Link Resource Allocation Strategy Based on Age of Information and Sample Extrusion Awareness in Dynamic Channels

As an emerging measure of data freshness, the age of information (AoI) is receiving extensive attention. Many methods using AoI have been proposed for communication scheduling in the Internet of Things (IoT). However, most of them are aimed at constant channel conditions in the ideal state, and the utilization of link resources is not sufficient. In addition, only the optimization of AoI is considered, without considering whether the sample is extruded. Sample extrusion refers to the scenario in which the transmission of the remaining untransmitted sample of the source node cannot be completed within the transmission time interval (TTI) before the arrival of the new sampling period of the source node. Thus, sample extrusion is a phenomenon that occurs when the new sample arrives while the old sample has not yet been completely transmitted. This scenario has a serious impact on delay-sensitive IoT applications. Therefore, under dynamic channel conditions and limited link resources, this paper establishes two mathematical models for AoI and sample extrusion. The influence of the scheduling algorithm on these two target values is analyzed and proven. Based on a greedy strategy, we propose a preemptive online algorithm for link resource allocation that considers two objectives: to give full play to the value of link resources and to minimize sample extrusion. The simulation results show that the proposed strategy can achieve better comprehensive performance in both scenarios where the sample variance between source nodes is small or large.