A reinforcement learning approach to enhance the trust level of MANETs

Abstract

A Mobile ad-hoc network (MANET) consists of many freely interconnected and autonomous nodes that is often composed of mobile devices. MANETs are decentralized and self-organized wireless communication systems, which are able to arrange themselves in various ways and have no fixed infrastructure. Since MANETs are mobile, the network topology is changing rapidly and unpredictably. Because of this nature of mobility of the nodes in MANETs, the main problems that occur are unreliable communications and weak security where the data can be compromised or easily misused. Therefore, a trust enhancement approach to a MANET is proposed which is RLTM (Reinforcement Learning Trust Manager), a set of algorithms, considering Ad-hoc On-demand Distance Vector (AODV) protocol as the specific protocol, via Reinforcement Learning (RL) and Deep Learning concepts. The proposed system consists of RL agent, who learns to detect and give predictions on trustworthy nodes, reputed nodes, and malicious nodes and classifies them. The identified parameters from AODV simulation using Network Simulator-3(NS-3) were given to the designed RNN (Recurrent Neural Network) model and results were evaluated.