Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems

TL;DR

This paper introduces a Q-learning-based offloading scheme for real-time mobile air quality monitoring, reducing 4G costs by 40-50% while maintaining low data latency.

Contribution

It presents a novel Q-learning approach for opportunistic data offloading in mobile air quality monitoring systems, balancing cost and latency.

Findings

Reduces 4G communication costs by 40-50%.

Maintains 99.5% packet latency below threshold.

Demonstrates effective offloading in real-world scenarios.

Abstract

We focus on real-time air quality monitoring systems that rely on devices installed on automobiles in this research. We investigate an opportunistic communication model in which devices can send the measured data directly to the air quality server through a 4G communication channel or via Wi-Fi to adjacent devices or the so-called Road Side Units deployed along the road. We aim to reduce 4G costs while assuring data latency, where the data latency is defined as the amount of time it takes for data to reach the server. We propose an offloading scheme that leverages Q-learning to accomplish the purpose. The experiment results show that our offloading method significantly cuts down around 40-50% of the 4G communication cost while keeping the latency of 99.5% packets smaller than the required threshold.