# Investigating Value of Information in Future Vehicular Communications

arXiv: https://arxiv.org/abs/1907.10124 · HTML: https://mlsanity.com/paper/1907.10124/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks](https://mlsanity.com/paper/2104.11785/) (2021)
- [Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3](https://mlsanity.com/paper/2203.05449/) (2022)
- [Learning Value of Information towards Joint Communication and Control in 6G V2X](https://mlsanity.com/paper/2505.06978/) (2025)
- [PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence](https://mlsanity.com/paper/2507.14211/) (2025)

## Abstract (arXiv)

The next generations of vehicles are expected to be equipped with sophisticated sensors to support advanced automotive services. The large volume of data generated by such applications will likely put a strain on the vehicular communication technologies, which may be unable to guarantee the required quality of service. In this scenario, it is fundamental to assess the value of information (VoI) provided by each data source, to prioritize the transmissions that have greatest importance for the target applications. In this paper, we characterize VoI in future vehicular networks, and investigate efficient data dissemination methods to tackle capacity issues. Through a simulation study, we show how analytic hierarchy multicriteria decision processes can be exploited to determine the value of sensory observations as a function of space, time, and quality criteria.