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

TLQ-Geo: a two-level q-learning-based geographic routing protocol for flying ad hoc networks

A two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs, which significantly reduces convergence time and computational overhead and integrates hierarchical decision-making with adaptive reinforcement learning.

AA-BOR: An Adaptive and Autonomous QoS-Aware Forwarding Strategy for Opportunistic Routing in Vehicular Ad Hoc Networks

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

An Adaptive Hybrid Routing Framework that integrates RPL and GPSR under a machine learning (ML)-driven decision engine that offers a resilient and energy-efficient routing solution for next-generation smart grid neighborhood area networks is proposed.

Explainable Neural Network-Driven Context-Aware Predictive Routing for QoS Optimization in MANETs

– Mobile Ad Hoc Networks (MANETs) play a critical role in disaster recovery, military communications, vehicular networking, emergency response, and remote monitoring applications. However, sustaining Quality of Service (QoS) in MANETs is challenging because of changing topologies, mobile nodes, energy constraints, unreliable wireless links, and congestion. Widely adopted routing protocols, including Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Destination-Sequenced Distance Vector (DSDV), and Optimized Link State Routing (OLSR), rely on reactive or proactive route discovery mechanisms and often fail to adapt efficiently to rapidly changing network conditions. Although Deep Reinforcement Learning (DRL)-based routing approaches have demonstrated improved adaptability, they suffer from convergence instability, high computational complexity, continuous online learning requirements, and limited interpretability, restricting their practical deployment in mission-critical environments. To overcome these challenges, this paper presents an Explainable Neural Network-Driven Context-Aware Predictive Routing (XNN-CPR) method. The proposed method predicts the reliability of network links, selects routes based on QoS requirements, and uses SHAP to explain routing decisions. All these functions are combined into a single routing framework. The proposed method uses contextual network parameters, which include node mobility, residual energy, queue occupancy, Received Signal Strength Indicator (RSSI), and Signal-to-Noise Ratio (SNR), to infer future link reliability and congestion conditions. An explainable neural network is trained using simulation-generated data to estimate route stability, while SHAP-based explanations are employed to interpret and validate routing decisions. Unlike conventional DRL-based routing approaches that require continuous policy optimization during network operation, the proposed framework separates model training from routing execution, thereby reducing computational overhead and improving decision stability. Simulation experiments conducted in the NS-3 environment show that the proposed XNN-CPR framework steadily outperforms AODV, DSR, OLSR, and Deep Deterministic Policy Gradient (DDPG)- based routing protocols. Compared with the DRL-based benchmark, XNN-CPR obtains a 3.94% improvement in Packet Delivery Ratio (PDR), a 17.67% reduction in End-to-End Delay, and an 8.69% rise in Throughput. The results make sure that the integration of predictive intelligence with explainable decision-making noticeably enhances routing reliability, network stability, and QoS performance while ensuring transparency and trustworthiness in highly dynamic MANET environments.

A Novel RL-Driven Zone-Based Leader-Aware Energy-Efficient Routing Protocol for Dynamic MANET Environments

The paper introduces RML-ZEREM to solve existing limitations, which functions as a Reinforcement Learning (RL) based Zone-Based Leader-Aware Energy-Efficient Routing Protocol for MANETs, which serves next-generation MANET applications.

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.