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

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 …

Reinforcement learning (RL) is increasingly being used to optimize resource-constrained
wireless Internet of Things (IoT) devices. However, existing RL algorithms that are …

For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing
solutions to overcome key IoT challenges that include data processing, resource …

The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …

Training machine learning models, such as reinforcement learning models, require a
significant investment of time, and a trained model can only work on a specific system in a …

The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

Wireless local area networks (WLANs) are widely deployed for Internet-centric data
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …

Next-generation wireless networks have to be robust and self-sustained. Internet of things
(IoT) is reshaping the technological adaptation in the daily life of human beings. IoT …

Advances in artificial intelligence (AI) and wireless technology are driving forward the large
deployment of interconnected smart technologies that constitute cyber–physical systems …