Energy and cost are two primary concerns when leveraging drones for urban sensing. With the advances of wireless charging technologies and the inspiration from the sparse crowdsensing paradigm, this paper proposes a novel drone-based collaborative sparse-sensing framework DroneSense, demonstrating its feasibility for sustainable urban-scale sensing. We focus on a typical use case, i.e., leveraging DroneSense to sense open parking spaces. DroneSense selects a minimum number of Points of Interest (POIs) to schedule drones for physical data sensing and then infers the parking occupancy of the remaining POIs to meet the overall quality requirement. However, drone-based sensing is different from human-centric crowdsensing, resulting in a series of new problems, including which POIs are visited first, when and where to charge drones, which drones to charge first, how much to charge, and when to stop the scheduling. To this end, we design a holistic solution, including context-aware matrix factorization for parking occupancy data inference, progressive determination of task quantity, deep reinforcement learning (DRL) based task selection, energy-aware DRL-based task scheduling, and adaptive charger scheduling. Extensive experiments with a real-world on-street parking dataset from Shenzhen, China demonstrate the obvious advantages of DroneSense.

Mobile crowdsensing (MCS) and unmanned vehicle sensing (UVS) provide two complementary paradigms for large-scale urban sensing. Generally, MCS has a lower cost but often confronts sensing imbalance and even blind areas due to the limitation of human mobility, whereas UVS is often capable of completing more demanding tasks at the expense of limited energy supply and hardware cost. Thus, it is significant to investigate whether we could integrate the two paradigms for high-quality urban sensing in a cost-effective collaborative way. However, it is nontrivial due to complex and long-term optimization objectives, uncontrolled dynamics, and a large number of heterogeneous agents. To address the collaborative sensing problem, we propose an actor-critic-based heterogeneous collaborative reinforcement learning (HCRL) algorithm, which leverages several key ideas: local observation to handle expanded state space and extract the states of neighbor nodes, generalized model to avoid environment nonstationarity and ensure the scalability and stability of network, and proximal policy optimization to prevent the destructively large policy updates. Extensive simulations based on a mobility model and a realistic trace data set are conducted to confirm that HCRL outperforms the state-of-the-art baselines.