In recent years, battery swapping services have experienced rapid growth, positioning themselves as a significant alternative to traditional charging stations. One determinant of their success lies in the ability to continuously refine the battery swapping station network, i.e., redistributing batteries across stations, to meet the fluctuating demands of electric scooters. However, achieving such network refining is far from straightforward and presents two major challenges. First, for regions where stations have been recently deployed, the lack of historical data makes it difficult to accurately predict user demand. Secondly, effective battery scheduling is inherently complex, as it requires meticulous coordination across all stations to dynamically balance users’ fluctuating long-term demand. To tackle these challenges, we propose dRefine, a novel data-driven battery swapping station network refinement system. Specifically, to address the first challenge, we develop a station-level spatiotemporal representation-guided conditional diffusion model, which leverages data from regions with established networks to predict demand in regions lacking historical data. For the second challenge, we develop a demand-oriented deep reinforcement learning model that dynamically optimizes battery scheduling strategies. By continuously learning from real-time demand patterns and operational feedback, it ensures efficient and adaptive battery redistribution across the entire network. We evaluate dRefine using a real-world dataset encompassing 388 stations, 41,358 batteries, and 108,574 electric scooter users. Extensive experimental results demonstrate that our method consistently outperforms state-of-the-art approaches by an average of 29.28%.

In recent years, drone-based wireless data collection has emerged as a significant research hotspot in the Internet of Things (IoT) field. However, most existing studies primarily focus on dedicated drones, which incur high deployment costs. In contrast, reusing existing delivery drones provides a cost-effective alternative. This paper concerns the problem of scheduling delivery drones during their delivery to enable cost-efficient wireless data collection. Specifically, the problem aims to maximize the data collection amount while subject to the constraints of the drone’s battery capacity and package delivery deadlines. Nevertheless, solving this problem is challenging due to the involvement of three coupled variables: collection task allocation, collection time distribution, and flying path planning. To tackle this challenge, we propose MdSche, a multi-delivery drone scheduling scheme for wireless data collection. Specifically, we first design a time slicing-based data collection time discretization algorithm, which transforms the original problem into a Multi-Drone Scheduling Problem (MDSP) involving two variables. Then, an auxiliary graph construction method is employed to decompose MDSP into multiple independent single-drone scheduling subproblems. Finally, we construct a surrogate function to simplify each subproblem into a collection task allocation problem with only one variable. Theoretical analysis verifies that MdSche achieves an approximation ratio of 1/2(1 − (1/e)^1/4 ) in polynomial time. Extensive trace-based simulations show that, compared to five baseline methods, our approach improves the data collection amount by an average of 69.68%.