Abstract
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often updated periodically, which makes much of which outdated, or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.