Joint Sensing and Computation Decision for Age of Information-Sensitive Wireless Networks: A Deep Reinforcement Learning Approach.

In this paper, we propose a joint sensing and computing decision algorithm for data freshness in edge computing (EC)-enabled wireless sensor networks. By introducing the data freshness at the presented networks, we define the $\eta$ -coverage probability to show the probability of maintaining fresh data for more than $\eta$ ratio of the network, where the spatial-temporal correlation of information is considered. To maximize the $\eta$ -coverage probability in the networks with limited energy, we propose the reinforcement learning (RL)-based decision algorithm by training the policy of sensors. Our simulation results verify the performance of the proposed algorithm for different number of sensors and the computing energy. From the results, we show the proposed algorithm achieves higher $\eta$ -coverage probability compared to the baseline algorithms.