## 1 Introduction  
With major technological advances in communication, computing, and sensing, sensor networks play important roles for modern society. Sensor nodes help in collecting data from environment or devices, which can be used to monitor environments, develop and implement intelligent control systems, such as smart cities, smart factories, and intelligent surveillance systems. For example, there are about 70 million surveillance cameras in the U.S. While the fast-growing number of sensors provide the data needed for big data analytics and intelligence, the massive data traffic also presents a big challenge for data transport and networking, e.g., increased network congestion and poor network quality of services. One of the promising networking approaches to tackle these challenges is software-defined networking (SDN) [1]. In the SDN paradigm, the control plane is decoupled from the data plane to provide flexible traffic control and simplify the network operation and management. An investigation of the SDN technology for the Internet of Things (IoT) was reported in [2, 3]. Intelligent traffic routing control for the SDWSN controllers is critical and very challenging as it needs to be effective, adaptive, and reliable. Several researches discuss the routing control for SDWSN, such as SDN-WISE [4] and IT-SDN [5]. The performance of SDN-WISE is evaluated by considering six nodes in a linear topology and only one node generates a data packet at a certain time. The size of the network in IT-SDN is larger, and every node transmits one packet per minute. The controllers of these approaches determine the path from one node to another only using the Dijkstra algorithm. The Dijkstra algorithm is effective when the topology becomes small and the network is under light traffic. However, it will lead to network abnormalities such as slow response speed in the case of heavy traffic [6]. For example, in Figure 1, the Dijkstra algorithm will select node 5 as a relay node of multiple nodes. Node 5 needs to undertake the task of transmitting data packets to the sink node for multiple nodes. Congestion will occur on node 5 in the case of heavy traffic. The packet loss rate and delay of the network will then rise sharply. The routing architecture proposed by [PERSON] and [PERSON] [7] combines context-aware and policy-based routing modules. The controller chooses the finest next hop depending on the context information such as CPU load, service information, and power levels. In literature [8], the authors introduced reinforcement learning into SDWSN by designing a quadruple reward function and combining Q-learning algorithm. In fact, the Q-learning algorithm is suitable for discrete and low dimensional action space. When there are many states, the Q table will be very large, and the search and storage will consume a lot of memory. In view of the research gaps, we propose in this paper a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution and deterministic policy gradient. Reinforcement learning is a category of machine learning technology in which agents learn and make decisions by interacting with the environment according to the current state [9]. Compared with deep learning, reinforcement learning has better real-time performance. In the general reinforcement learning (RL) algorithm, the quality of the action is determined by the reward value after the policy is implemented. As time goes by, the agent will be able to make the best reward decision based on experience. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. In the application scenario of SDWSN data forwarding and traffic control we studied in this paper, the controller learns the policy of data packets forwarding according to the current operating conditions.