The DOI matches its Crossref record; 252 of the 266 references it cites were found in a registry or an index.
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems
Abstract
Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In this regard, there are many potential areas where DL can be applied, including online content streaming [285], routing and traffic control [286] [287], etc.”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [287]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
(2014) · Cited by 33,419
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Flagged first, then by citations
14 of the 266 references couldn’t be matched to a known paper.
Research works printed but not linked: 20
- wang, 2016, The Applications of Deep Learning on Traffic Identification
- 2016, Methods and Apparatus for Reinforcement Learning
- 2016, Microsoft Audio Video Indexing Service (MAVIS)
- 2016, The iBrain is Here and It's Already Inside Your Phone
- 2016, The MNIST database
- 2016, Google Unveils Neural Network With ‘Superhuman’ Ability to Determine the Location of Almost Any Image
- landauer, 1987, Learning a minimally structured back propagation network to recognize speech, Proc 9th Annu Conf Cogn Sci Soc, 531
- hyndman, 2016, DataMarket Data Library (TSDL)
- 2016, Wall Street Journal-Based Continuous Speech Recognition (CSR) Corpus
- castelvecchi, 2016, Nature News Deep Learning Boosts Google Translate Tool
- 2016, The Zettabyte EraTrends and Analysis
- russon, 2016, Using Deep Learning Neural Networks to Predict Social Unrest Five Days Before it Happens
- 2016, The Zettabyte Era-Trends and Analysis
- konno, 2011, An adaptive media access control mechanism for cognitive radio, IEICE Trans Commun, j94 b, 253
- song, 2016, DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level, Proc IJCAI, 2618
- rondeau, 2004, Cognitive radios with genetic algorithms: Intelligent control of software defined radios, Software Defined Radio Forum Technical Conference, 3c
- chintala, 2016, Understanding Natural Language with Deep Neural Networks Using Torch
- 2016, WILL API
- 2016, Deep Learning Comp Sheet Deeplearning4j vs Torch vs Theano vs Caffe vs TensorFlow
- 2016, Intel Autonomous Cars
Cited by
Sorted by citations
Items 201–300 of 810
Chen, Y., & Zhou, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105479 · Cited by 61
Neural Network-Based Relay Selection in Two-Way SWIPT-Enabled Cognitive Radio Networks
Zhang, Z., Lu, Y., Huang, Y., & Zhang, P. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2984327 · Cited by 60
Big Data Analytics for Large Scale Wireless Networks: Challenges and OpportunitiesPreprint
Dai, H., Wong, R. C.-W., Wang, H. H., Zheng, Z., & Vasilakos, A. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.08069 · Cited by 60
Detecting Internet Abuse by Analyzing Passive DNS Traffic: A Survey of Implemented Systems
Torabi, S., Boukhtouta, A., Assi, C. M., & Debbabi, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2849614 · Cited by 58
Choudhury, G. L., Lynch, D. F., Thakur, G., & Tse, S. (2018) · Journal of Optical Communications and Networking · doi:10.1364/jocn.10.000d52 · Cited by 58
A Hybrid Data-driven Model for Intrusion Detection in VANET
Bangui, H., Ge, M., & Bühnová, B. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.03.065 · Cited by 58
Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security
Khater, B. S., Wahab, A. W. A., Idris, M. Y. I., Hussain, M. A., Ibrahim, A. A., Amin, M. A., & Shehadeh, H. A. (2021) · Electronics · doi:10.3390/electronics10141633 · Cited by 63
Reinforcement Learning for Cyber-Physical Systems
Liu, X., Xu, H., Liao, W., & Yu, W. (2019) · IEEE International Conference on Industrial Internet (ICII) · doi:10.1109/icii.2019.00063 · Cited by 59
Machine Learning Aided Load Balance Routing Scheme Considering Queue Utilization
Yao, H., Yuan, X., Zhang, P., Wang, J., Jiang, C., & Guizani, M. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2921792 · Cited by 56
Anomaly Detection for Cyber-Security Based on Convolution Neural Network : A survey
Alabadi, M., & Celik, Y. (2020) · 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) · doi:10.1109/hora49412.2020.9152899 · Cited by 55
Predicting Network Flow Characteristics Using Deep Learning and Real-World Network Traffic
Hardegen, C., Pfülb, B., Rieger, S., & Gepperth, A. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3025131 · Cited by 55
Vehicle detection in intelligent transport system under a hazy environment: a survey
Husain, A., Maity, T., & Yadav, R. K. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2018.5351 · Cited by 48
Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network
Alekseeva, D., Stepanov, N., Veprev, A., Sharapova, A., Lohan, E. S., & Ometov, A. (2021) · IEEE Access · doi:10.1109/access.2021.3129850 · Cited by 66
Naeem, M., Pietro, G. D., & Coronato, A. (2021) · Sensors · doi:10.3390/s22010309 · Cited by 56
Abubakar, A. I., Omeke, K. G., Öztürk, M., Hussain, S., & Imran, M. A. (2020) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2020.575065 · Cited by 55
Machine learning-inspired intrusion detection system for IoT: Security issues and future challenges
Ahanger, T. A., Ullah, I., Algamdi, S. A., & Tariq, U. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110265 · Cited by 53
Neural network architecture based on gradient boosting for IoT traffic prediction
López‐Martín, M., Carro, B., & Sánchez-Esguevillas, A. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.060 · Cited by 53
Deep Q-Network Learning Based Downlink Resource Allocation for Hybrid RF/VLC Systems
Shrivastava, S., Chen, B., Chen, C., Wang, H., & Dai, M. (2020) · IEEE Access · doi:10.1109/access.2020.3014427 · Cited by 53
An integrated MPC and deep reinforcement learning approach to trams-priority active signal control
Guo, G., & Wang, Y. (2021) · Control Engineering Practice · doi:10.1016/j.conengprac.2021.104758 · Cited by 57
Fadlullah, Z. M., Tang, F., Mao, B., Liu, J., & Kato, N. (2018) · IEEE Communications Letters · doi:10.1109/lcomm.2018.2875431 · Cited by 52
Self-citationOptical networks management and control: A review and recent challenges
Andriolli, N., Giorgetti, A., Castoldi, P., Cecchetti, G., Cerutti, I., Sambo, N., Sgambelluri, A., Valcarenghi, L., Cugini, F., Martini, B., et al. (2021) · Optical Switching and Networking · doi:10.1016/j.osn.2021.100652 · Cited by 52
Anomaly detection model based on gradient boosting and decision tree for IoT environments security
Douiba, M., Benkirane, S., Guezzaz, A., & Azrour, M. (2022) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-022-00184-3 · Cited by 52
Black-box Adversarial Machine Learning Attack on Network Traffic Classification
Usama, M., Qayyum, A., Qadir, J., & Al‐Fuqaha, A. (2019) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2019.8766505 · Cited by 53
Short-Term Traffic Prediction Based on DeepCluster in Large-Scale Road Networks
Han, L., Zheng, K., Zhao, L., Wang, X., & Shen, X. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2947080 · Cited by 55
A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System
Haider, A., Khan, M. A., Rehman, A., Rahman, M., & Kim, H. S. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.013910 · Cited by 52
Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS
Alsamhi, S. H., Almalki, F. A., Al-Dois, H., Soufiene, B. O., Hassan, J. A., Hawbani, A., Sahal, R., Lee, B., & Saleh, H. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6805151 · Cited by 61
Real-Time Classification of Green Coffee Beans by Using a Convolutional Neural Network
Huang, N.-F., Chou, D., & Lee, C.-A. (2019) · International Conference on Imaging, Signal Processing and Communications (ICISPC) · doi:10.1109/icispc.2019.8935644 · Cited by 51
Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence
Guo, A., & Yuan, C. (2021) · Electronics · doi:10.3390/electronics10060700 · Cited by 51
Ruan, L., Dias, M. P. I., & Wong, E. (2020) · Journal of Optical Communications and Networking · doi:10.1364/jocn.379715 · Cited by 49
AI-Empowered Maritime Internet of Things: A Parallel-Network-Driven Approach
Yang, T., Chen, J., & Zhang, N. (2020) · IEEE Network · doi:10.1109/mnet.011.2000020 · Cited by 50
A Review of Machine Learning Approaches in Synchrophasor Technology
Dhinu Lal, M., & Varadarajan, R. (2023) · IEEE Access · doi:10.1109/access.2023.3263547 · Cited by 48
Fadlullah, Z. M., Mao, B., Tang, F., & Kato, N. (2018) · IEEE Transactions on Computers · doi:10.1109/tc.2018.2874483 · Cited by 48
Self-citationDistributed and Efficient Object Detection via Interactions Among Devices, Edge, and Cloud
Guo, Y., Zou, B., Ren, J., Liu, Q., Zhang, D., & Zhang, Y. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2912703 · Cited by 49
Machine Learning-Based Multipath Routing for Software Defined Networks
Awad, M. K., Ahmed, M. H. H., Almutairi, A. F., & Ahmad, I. (2021) · Journal of Network and Systems Management · doi:10.1007/s10922-020-09583-4 · Cited by 48
Susilo, B., Muis, A., & Sari, R. F. (2025) · Sensors · doi:10.3390/s25020580 · Cited by 70
Shan, N., Cui, X., & Gao, Z. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.05.037 · Cited by 52
Cycle-Based Time-Sensitive and Deterministic Networks: Architecture, Challenges, and Open Issues
Huang, Y., Wang, S., Huang, T., & Liu, Y. (2022) · IEEE Communications Magazine · doi:10.1109/mcom.001.2100865 · Cited by 47
Surface Cracking and Fractal Characteristics of Cement Paste after Exposure to High Temperatures
Li, L., Zhang, Y., Yuqiong, S., Xue, Z., & Cao, M. (2022) · Fractal and Fractional · doi:10.3390/fractalfract6090465 · Cited by 47
Enabling Ultra-Dense UAV-Aided Network with Overlapped Spectrum Sharing: Potential and Approaches
Wang, L., Yang, H., Long, J., Wu, K., & Chen, J. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700464 · Cited by 46
Sahani, M., & Dash, P. K. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3023770 · Cited by 46
Deep Learning Empowered Traffic Offloading in Intelligent Software Defined Cellular V2X Networks
Fan, B. Y., He, Z., Wu, Y., He, J., Chen, Y., & Jiang, L. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3023194 · Cited by 50
Decentralized signal control for multi-modal traffic network: A deep reinforcement learning approach
Yu, J., Laharotte, P.-A., Han, Y., & Leclercq, L. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104281 · Cited by 49
Socially-Motivated Cooperative Mobile Edge Computing
Chen, X., Zhou, Z., Wu, W., Wu, D., & Zhang, J. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700354 · Cited by 45
A Supervised Learning Based QoS Assurance Architecture for 5G Networks
Zhu, G., Zan, J., Yang, Y., & Qi, X. (2019) · IEEE Access · doi:10.1109/access.2019.2907142 · Cited by 45
A Tensor Based Deep Learning Technique for Intelligent Packet Routing
Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · Globecom · doi:10.1109/glocom.2017.8254036 · Cited by 46
Self-citationIntelligent SDN Traffic Classification Using Deep Learning: Deep-SDN
Malik, A., de Fréin, R., Al-Zeyadi, M., & Andreu-Pérez, J. (2020) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci49374.2020.9145971 · Cited by 47
Yang, Y., Gao, Z., Ma, Y., Cao, B., & He, D. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3001340 · Cited by 44
DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization
Li, L., Ota, K., & Dong, M. (2019) · IEEE Network · doi:10.1109/mnet.2018.1700394 · Cited by 43
Alhaidari, F. A., Almotiri, S. H., Ghamdi, M. A. A., Khan, M. A., Rehman, A., Abbas, S., Khan, K. M., & Rahman, A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.013303 · Cited by 43
Guo, A., Yuan, C., He, G., & Xu, L. (2018) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit.2018.8587985 · Cited by 43
Zhang, Y., Zhou, Y., Lu, H., & Fujita, H. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2976053 · Cited by 42
Hu, J., Zhan, X., Wu, J., & Yan, H. (2020) · IET Control Theory and Applications · doi:10.1049/iet-cta.2019.0578 · Cited by 42
Machine Learning for Service Migration: A Survey
Toumi, N., Bagaa, M., & Ksentini, A. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3273121 · Cited by 48
AbdelMoniem, M., Gasser, S. M., El-Mahallawy, M. S., Fakhr, M. W., & Soliman, A. (2019) · Applied Sciences · doi:10.3390/app9153022 · Cited by 44
A Forecasting Approach to Improve Control and Management for 5G Networks
Ferreira, D., Reis, A. B., Senna, C. R., & Sargento, S. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3056222 · Cited by 42
Samanta, R. K., Sadhukhan, B., Samaddar, H., Sarkar, S., Koner, C., & Ghosh, M. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12114 · Cited by 47
Cui, T., Yang, R., Fang, C., & Yu, S. (2023) · Symmetry · doi:10.3390/sym15010217 · Cited by 41
Message-Passing Neural Networks Learn Little’s Law
Rusek, K., & Chołda, P. (2018) · IEEE Communications Letters · doi:10.1109/lcomm.2018.2886259 · Cited by 41
Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems
Bao, X., Feng, W., Zheng, J., & Li, J. (2020) · IEEE Access · doi:10.1109/access.2020.2967402 · Cited by 41
Autonomous PEV Charging Scheduling Using Dyna-Q Reinforcement Learning
Wang, F., Gao, J., Li, M., & Zhao, L. Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3026004 · Cited by 41
A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0
Ghosh, S., Dagiuklas, T., Iqbal, M., & Wang, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3141403 · Cited by 40
Kumar, G., & Alqahtani, H. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.020724 · Cited by 51
Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture
Lee, K., Silva, B. N., & Han, K. (2020) · Applied Sciences · doi:10.3390/app10041544 · Cited by 40
Explainable, automated urban interventions to improve pedestrian and vehicle safety
Bustos, C., Rhoads, D., Solé‐Ribalta, A., Masip, D., Arenas, À., Lapedriza, À., & Borge‐Holthoefer, J. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103018 · Cited by 43
Dong, T., Qi, Q., Wang, J., Liu, A. X., Sun, H., Zhuang, Z., & Liao, J. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3077249 · Cited by 40
Bhattacharyya, A., Nambiar, S. M., Ojha, R., Gyaneshwar, A., Chadha, U., & Srinivasan, K. (2023) · International Journal of Satellite Communications and Networking · doi:10.1002/sat.1482 · Cited by 46
Deep Neural Networks for Network Routing
Reis, J., Rocha, M., Phan, T. K., Griffin, D. P., Le, F., & Miguel Rio, C. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851733 · Cited by 39
A Waveform Parameter Assignment Framework for 6G With the Role of Machine Learning
Yazar, A., & Arslan, H. (2020) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2020.2992502 · Cited by 40
Reinforcement Learning for Efficient and Fair Coexistence Between LTE-LAA and Wi-Fi
Han, M., Khairy, S., Cai, L. X., Cheng, Y., & Zhang, R. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2994525 · Cited by 40
On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis
Marzouk, F., Barraca, J. P., & Radwan, A. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3003261 · Cited by 39
A Federated Learning Approach to Routing in Challenged SDN-Enabled Edge Networks
Sacco, A., Esposito, F., & Marchetto, G. (2020) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft48620.2020.9165506 · Cited by 40
Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey
Abbasi, M., Shahraki, A., Piran, M. J., & Taherkordi, A. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104234 · Cited by 39
DeepComNet: Performance evaluation of network topologies using graph-based deep learning
Geyer, F. (2018) · Performance Evaluation · doi:10.1016/j.peva.2018.12.003 · Cited by 38
Secure Transmission via Power Allocation in NOMA-UAV Networks With Circular Trajectory
Chen, X., Yang, Z., Zhao, N., Chen, Y., Wang, J., Ding, Z., & Yu, F. R. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3005120 · Cited by 38
LOS and NLOS identification in real indoor environment using deep learning approach
Olejniczak, A., Błaszkiewicz, O., Cwalina, K. K., Rajchowski, P., & Sadowski, J. (2023) · Digital Communications and Networks · doi:10.1016/j.dcan.2023.05.009 · Cited by 37
Deep-EERA: DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks
Ahmad, S., Zhang, J., Nauman, A., Khan, A. S., Abbas, K., & Hayat, B. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010071 · Cited by 37
Heart disease classification through crow intelligence optimization-based deep learning approach
Dubey, A. K., Sinhal, A., & Sharma, R. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01445-x · Cited by 36
Location and time embedded feature representation for spatiotemporal traffic prediction
Li, W., Liu, X., Tao, W., Zhang, L., Zou, J., Pan, Y., & Pan, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122449 · Cited by 39
Improved Network Traffic Classification Using Ensemble Learning
Possebon, I. P., Silva, A. S., Granville, L. Z., Schaeffer-Filho, A. E., & Marnerides, A. K. (2019) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc47284.2019.8969637 · Cited by 37
Enabling AI in future wireless networks: a data life cycle perspective
Nguyen, D. C., Cheng, P., Ding, M., López‐Pérez, D., Pathirana, P. N., Li, J., Seneviratne, A. P., Li, Y., & Poor, H. V. (2024) · Figshare · Cited by 36
Dynamic Social-Aware Computation Offloading for Low-Latency Communications in IoT
Gao, Y., Tang, W., Wu, M., Yang, P., & Dan, L. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2909299 · Cited by 35
Optimization of traffic network signal timing using decentralized genetic algorithm
Tan, M. K., Chuo, H. S. E., Chin, R. K. Y., Yeo, K. B., & Teo, K. T. K. (2017) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis.2017.8239034 · Cited by 35
Li, M., Zhao, X., Liang, H. P., & Hu, F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2911544 · Cited by 34
Adaptive Deep Learning Aided Digital Predistorter Considering Dynamic Envelope
Sun, J., Wang, J., Guo, L., Yang, J., & Gui, G. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2974506 · Cited by 37
Bouchmal, O., Cimoli, B., Stabile, R., Olmos, J. J. V., & Monroy, I. T. (2023) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2023.1220227 · Cited by 37
Zhang, Y., & Kantarcı, B. (2019) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2019.00014 · Cited by 33
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems
Munir, M. S., Tran, N. H. K., Saad, W., & Hong, C. S. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3057960 · Cited by 37
A Machine Learning-Based Defensive Alerting System Against Reckless Driving in Vehicular Networks
Zhang, L., Yan, L., Fang, Y. M., Fang, X., & Huang, X. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2945398 · Cited by 32
Subcarrier Assignment Schemes Based on Q-Learning in Wideband Cognitive Radio Networks
Zhou, Y., Zhou, F., Wu, Y., Hu, R. Q., & Wang, Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2953809 · Cited by 32
Image Processing in Intelligent Traffic Management
(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.b1040.0782s419 · Cited by 32
Controlling highway toll stations using deep learning, queuing theory, and differential evolution
Petrović, A., Nikolić, M., Bugarić, U., Delibašić, B., & Lió, P. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105683 · Cited by 32
Delay-Optimal Traffic Engineering through Multi-agent Reinforcement Learning
Pinyoanuntapong, P., Lee, M., & Wang, P. (2019) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2019.8845154 · Cited by 32
Tang, F., Mao, B., Fadlullah, Z. M., Liu, J., & Kato, N. (2019) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2019.2929935 · Cited by 32
Self-citationCapacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting
Mahmood, A., Kiah, M. L. M., Z’aba, M. R., Qureshi, A. N., Kassim, M. S. S., Azizul, Z. H., Kakarla, J., Amiri, I. S., & Azzuhri, S. R. (2020) · IEEE Access · doi:10.1109/access.2020.2970224 · Cited by 31
Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications
Zhang, T., Cong, S., & Zhou, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2981130 · Cited by 32
Breaking Wireless Propagation Environmental Uncertainty With Deep Learning
Morocho-Cayamcela, M. E., Maier, M., & Lim, W. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2986202 · Cited by 31
Etengu, R., Tan, S. C., Lee, C. K., Abbou, F. M., & Chuah, T. C. (2020) · IEEE Access · doi:10.1109/access.2020.3022291 · Cited by 35
Huang, N., Chou, D., Lee, C., Wu, F., Chuang, A.-C., Chen, Y., & Tsai, Y. (2020) · IET Smart Cities · doi:10.1049/iet-smc.2020.0068 · Cited by 31
Chen, Q., Meng, W., Li, S., Li, C., & Chen, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3059022 · Cited by 31
Zhang, A., Sun, M., Wang, J., Li, Z., Cheng, Y., & Wang, C. (2021) · Applied Sciences · doi:10.3390/app11104436 · Cited by 31
Metadata
- Authors
- Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K.
- Affiliations
- Tohoku University (JP)
- NTT (Japan) (JP)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2017
- Volume
- 19
- Issue
- 4
- Pages
- 2432-2455
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2617931713
- Cited by
- 810
- References
- 266
- Citations by year
- 2026: 25 · 2025: 46 · 2024: 71 · 2023: 69 · 2022: 82 · 2021: 122 · 2020: 170 · 2019: 135 · 2018: 80 · 2017: 10
- FWCI
- 71.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (16H05858)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- deep learning, network traffic management, intelligent routing, autonomous network management, deep learning architectures, network traffic growth, packet-switched networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review