State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

By Zubair Muhammad Fadlullah, Fengxiao Tang, Bomin Mao, Nei Kato, Osamu Akashi, Takeru Inoue, Kimihiro Mizutani. 2017

729 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nei Kato
2. Jiajia Liu
3. Fengxiao Tang
4. Bomin Mao
5. Guan Gui
6. Yuichi Kawamoto
7. Hongji Huang
8. Yiwei Song

In Building and Construction

1. Nei Kato
2. Guan Gui
3. Fengxiao Tang
4. Bomin Mao
5. Jiajia Liu
6. Yiwei Song
7. Hongji Huang
8. Ala Al-Fuqaha

In Computer Networks and Communications

1. Jiajia Liu
2. Nei Kato
3. Fengxiao Tang
4. Bomin Mao
5. Guan Gui
6. F. Yu
7. Yuichi Kawamoto
8. Shree Krishna Sharma

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “Finally, Fadlullah et al. [12] addressed DL approaches in network traffic control systems.”
• A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “Though there are various surveys on ML in networking [18, 61, 82, 142, 246, 339], this survey is purposefully different.”
• Deep Learning for an Effective Nonorthogonal Multiple Access Scheme (2018, positive): “Furthermore, DL has been adopted in traffic control systems, the encouraging performance of which has been demonstrated [22]–[25].”
• Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System (2018, methodology): “Furthermore, deep learning has been applied in traffic control systems to optimize average delay and packet loss rate performance [28]–[30].”
• Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, background): “Furthermore, depending on the objective of how a learning architecture will be utilized, DL architectures can be broadly categorized into [35], [166]: generative, discriminative and hybrid.”
• Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing (2018, background): “I N recent years, deep learning becomes an important methodology in many informatics fields such as vision recognition, natural language processing, and bioinformatics [1] [2].”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[65] A comprehensive survey of deep learning for network traffic control.”
• A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “Another advantage of DL algorithms is their ability to allow deep linking in IoT networks [172].”
• On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control (2018, background): “The survey conducted in [7] demonstrated that there exist different deep learning architectures such as Deep Boltzmann Machines (DBMs), Deep CNNs, and so forth that could be exploited for network traffic control systems.”
• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “In particular, the survey in [9] discusses deep learning approaches for network cybersecurity, the survey in [10] reviews deep learning approaches for network traffic”
• Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “Overall, this survey aims to fill the gaps found in the previous papers [19]–[33], and our contributions are threefold:”
• Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “[33] provided an overview of the state-of-the-art in learning aided network traffic control schemes as well as in deep learning aided intelligent routing strategies, while Nguyen and Armitage [34] focused their attention on the ML techniques conceived for Internet traffic classification.”
• A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “How to apply machine learning techniques to improve the network traffic control has been surveyed in [13].”
• A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “the deep linking of the IoT environment [29].”
• Networking and Communications in Autonomous Driving: A Survey (2019, background): “[222] summarize the most advanced deep learning architectures and algorithms related to network traffic control systems.”
• Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, background): “Surveys about IDS based on learning techniques (not specially for IoT) such as [13], [15], and [24]–[27] have performed comprehensive evaluation for traditional systems.”