Minimizing Age of Usage Information for Capturing Freshness and Usability of Correlated Data in Edge Computing Enabled IoT Systems

Age of Information (AoI) is a popular metric of data freshness, however, it neglects the usability of data. In light of this, we introduce a new metric,
Age of Usage Information
(AoUI), which can jointly capture the freshness and usability of correlated data in the Internet of Things in a fine-grained manner. Based on the proposed metric, we investigate the optimization problem of minimizing average AoUI, where the correlated nodes transmit data to the destination via noisy channels. To seek the optimal data scheduling policy, we first develop a virtual queue based (VQ) policy under the assumption that the priori knowledge of the channel state is known. Then, considering the case where the channel state is unknown, we utilize the model-free characteristic of double deep Q-network (DDQN) to design an improved exploration based DDQN (IE-DDQN) policy which does not require a priori knowledge of the channel state. Furthermore, we investigate the development of joint data scheduling and usage policy and introduce decoupled action branches to improve the structure of the neural network of DDQN proposing a decoupling action based DDQN (DA-DDQN) policy. Simulation results show that the proposed VQ, IE-DDQN, and DA-DDQN policies all exhibit superior performance compared to baseline algorithms such as the classical DDQN method and the greedy policy of scheduling the node with the largest product of AoUI and usable factor, which may be due to the consideration of the stability of the virtual queue, the improvement of the exploration process, and the reconstruction of the neural network structure, respectively.