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

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

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

Feasibility-Aware Reinforcement Learning for Reliable Hop-Constrained Routing in Wireless Sensor Networks

The experiments show that feasibility-aware learning can approach deterministic baseline reliability while retaining learned forwarding capability under hop constraints, and confirm that action masking is the dominant mechanism for maintaining feasible routing decisions, whereas trust mainly provides reliability-aware regularization.

A dynamic reward framework for scalable and efficient IoT-WSN routing using deep reinforcement learning.

A dynamic reward structuring framework within deep reinforcement learning to enable adaptive and balanced routing in IoT-WSNs and achieves significant performance gains, including approximately 30% improvement in energy efficiency, 25% reduction in latency, and 35% increase in network throughput compared with baseline methods.

Modeling a Software‐Defined Network for Analyzing Route and Energy With Sensor Nodes

The practical architecture known as software‐defined networking (SDN) enables the Internet of Things (IoT) to function in various applications. Also, SDN has been adopted for effective routing in wireless networks. The controller intends to work using an algorithm to offer secure routing. However, some existing algorithms must provide optimized and secured routing paths. This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA). The aim is to ensure that the chain and reward functions align with the Quality of Service (QoS). The reward regarding the following successive routing path is analyzed where SDN‐enabled IoT enhances the routing based on the prior routing ideas. Moreover, the entire network is managed via the network remotely. The performance of the anticipated is compared with various prevailing approaches. Multiple metrics like packet delivery rate (PDR), network lifetime, routing overhead, energy efficiency, and delay are compared to attain suitable WSN performance via efficient routing.

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 Algorithms (EERHA) have made progress in extending network lifespan using machine learning, they still encounter limitations particularly in managing processing overhead, adapting to changing conditions, and scaling to larger deployments. Unlike existing approaches that merely combine machine learning modules, DEERL-WSN (Distributed Energy-Efficient Reinforcement Learning for Wireless Sensor Networks), introduces a unified hierarchical learning framework where distributed reinforcement learning, lightweight graph neural networks, transfer learning, and federated optimization operate cooperatively. The novelty lies in (i) adaptive reward-driven routing using meta-learned objective weights, (ii) topology-aware clustering through lightweight GNN embeddings with significantly reduced computational complexity, (iii) transfer learning-assisted cluster-head prediction to eliminate repetitive optimization overhead, and (iv) federated deep reinforcement learning enabling scalable learning without centralized processing bottlenecks. These integrated innovations collectively address energy efficiency, scalability, and computational constraints simultaneously, which remain largely unresolved in existing WSN routing frameworks. DEERL-WSN addresses several bottlenecks found in earlier protocols and the simulation results show that DEERL-WSN significantly outperforms EERHA and other state-of-the-art methods.