  Resource management in Internet of Things (IoT) systems is a major challenge
due to the massive scale and heterogeneity of the IoT system. For instance,
most IoT applications require timely delivery of collected information, which
is a key challenge for the IoT. In this paper, novel centralized and
distributed resource allocation schemes are proposed to enable IoT devices to
share limited communication resources and to transmit IoT messages in a timely
manner. In the considered system, the timeliness of information is captured
using non-linear age of information (AoI) metrics that can naturally quantify
the freshness of information. To model the inherent heterogeneity of the IoT
system, the non-linear aging functions are defined in terms of IoT device types
and message content. To minimize AoI, the proposed resource management schemes
allocate the limited communication resources considering AoI. In particular,
the proposed centralized scheme enables the base station to learn the device
types and to determine aging functions. Moreover, the proposed distributed
scheme enables the devices to share the limited communication resources based
on available information on other devices and their AoI. The convergence of the
proposed distributed scheme is proved, and the effectiveness in reducing the
AoI with partial information is analyzed. Simulation results show that the
proposed centralized scheme achieves significantly lower average instantaneous
AoI when compared to simple centralized allocation without learning, while the
proposed distributed scheme achieves significantly lower average instantaneous
AoI when compared to random allocation. The results also show that the proposed
centralized scheme outperforms the proposed distributed scheme in almost all
cases, but the distributed approach is more viable for a massive IoT.
