## 1 Introduction
In addition, the angle of the received signal can be measured using a compass or special smart antenna [19]. Media access control protocol  
In WSNs, many sensors work together to perform data transfer tasks efficiently. Therefore, designing MAC protocols for WSNs presents different challenges than typical wireless networks, as well as energy consumption and latency challenges [20]. In addition, WSNs must control the duty cycle of nodes in data transmission scheduling, which is beneficial to saving energy. Therefore, the MAC protocol used in WSNs must be modified to support sensor nodes to carry out data transmission and receiving tasks effectively. MAC protocols proposed in WSNs include TDMA-based MAC protocol [21], variable/burst traffic hybrid CSMA/TDMA iQueue-MAC protocol [22], probabilistic polling MAC protocol (PP-MAC), energy collection MAC protocol (EH-MAC), ERI-MAC protocol, etc. [23]. Reinforcement learning is a kind of effective decision-making method, which can find the best or nearly the best strategy for an agent. However, reinforcement learning is generally applicable to the case of small system space or limited network topology. When the system complexity is high or the data latitude is high, reinforcement learning methods have problems such as dimension disaster and insufficient memory. Starting from the topology of WSNs, this paper considers the scheduling problem of data transmission in the case of concurrent data, at which time the general reinforcement learning method has been difficult to solve. At this point, the deep reinforcement learning method combining the powerful information perception ability of deep learning and the decision-making ability of reinforcement learning is a new research idea. This paper takes an industrial WSNs environment as the research object. The real-time data transmission scheduling method is studied to improve the reliability and real-time performance of WSNs. Based on the topology structure of WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed to solve the problem of concurrent data transmission scheduling in WSNs. A Q-learning model was established to comprehensively consider the influences of communication constraints and interference between nodes in WSNs, remaining deadline of transmission data, remaining hop count, and other factors on data transmission to the destination node. By using the reinforcement learning method to determine the sensor nodes for data transmission in each time slot, the deep learning method combined with experience playback is used to perceive the mapping relationship between system state and behavior. Q-learning is used to update the mapping relationship between state and behavior, so as to realize the learning of data transmission scheduling strategy. An effective data transmission scheduling algorithm is obtained. The network performance of this algorithm is simulated and compared with other existing algorithms to verify its effectiveness. The main contributions of this paper are listed as follows:  
To solve the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed in this paper. 1. The algorithm comprehensively considers the influence of remaining cutoff time, remaining hop count, and unallocated time slot nodes in data transmission, defines the reward function and action selection strategy of Q-learning, and guides the process of system state information transfer. 2. To solve the disaster maintenance problem caused by large-scale system states, deep learning and Q-learning are combined, and a multi-layer Stacked Auto Encoder (SAE) network model is used to establish the state-action mapping relationship. The mapping relationship is updated by the Q-learning algorithm. 3. According to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained, and the simulation results show that the strategy is effective.