Energy-Aware Adaptive Routing for Mobile Sink Nodes in Wireless Sensor Networks Leveraging Deep Reinforcement Learning and Fuzzy-Based Clustering

DEERL-WSN: A Distributed Energy-Efficient Reinforcement Learning Approach for Wireless Sensor Networks in IoT Applications

Wireless Sensor Networks (WSNs) play a crucial role in the expanding landscape of the Internet of Things (IoT), yet they continue to face persistent challenges related to energy consumption, computational efficiency, and scalability. Although protocols like the Energy-Efficient Routing Protocol through Hybrid Algorithm...

ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks

This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.

MITIGATING ENERGY DISSIPATION IN WIRELESS SENSOR NETWORKS VIA REINFORCEMENT LEARNING

Simulation outcomes indicate that the suggested algorithm can save up to 25% of energy per round relative to traditional protocols and increase network life to 50 times that of the LEACH protocol.

ARFOR: adaptive random forest and owl optimization for energy-efficient routing in WSN-IoT

An intelligent reinforcement learning framework for congestion aware routing in IoT sensor networks

An intelligent routing algorithm called Reinforcement Learning-based Congestion-Aware Routing (RLbCAR) is introduced for intelligent routing in IoT sensor networks and ensures reliable, congestion-adaptive, and computationally efficient routing in a resource-limited IoT sensor network.

Optimized Energy Efficiency in Wireless Sensor Networks Using Dynamic Sub-station Placement and Q-learning-Based Cluster Head Selection

An innovative EDSP-QCH (Energy-efficient Dynamic Sub-Station Placement and reward-based Q-Learning reinforcement model for Cluster Head selection) strategy is proposed, which indicates significant improvements in energy efficiency and network robustness.