The applicability of reinforcement learning methods in the development of industry 4.0 applications
Reinforcement learning (RL) methods can successfully solve complex optimization
problems. Our article gives a systematic overview of major types of RL methods, their …
problems. Our article gives a systematic overview of major types of RL methods, their …
A DQN-based frame aggregation and task offloading approach for edge-enabled IoMT
The rapid expansion of wearable medical devices and health data of Internet of Medical
Things (IoMT) poses new challenges to the high Quality of Service (QoS) of intelligent health …
Things (IoMT) poses new challenges to the high Quality of Service (QoS) of intelligent health …
[HTML][HTML] Performance analysis of multihop full-duplex NOMA systems with imperfect interference cancellation and near-field path-loss
Outage probability (OP) and potential throughput (PT) of multihop full-duplex (FD)
nonorthogonal multiple access (NOMA) systems are addressed in the present paper. More …
nonorthogonal multiple access (NOMA) systems are addressed in the present paper. More …
Distributed Reinforcement Learning for scalable wireless medium access in IoTs and sensor networks
This paper presents a distributed Reinforcement Learning (RL) framework for synthesizing
wireless network protocols in IoT and Wireless Sensor Networks with low-complexity …
wireless network protocols in IoT and Wireless Sensor Networks with low-complexity …
One protocol to rule them all: Wireless {Network-on-Chip} using deep reinforcement learning
Wireless Network-on-Chip (NoC) has emerged as a promising solution to scale chip multi-
core processors to hundreds and thousands of cores. The broadcast nature of a wireless …
core processors to hundreds and thousands of cores. The broadcast nature of a wireless …
[HTML][HTML] An Improved CSMA/CA Protocol Anti-Jamming Method Based on Reinforcement Learning
Z Ming, X Liu, X Yang, M Wang - Electronics, 2023 - mdpi.com
The CSMA/CA algorithm uses the binary backoff mechanism to solve the multi-user channel
access problem, but this mechanism is vulnerable to jamming attacks. Existing research …
access problem, but this mechanism is vulnerable to jamming attacks. Existing research …
A top-down survey on securing IoT with machine learning: goals, recent advances and challenges
The Internet of Things (IoT) has seen it all from being just another innovation to a leading
technology; it is now a binding force that interconnects various aspects of our lives. The IoT's …
technology; it is now a binding force that interconnects various aspects of our lives. The IoT's …
[HTML][HTML] Adaptive human–machine evaluation framework using stochastic gradient descent-based reinforcement learning for dynamic competing network
Complex problems require considerable work, extensive computation, and the development
of effective solution methods. Recently, physical hardware-and software-based technologies …
of effective solution methods. Recently, physical hardware-and software-based technologies …
Towards multi-agent reinforcement learning for wireless network protocol synthesis
This paper proposes a multi-agent reinforcement learning based medium access framework
for wireless networks. The access problem is formulated as a Markov Decision Process …
for wireless networks. The access problem is formulated as a Markov Decision Process …
[HTML][HTML] Improving the Performance of ALOHA with Internet of Things Using Reinforcement Learning
Intelligent medium access control (MAC) protocols have been a vital solution in enhancing
the performance of a variety of wireless networks. ALOHA, as the first MAC approach …
the performance of a variety of wireless networks. ALOHA, as the first MAC approach …