Spatial Crowdsourcing (SC) has been proved as an effective paradigm for data acquisition in urban environments. Apart from using human participants, with the rapid development of unmanned vehicles (UVs) technologies, unmanned aerial or ground vehicles (UAVs, UGVs) are equipped with various high-precision sensors, enabling them to become new types of data collectors. However, UGVs’ operational range is constrained by the road network, and UAVs are limited by power supply, it is thus natural to use UGVs and UAVs together as a coalition, and more precisely, UGVs behave as the UAV carriers for range extensions to achieve complicated air-ground SC tasks. In this paper, we propose a novel communication-based multi-agent deep reinforcement learning method called "GARL", which consists of a multi-center attention-based graph convolutional network (GCN) to accurately extract UGV specific features from UGV stop network called "MC-GCN", and a novel GNN-based communication mechanism called "E-Comm" to make the cooperation among UGVs adaptive to constant changing of geometric shapes formed by UGVs. Extensive simulation results on two campuses of KAIST and UCLA campuses show that GARL consistently outperforms eight other baselines in terms of overall efficiency.

Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called "t-LocPred". It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods ("ConvAoI"); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model ("mem-attLSTM") to capture long-term behavior patterns. Experimental results show that t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.

Spatial crowdsourcing (SC) has been proved quite successful by employing human participants to achieve certain tasks like Uber and Gigwalk. Meanwhile, with the fast devel-opment of unmanned aerial vehicles (e.g., drones), they have become a new source of data collectors equipped with a variety of different sensors. In this paper, we propose a novel SC scenario, enabling human participants to work collaboratively with drones in the presence of multiple charging stations to achieve certain data collection tasks, like videography and surveillance. We propose a novel deep reinforcement learning (D RL) framework called “FD- MAPPO (Cubic Map)”, which consists of a fully de-centralized multi-agent DRL (MADRL) algorithm called “Fully Decentralized Multi-Agent Proximal Policy Optimization (FD-MAPPO)”, and a spatiotemporal memory augmented neural network with novel cubic writing and spatially contextual reading mechanisms called “Cubic Map”. Cubic Map extracts long-term spatiotemporal features, navigates drones to accurately locate the position of the target, i.e., charging stations or sensors. Extensive results on two real datasets of KAIST and NCSU campuses show that FD- MAPPO (Cubic Map) consistently outperforms six other baselines in terms of efficiency.

Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called “$t$t-LocPred”. It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods (“ConvAoI”); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model (“mem-attLSTM”) to capture long-term behavior patterns. Experimental results show that$t$t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.