This paper proposes a distributed Reinforcement Learning (RL) based framework that can
be used for synthesizing MAC layer wireless protocols in IoT networks with low-complexity …

This paper presents a distributed Reinforcement Learning (RL) framework for synthesizing
wireless network protocols in IoT and Wireless Sensor Networks with low-complexity …

In this paper, we propose a distributed solution to design a multi-hop ad hoc Internet of
Things (IoT) network where mobile IoT devices strategically determine their wireless …

Distributed Medium Access Control (MAC) protocols are fundamental in wireless
communication, yet traditional random access-based protocols face significant limitations …

Wireless communication and computation technologies are becoming increasingly complex
and dynamic due to the sophisticated and ubiquitous Internet of things (IoT) applications …

The next generation of the Internet of Things (IoT) networks is expected to handle a massive
scale of sensor deployment with radically heterogeneous traffic applications, which leads to …

Nowadays, many research studies and industrial investigations have allowed the integration
of the Internet of Things (IoT) in current and future networking applications by deploying a …

Power-limited devices (or sensors) constrain the deployment of modern IoT networks, such
as Next-Generation Industrial IoT (NG-IIoT). These networks are envisioned as the key …

Internet-of-Things (IoT) networks are composed of devices generating varying amounts of
data with diverse quality of service (QoS) requirements. Mission-critical IoT networks aim to …

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 …