Location‐dependent sensing data collection and processing mechanism in vehicular network

Abstract

AbstractWith the promotion of the Internet of Things, in which a growing number of objects in everyday life are able to communicate with each other, crowdsensing has attracted public attention. Among different devices, vehicles with various sensors can perform the traffic data collection tasks released by the traffic center, which is helpful for monitoring road conditions. Thus, we use crowdsensing between vehicles to improve the precision of traffic state estimation. We also propose a location‐dependent sensing task assignment mechanism for traffic data collection and a multisource data fusion model for traffic data processing in a vehicular network. First, we establish a mathematical model for the sensing task assignment considering the vehicle's time budget constraint, location, and collection task requirements. We then propose a task assignment algorithm consisting of determining the order for vehicle allocation and scheduling the optimal collection path for each vehicle, which aims to achieve maximum platform utility. Furthermore, we put forward a fusion model, including spatial fusion and temporal fusion, to better process the collected data. We use a power average operator for the spatial fusion and a temporal correlation–based data compression algorithm for the temporal fusion. The obtained simulation results validate the accuracy and correctness of our approach.