Energy-Efficient Data Collection and Resource Allocation in Age-Aware IoV

The emergence of Internet of Vehicles (IoV) has facilitated many attractive vehicular applications that require massive sensed data and timely data analysis. Data collection and resource allocation are critical issues in IoV for timely data processing in dynamic network environments. However, the energy consumption of IoV infrastructure and vehicles pose challenges to developing sustainable vehicular communication and networking infrastructure. Moreover, the communication and computing resources are generally insufficient to support the transmission and analysis of the excessive data. Vehicular data are often updated periodically, which makes much of which outdated, or useless for vehicular applications, and thus leads to large latency and tremendous energy consumption. To this end, age of Information (AoI) has been introduced as a novel metric to characterize data freshness. Satisfying the state update AoI constraint is of great significance to guarantee the freshness of the IoV system. In this paper, we design a sampling strategy with responsive transmission and computing (e.g., no waiting latency), and investigate an energy minimization problem under peak AoI constraints in age-aware vehicular networks. We decouple the problem as a minimum set cover problem, and a convex problem, and propose a joint sampling selection and resource allocation (JSRA) algorithm to obtain the approximate optimal solution. We evaluate the proposed sampling selection and resource allocation strategy and JSRA algorithm by experiments on the simulation of urban mobilty (SUMO). Numerical results show that the proposed sampling strategy and algorithm outperform existing methods in terms of energy consumption, especially for the scenario with dense vehicles and pedestrians.