Dynamic Traffic Detection and Modeling for Beidou Satellite Networks Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS3 continues to increase, BDS3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS3. A dynamic traffic detection method including traffic prediction by Long ShortTerm Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A timevarying graph (TVG) model is introduced to represent the dynamic topology, the timevarying link, and its traffic. The BDS3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS3 to establish automatic management and security system and develop business.

BeiDou Satellite Positioning Method Based on IoT and Edge Computing The BeiDou navigation satellite system (BDS) developed by China can provide users with high precision, as well as allweather and realtime positioning and navigation. It can be widely used in many applications. However, new challenges emerge with the development of 5G communication system and Internet of Things (IoT) technologies. The BDS needs to be suitable for the largescaled terminal scenario and provides higher positioning precision. In this paper, a BeiDou differential positioning method based on IoT and edge computing is proposed. The computational pressure on the data center is offloaded to the edge nodes when the massive positioning requests of IoT terminals need to be processed. To ensure the load balancing of the edge nodes, the resource allocation of the terminal positioning requests is performed with the improved genetic algorithm, thereby reducing the service delay of the entire edge network. Moreover, the optimized unscented Kalman filter based on the edge node (EUKF) algorithm is used to improve the positioning precision of IoT terminals. The results demonstrate that the proposed positioning method has better positioning performance which can provide the realtime positioning service for the largescale IoT terminals.