Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects.

Big data analytics and mining have the potential to enable real-time decision making and control in a range of Internet of Things (IoT) application domains, such as the Internet of Vehicles, the Internet of Wings, and the Airport of Things. The prediction toward air mobility, which is essential to the studies of air traffic management, has been a challenging task due to the complex spatial and temporal dependencies in air traffic data with highly nonlinear and variational patterns. Existing works for air traffic prediction only focus on either modeling static traffic patterns of individual flight or temporal correlation, with no or limited addressing of the spatial impact, namely, the propagation of traffic perturbation among airports. In this article, we propose to leverage the concept of graph and model the airports as nodes with time-series features and conduct data mining on graph-structured data. To be specific, first, airline on-time performance (AOTP) data is preprocessed to generate a temporal graph data set, which includes three features: 1) the number; 2) average delay; and 3) average taxiing time of departure and arrival flights. Then, a spatial–temporal graph neural networks model is implemented to forecast the mobility level at each airport over time, where a combination of graph convolution and time-dimensional convolution is used to capture the spatial and temporal correlation simultaneously. Experiments on the data set demonstrate the advantage of the model on spatial–temporal air mobility prediction, together with the impact of different priors on adjacency matrices and the effectiveness of the temporal attention mechanism. Finally, we analyze the prediction performance and discuss the capability of our model. The prediction framework proposed in this work has the potential to be generalized to other spatial–temporal tasks in IoT.