  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.
