Reinforcement learning-based fuzzy geocast routing protocol for opportunistic networks

Abstract

In a communication environment like opportunistic networks (OppNets) where there is no stable path, the message routing is a challenge. In this sense, using a deep learning approach that focuses on utilizing the agents in the environment to fulfill the message routing task has proven to be effective. This paper proposes a novel routing scheme for OppNets called Reinforcement Learning-based Fuzzy Geocast Routing Protocol (RLFGRP) for OppNets, in which a fuzzy controller makes use of the node’s Q-value, reward value and remaining buffer space as input parameters to determine the likelihood of that node to be selected as suitable forwarder of a message from source towards its destination. Through simulations using real mobility traces, the proposed RLFGRP protocol is shown to outperform the established Geocast Fuzzy-Based Check-and-Spray routing (FCSG) [4] and the Fuzzy logic-based Q-learning routing (FQLRP) [9] protocols in terms of overhead ratio, delivery ratio and average latency.