Fuzzy Q-Learning-Based Opportunistic Communication for MEC-Enhanced Vehicular Crowdsensing

TL;DR

This paper proposes a Fuzzy Q-Learning-based offloading strategy for MEC-enhanced vehicular crowdsensing that reduces 4G communication costs by 30-40% while maintaining low latency for most packets.

Contribution

It introduces a novel combination of Q-learning and Fuzzy logic to optimize communication channel selection in vehicular crowdsensing systems.

Findings

Reduces 4G communication costs by 30-40%.

Maintains 99% packet latency below threshold.

Effective in dynamic vehicular environments.

Abstract

This study focuses on MEC-enhanced, vehicle-based crowdsensing systems that rely on devices installed on automobiles. We investigate an opportunistic communication paradigm in which devices can transmit measured data directly to a crowdsensing server over a 4G communication channel or to nearby devices or so-called Road Side Units positioned along the road via Wi-Fi. We tackle a new problem that is how to reduce the cost of 4G while preserving the latency. We propose an offloading strategy that combines a reinforcement learning technique known as Q-learning with Fuzzy logic to accomplish the purpose. Q-learning assists devices in learning to decide the communication channel. Meanwhile, Fuzzy logic is used to optimize the reward function in Q-learning. The experiment results show that our offloading method significantly cuts down around 30-40% of the 4G communication cost while keeping the latency of 99% packets below the required threshold.