Reinforcement learning for adaptive resource allocation in fog RAN for IoT with heterogeneous latency requirements
A Nassar, Y Yilmaz - IEEE Access, 2019 - ieeexplore.ieee.org
In light of the quick proliferation of Internet of things (IoT) devices and applications, fog radio
access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless …
access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless …
Preemptive SDN load balancing with machine learning for delay sensitive applications
A Filali, Z Mlika, S Cherkaoui… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
SDN is a key-enabler to achieve scalability in 5G and Multi-access Edge Computing
networks. To balance the load between distributed SDN controllers, the migration of the data …
networks. To balance the load between distributed SDN controllers, the migration of the data …
Machine learning and deep learning based traffic classification and prediction in software defined networking
AŞR Mohammed, SA Mohammed… - … on Measurements & …, 2019 - ieeexplore.ieee.org
The Internet is constantly growing in size and becoming more complex. The field of
networking is thus continuously progressing to cope with this monumental growth of network …
networking is thus continuously progressing to cope with this monumental growth of network …
Deep reinforcement learning for router selection in network with heavy traffic
The rapid development of wireless communications brings a tremendous increase in the
amount number of data streams and poses significant challenges to the traditional routing …
amount number of data streams and poses significant challenges to the traditional routing …
[HTML][HTML] A machine learning SDN-enabled big data model for IoMT systems
In recent times, health applications have been gaining rapid popularity in smart cities using
the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both …
the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both …
Multiuser resource control with deep reinforcement learning in IoT edge computing
By leveraging the concept of mobile edge computing (MEC), massive amount of data
generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC …
generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC …
Deep reinforcement learning based resource management for DNN inference in industrial IoT
Performing deep neural network (DNN) inference in real time requires excessive network
resources, which poses a big challenge to the resource-limited industrial Internet of things …
resources, which poses a big challenge to the resource-limited industrial Internet of things …
A survey on application of machine learning for Internet of Things
Abstract Internet of Things (IoT) has become an important network paradigm and there are
lots of smart devices connected by IoT. IoT systems are producing massive data and thus …
lots of smart devices connected by IoT. IoT systems are producing massive data and thus …
Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data
Machine (deep) learning-enabled accurate traffic modeling and prediction is an
indispensable part for future big data-driven intelligent cellular networks, since it can help …
indispensable part for future big data-driven intelligent cellular networks, since it can help …
Multiscale network traffic prediction method based on deep echo-state network for internet of things
As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive
role in congestion control, resource allocation, and anomaly detection. The trend of network …
role in congestion control, resource allocation, and anomaly detection. The trend of network …