###### Abstract  
Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a _Space-Air-Ground integrated Mobile Crowdsensing_ (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called _Multi-Scale Soft Deep Recurrent Graph Network_ (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions. mobile crowdsensing; deep reinforcement learning; UAV control; graph network; maximum-entropy learning +
Footnote †: journal: _entropy_  
0000-0001-6979-2244]Z.X.Y. With the help of high-precision embedded sensors and path planning algorithms [5], smart unmanned vehicles, including automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs), are gradually taking the place of human participants for data collection. A swarm of intelligent unmanned vehicles can perform collaborative sensing tasks round-the-clock [6; 7], or even cooperate with humans [8]. Among all kinds of unmanned vehicles, UAVs have better maneuverability and versatility compared to ground vehicles. Hence, UAV-based MCS technology can achieve large-scale, high-quality, long-term, and in-depth data collection in diverse real-world scenarios, such as efficient area coverage [9; 10], smart city traffic monitoring [11; 12], field search and rescue [13], post-disaster relief [14], communication support [15; 16], reconnaissance in future ways [17], etc. As the rapid developments and applications of modern network technologies [18; 19], several studies have dug deep into heterogeneous networking and proposed an architecture called Space-Air-Ground Integrated Network (SAGIN [20; 21]). SAGIN interconnects space, air, and ground network segments using different networking protocols. Satellite-based networks in space could provide global yet fuzzy observations of large-scale areas, but have some propagation delay due to the operating orbits and long communication ranges. Aerial networks, such as Flying Ad-Hoc Network (FANET [22]), have high mobility and self-organizing ability, but their performance are commonly constrained by unstable connections or dynamic network topology [23]. Ground networks have low transmission latency and efficient power supply, while they cannot maintain network coverage in certain remote areas.