Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
Intelligent load balancing techniques in software defined networks: A survey
In the current technology driven era, the use of devices that connect to the internet has
increased significantly. Consequently, there has been a significant increase in internet …
increased significantly. Consequently, there has been a significant increase in internet …
Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks
In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly
optimize the content placement and content delivery in the vehicular edge computing and …
optimize the content placement and content delivery in the vehicular edge computing and …
Ai-based mobile edge computing for iot: Applications, challenges, and future scope
New technology is needed to meet the latency and bandwidth issues present in cloud
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …
A survey of networking applications applying the software defined networking concept based on machine learning
The main task of future networks is to build, as much as possible, intelligent networking
architectures for intellectualization, activation, and customization. Software-defined …
architectures for intellectualization, activation, and customization. Software-defined …
Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT
X Guo, H Lin, Z Li, M Peng - IEEE Internet of things journal, 2019 - ieeexplore.ieee.org
Recently, with the proliferation of communication devices, Internet of Things (IoT) has
become an emerging technology which facilitates massive devices to be enabled with …
become an emerging technology which facilitates massive devices to be enabled with …
Deep reinforcement learning for communication flow control in wireless mesh networks
Wireless mesh network (WMN) is one of the most promising technologies for Internet of
Things (IoT) applications because of its self-adaptive and self-organization nature. To meet …
Things (IoT) applications because of its self-adaptive and self-organization nature. To meet …
Machine learning for next‐generation intelligent transportation systems: A survey
T Yuan, W da Rocha Neto… - Transactions on …, 2022 - Wiley Online Library
Intelligent transportation systems, or ITS for short, includes a variety of services and
applications such as road traffic management, traveler information systems, public transit …
applications such as road traffic management, traveler information systems, public transit …
Deep reinforcement learning-based routing on software-defined networks
With an exponential increase in network traffic demands requiring quality of services, the
need for routing optimization has become more prominent. Recently, the advent of software …
need for routing optimization has become more prominent. Recently, the advent of software …
A survey and comparative evaluation of actor‐critic methods in process control
D Dutta, SR Upreti - The Canadian Journal of Chemical …, 2022 - Wiley Online Library
Actor‐critic (AC) methods have emerged as an important class of reinforcement learning
(RL) paradigm that enables model‐free control by acting on a process and learning from the …
(RL) paradigm that enables model‐free control by acting on a process and learning from the …