration Algorithm in Parked Vehicle Edge Computing for Internet of Things. Sensors (Basel) 2020; 20:s20102786. [PMID: 32422954 PMCID: PMC7285760 DOI: 10.3390/s20102786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/02/2020] [Accepted: 05/12/2020] [Indexed: 06/11/2023]
Abstract
Parked vehicle edge computing (PVEC) utilizes both idle resources in parked vehicles (PVs) and roadside units (RSUs) as service providers (SPs) to improve the performance of vehicular internet of things (IoT). However, it is difficult to make optimal service migration decisions in PVEC networks due to the uncertain parking duration and resources heterogeneity of PVs. In this paper, we formulate the service migration of all the vehicles as an optimization problem with the objective of minimizing the average latency. We propose a two-stage service migration algorithm for PVEC networks, which divides the original problem into the service migration between SPs and the serving PV selection in parking lots. The service migration between SPs is transformed to an online problem based on Lyapunov optimization, where the expected parking duration of PVs is utilized. A modified Hungarian algorithm is proposed to select the PVs for migration. A series of simulation experiments based on the real-world vehicle traces are conducted to verify the superior performance of the proposed two-stage service migration (SEA) algorithm as compared with the state-of- art solutions.
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Affiliation(s)
- Shuxin Ge
- Tianjin Key Laboratory of Advanced Networking (TANK), College of Intelligence and Computing, Tianjin University, Tianjin 300350, China; (S.G.); (X.Z.)
| | - Meng Cheng
- School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa 923-1292, Japan;
| | - Xin He
- School of Computerand Information, Anhui Normal University, Wuhu 241001, China
| | - Xiaobo Zhou
- Tianjin Key Laboratory of Advanced Networking (TANK), College of Intelligence and Computing, Tianjin University, Tianjin 300350, China; (S.G.); (X.Z.)
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Fan X, Zheng H, Jiang R, Zhang J. Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching. Sensors (Basel) 2020; 20:s20061582. [PMID: 32178300 PMCID: PMC7361789 DOI: 10.3390/s20061582] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/07/2020] [Accepted: 03/09/2020] [Indexed: 11/16/2022]
Abstract
This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.
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Affiliation(s)
- Xiaoqian Fan
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; (X.F.); (R.J.); (J.Z.)
| | - Haina Zheng
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; (X.F.); (R.J.); (J.Z.)
- State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
- Correspondence:
| | - Ruihong Jiang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; (X.F.); (R.J.); (J.Z.)
- State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
| | - Jinyu Zhang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; (X.F.); (R.J.); (J.Z.)
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