# A Framework to Assess Value of Information in Future Vehicular Networks

## TL;DR

This paper presents a framework using multicriteria decision processes to evaluate the value of information in future vehicular networks, addressing data prioritization challenges due to increasing sensor data volume.

## Contribution

It introduces a novel framework for assessing VoI based on space, time, and quality, considering various factors affecting information value in vehicular communication.

## Key findings

- Propagation scenario impacts VoI assessment accuracy
- Sensor resolution and observation type influence VoI predictions
- VoI varies with application-specific requirements

## Abstract

Vehicles are becoming increasingly intelligent and connected, incorporating more and more sensors to support safer and more efficient driving. The large volume of data generated by such sensors, however, will likely saturate the capacity of vehicular communication technologies, making it challenging to guarantee the required quality of service. In this perspective, it is essential to assess the value of information (VoI) provided by each data source, to prioritize the transmissions that have the greatest importance for the target applications. In this paper, we propose and evaluate a framework that uses analytic hierarchy multicriteria decision processes to predict VoI based on space, time, and quality attributes. Our results shed light on the impact of the propagation scenario, the sensor resolution, the type of observation, and the communication distance on the value assessment performance. In particular, we show that VoI evolves at different rates as a function of the target application's characteristics.