###### Abstract
In this paper, we employ the concept of SAGIN into the data collection task, and present a new MCS framework with a collection of UAVs, ground nodes and satellites, namely _Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS)_. In SAG-MCS scenario, a UAV swarm is used to cooperate autonomously and fly above an area with multiple Points of Interest (Pols) for coverage and sensing. As illustrated in Figure 1, UAV agents can partially observe ground information using embedded sensors within a fixed observation range. They also have access to fuzzy global information periodically from remote sensing satellites in space, which contains ambiguous locations of PoIs and other agents. As the coverage range is set smaller than the observation radius, UAVs should get close enough to the observed PoIs for valid data collection. Based on the FANET, UAV pairs that within maximum communication range can interconnect together and share current states and observations using Wi-Fi, Bluetooth or LoRa. We consider communication dropout would occur inevitably during such aerial ad-hoc network connections. As for energy consumption, due to the limitations of the rotor power efficiency and the onboard battery capacity, we set all UAVs with limited battery attributes as energy constraints. Several charging stations and barriers are deployed in the SAG-MCS simulation scenario as well. The UAV swarm is required to avoid collision with obstacles when performing data collection and flight path planning tasks, and makes proper decisions to go for charging before their batteries run out. On arrival at the charging stations, UAVs can transfer the data collected and batteries will be replaced. On the whole, this paper endeavours to propose a decision-making model for UAVs, which are powered by limited onboard batteries and distributed charging stations, to energy-efficiently and persistently sense and collect PoIs on the ground. The multi-UAV swarm shall perform actions according to local airborne observations and global observations from satellites. The overall optimization objective of the UAV swarm is to maximize the data coverage and geographical fairness among all PoIs, and minimize the power consumed during flying or battery charging. For such an MCS task that has multiple complex objectives, existing approaches that modeling MCS as an optimization problem is no longer effective. However, recently well-explored Deep Reinforcement Learning (DRL) could be a feasible solution. It has achieved great performances in several game-playing tasks [24] or path planning problems [25]. Based on powerful deep neural networks, DRL models can extract more complicated features of higher dimensions from environmental states, thereby can optimize action policies to achieve different objectives. For multi-agent systems such as our SAG-MCS, typical methods that take the whole system as a single agent cannot guarantee promising results, while recent studies on Multi-Agent Deep Reinforcement Learning (MADRL) focus on controlling multiple agents in a fully distributed manner. The action strategy of each agent in MADRL depends on not only the interaction with the environment, but also other agents' actions, observations, etc.