Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks

TL;DR

This paper proposes a machine learning-based, multi-metric context-aware scheme for efficient vehicular sensor data transmission in 5G networks, significantly improving data rates while reducing resource consumption.

Contribution

It introduces a novel probabilistic transmission scheme leveraging multiple context metrics and machine learning to optimize vehicular sensor data transmission in 5G networks.

Findings

Achieves up to 164% higher data rates for sensor data.

Effectively reduces resource consumption during transmission.

Validated through comprehensive real-world experiments.

Abstract

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.