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Articles (5)

Research Article

Available Online: 25 Sep 2025

Optimizing Cluster Head Selection and Routing in 5G WSNs: A Reinforcement Learning and Deep Learning Approach

Volume 2

Journal: Communications & Networks Connect
Research Article

Published: 08 Aug 2025

An Enhanced Puma Optimized Reinforcement Learning Model for Detection of Results Anomalies in Higher Education

Volume 2

Journal: Computing&AI Connect
Research Article

Published: 07 May 2025

A Novel Transformer Reinforcement Learning-Based NFV Service Placement in MEC Networks

Volume 2

The advent of 5G networks has facilitated various Industry 4.0 applications requiring stringent Quality-of-Service (QoS) demands, notably Ultra-Reliable Low-Latency Communication (URLLC). Multi-Access Edge Computing (MEC) has emerged as a key technology to support these URLLC applications by bringing computational resources closer to the user, thus reducing latency. Meanwhile, Network Function Virtualization (NFV) supports 5G networks by offering flexibility and scalability in service provisioning across various applications. Despite their benefits, MEC..

Journal: Computing&AI Connect
Research Article

Published: 02 Aug 2024

Challenging Conventions Towards Reliable Robot Navigation Using Deep Reinforcement Learning

Volume 1

Effective indoor navigation in the presence of dynamic obstacles is crucial for mobile robots. Previous research on deep reinforcement learning (DRL) for robot navigation has primarily focused on expanding neural network (NN) architectures and optimizing hardware setups. However, the impact of other critical factors, such as backward motion enablement, frame stacking buffer size, and the design of the behavioral reward function, on DRL-based navigation remains relatively unexplored. To address this..

Journal: Computing&AI Connect
Review Article

Published: 31 Dec 2024

Distributed Reinforcement Learning for IoT Security in Heterogeneous and Distributed Networks

Volume 1

The explosive growth of the Internet of Things (IoT) has significantly increased networked devices within distributed and heterogeneous networks. Due to these networks’ inherent vulnerabilities and diversity, the proliferation of IoT devices presents substantial security challenges. Traditional security solutions face challenges in keeping up with the constantly changing threats in dynamic situations. This article reviews the application of distributed Reinforcement Learning approaches to enhance IoT security in dispersed and heterogeneous..

Journal: Computing&AI Connect
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