A new spatiotemporal resilience optimization strategy for UAV swarm in data-physical-enabled low-altitude IoT networks

With the continuous progress of Internet of Things (IoT) technology, unmanned aerial vehicles (UAVs) have been developed unprecedentedly. The environment in which UAV missions are carried out is diverse and complex, and it is inevitable that they will be interfered with to different degrees in the process of performing missions. There is a lack of analysis of the spatial in which UAV clusters operate in harsh mission environments, and the UAV signal transmission loss under the control of ground base station is large, which is difficult to ensure the data quality. Meanwhile, when multiple UAVs fail at the same time, current recovery strategies are lacking in consideration. Based on these, first, we constructed a multi-layer architecture for UAV swarm based on low-altitude IoT technology and analyzed the protocol conversion under the control of ground and air base stations. Then, we analyze the performance of the UAV swarm data layer and physical layer. Second, considering the temporal and spatial evolution characteristics of UAV swarm, the concept of spatiotemporal resilience is proposed. Furthermore, the data layer resilience optimization strategy with air-ground base station coordination and the delayed recovery strategy in the physical layer are proposed to optimize the spatiotemporal resilience of the UAV swarm. Finally, seven UAVs are simulated to perform the mission to validate the spatiotemporal resilience optimization strategy proposed in this paper. The results show that compared with the traditional recovery strategy, the proposed strategy in this paper improves the spatiotemporal resilience of the UAV swarm by 34.48 % and 20.08 %, respectively.