Cloud-Edge Framework for AoI-Efficient Data Processing in Multi-UAV-Assisted Sensor Networks

Cloud and edge computing paradigms are increasingly being applied to data processing for Internet of Things (IoT) sensors. Meanwhile, unmanned aerial vehicles (UAVs) can assist these sensor systems in data acquisition, especially in smart applications such as environmental monitoring and smart agriculture, where direct network connectivity for sensors is limited due to remote deployment. In this work, the Age of Information (AoI) is introduced for wireless sensor networks to measure the freshness of data information. We also develop a hierarchical UAV-assisted data processing framework to minimize AoI, where the multi-UAVs hover over the sensor clusters to collect data and conduct computing offloading by flexibly using the computation resources of edge server or cloud. Then, we innovatively propose a joint service association, trajectory scheduling and computing offloading mechanism for UAVs oriented by AoI. Specifically, we design a sensor clustering and sensor-hovering point (HP) association management scheme to improve the efficiency of data collection, and then propose an HP clustering model to establish the HP-UAV association. Further, a multi-objective optimization model is solved by devising a learning-based trajectory scheduling scheme. Simulation results show that the proposed ATSCO can not only converge well and improve the freshness of data information, but also realize superior performance than the mainstream schemes in various situations.