Intelligent Routing Control for MANET Based on Reinforcement Learning

Abstract

With the rapid development and wide use of MANET, the quality of service for various businesses is much higher than before. Aiming at the adaptive routing control with multiple parameters for universal scenes, we propose an intelligent routing control algorithm for MANET based on reinforcement learning, which can constantly optimize the node selection strategy through the interaction with the environment and converge to the optimal transmission paths gradually. There is no need to update the network state frequently, which can save the cost of routing maintenance while improving the transmission performance. Simulation results show that, compared with other algorithms, the proposed approach can choose appropriate paths under constraint conditions, and can obtain better optimization objective.

FQ‐MEC: Fuzzy‐Based Q‐Learning Approach for Mobility‐Aware Energy‐Efficient Clustering in MANET

“…In [8], the authors propounded an intelligent routing control algorithm for MANET, which was based on reinforcement learning. The employed algorithm can optimize the selection strategy of nodes through interplay with an environment and coverage with the optimal transmission pathway.…”

“…where max ðq½i, a k Þ is the maximum of the q values achievable in state s′ (8) Calculate the error signal: if the maximum reward given to the system deviates from the predicted one, then an error signal is calculated as follows:…”

DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing

“…Deep reinforcement learning (DRL) algorithms are successfully applied to complex high-dimensional problems, mainly due to the use of deep neural networks (DNN) for function approximations [1]. Researchers have applied DRL algorithms for various problems in mobile ad-hoc networks (MANETs), e.g., for minimizing average or worst-case end-to-end delay in routing problems [2], [3] and routing path optimization [4]. In [5], it is shown that the DRL-based DeepCQ+ algorithm outperforms the state-of-the-art robust routing for dynamic networks (R2DN) [6].…”