Adaptive Hybrid Routing for Wireless Mesh Networks in Smart Grid: An ML-Driven Framework Integrating RPL and GPSR

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.

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

FGOGNN: An Energy-Efficient and Intelligent Cluster-Based Routing Protocol for Wireless Sensor Networks

A novel cluster-based routing protocol that integrates a Fungal Growth Optimizer for adaptive cluster head (CH) selection and a Graph Neural Network for inter-cluster routing, which demonstrates FGOGNN’s potential for deployment in real-time WSN applications, where energy efficiency and dynamic adaptability are paramount.

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.

Ai Powered Telemetry in Mobile Backbone Networks: Improving Route Stability and Performance Forecasting

Due to cross-domain heterogeneity, unpredictable traffic patterns, and limited real-time visibility into network conditions, mobile backbone networks are increasingly experiencing performance degradation. This study presents a novel cross-domain AI-driven telemetry pipeline that facilitates intelligent, high-performance routing across the core network, transport, and radio access domains in order to address these issues. The suggested approach captures fine-grained network information, such as latency, link utilization, queue depth, and packet loss, in real time by combining streaming telemetry with a uniform cross-layer data aggregation paradigm. Using this telemetry, a lightweight AI-powered predictive routing engine forecasts congestion and uses adaptive path selection to dynamically improve routing choices. The suggested method greatly increases routing efficiency and network resilience by employing the Ant colony optimization (ACO) algorithm to provide improved proactive and context-aware traffic steering, in contrast to conventional reactive routing protocols. Within the mobile backbone, the innovation is found in the smooth integration of AI-driven decision intelligence and cross-domain telemetry. Experiments show significant gains in throughput stability, end-to-end latency, and resource usage, confirming the usefulness of the suggested framework for next-generation mobile networks.

QL-6GRP: A Lightweight Q-Learning-Based Routing Protocol for Dynamic MANETs in 6G-Oriented Environments

Mobile Ad Hoc Networks (MANETs) are expected to support highly dynamic and decentralized communication scenarios in future 6G-oriented wireless systems. However, routing remains challenging because of mobility, topology variability, and resource constraints. Reinforcement learning (RL) offers a promising alternative by enabling adaptive routing decisions based on observed network conditions. This paper presents QL-6GRP (Q-Learning for 6G Routing Protocol), a lightweight Q-learning-based routing protocol designed for fully distributed MANET environments. The protocol enables each node to learn next-hop forwarding decisions using local observations, including link quality, residual energy, hop progress, and neighborhood density. A complete implementation of QL-6GRP was developed within the NS-3 simulator, supporting online learning through hop-level feedback signaling and bounded-memory operation. The protocol was evaluated under multiple parameter settings and network sizes using Random Waypoint mobility and UDP constant-bit-rate traffic to examine both routing performance and computational behavior. The experimental results demonstrate the feasibility of adaptive routing with moderate signaling overhead under carefully tuned moderate-scale scenarios while revealing key trade-offs between feedback frequency, routing quality, and computational scalability. Moderate periodic feedback provides the most favorable balance, whereas excessive feedback increases overhead without improving performance. In addition, reinforcement-learning operations incur substantial computational costs, with the wall-clock runtime increasing by approximately 13 times when the network size increases from 50 to 100 nodes. These findings reveal the operating limits and practical design trade-offs of lightweight tabular RL-based MANET routing and provide useful guidelines for future scalable learning-driven protocols in dynamic wireless environments.