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To measure the freshness of information more accurately and satisfy the timeliness demands for a wide range of time-sensitive applications, Age of Information (AoI) becomes a popular network performance metric. In this paper, we investigate the optimization of real-time performance in an Internet of Things (IoT) system with heterogeneous traffic, where periodic traffic for control and random traffic for update coexist. Generally, the periodically arriving data used for control is strictly deadline-sensitive, while the randomly arriving data used for updates has freshness requirements. Thus, we seek to develop a scheduling scheme that minimizes the long-term average AoI under the delay tolerance constraint, to meet the varying transmission timeliness requirements for different arrival models. The optimization problem is modeled as a constrained Markov decision process, and then it is decoupled into two classes of optimization sub-problems for single-node by relaxing the hard constraint of channel resources with the Lagrange multiplier. On this basis, a multi-node scheduling scheme consisting of a master policy and two sub-policies is designed. Specifically, two sub-policies are built for the two classes of optimization sub-problems, respectively, with Lyapunov and linear programming methods. For the hard constraint, a master policy is designed by introducing deep reinforcement learning for scheduling opportunities allocation, and a truncated sub-policy is proposed based on one of the sub-policies. Numerical results show that the proposed scheme can obtain a smaller long-term average AoI compared to other advanced algorithms while satisfying the delay constraint.
Wang et al. (Fri,) studied this question.