Along with the advance of low-altitude economy, a variety of applications based on Unmanned Aerial Vehicles (UAVs) have been developed to accomplish diverse tasks. In this paper, we focus on the scenario of multiple UAVs performing Federated Learning (FL) tasks. Specifically, a group of UAVs is scheduled to repeatedly visit some Points of Interest (PoIs), collect the data produced by these PoIs, and jointly train a machine learning model based on the collected data. The most challenging issue is how to schedule UAVs to collect data so as to optimize the generalization and convergence of model training under the case that the distributions of the data produced by PoIs have not been known in advance. To address this issue, we propose a novel framework for online FL on distributed unknown data, named OFL-UD2, which is dedicated to online decision-making for UAVs to optimize model training performance. Concretely, we formulate the optimization problem while considering the convergence and quality of trained models as well as energy constraints. Then, we define a utility metric for the data quality of different PoIs and conduct a rigorous convergence analysis for OFL-UD2. Based on the analysis results, we design a two-stage algorithm to determine the scheduling of UAVs. Extensive simulations demonstrate that OFL-UD2can improve model accuracy and speed up running time compared to existing benchmarks significantly.

The registration of LiDAR point clouds between Connected Vehicles (CV s) is the basis for vision tasks such as multi-vehicle cooperative perception and high-precision scene reconstruction. Due to the huge amount of LiDAR point cloud data and the complexity of computation, the registration has high transmission latency and execution time. Fast and accurate point cloud registration between CV s is urgently required by safe driving applications. To achieve faster registration, we propose partitioning overlapping point clouds for registration in advance. Meanwhile, for higher accuracy, we incorporate more geometric features during the registration. This paper demonstrates the Echo Registration System (ERS) developed to increase the speed of existing point cloud registration methods. ERS employs an initial positioning based on point cloud distributions, a density-consistent partition strategy, and a virtual geometric feature fusion. We deploy ERS to accelerate multiple state-of-the-art registration methods. The comprehensive results show that ERS improves the overall running time of current state-of-the-art baselines by 5.7 x with 42.1 % registration recall gains.

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate the incentive mechanism design in MCS systems, taking the freshness of collected data and social benefits into consideration. First, the Age of Information (AoI) metric is introduced to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as an incomplete information two-stage Stackelberg game with multiple constraints. Next, we consider the scenario that all participants share the public utility function parameters of the Stackelberg game. By deriving the optimal remuneration paid by the platform and the optimal data update frequency for each worker, and proving the existence of a unique Stackelberg equilibrium, we propose an AoI-guaranteed Incentive Mechanism (AIM) that enables the platform and all workers to maximize their utilities simultaneously. Furthermore, we extend AIM to a general scenario where each participant has no prior knowledge of the utility function parameters of the game. By resorting to the Deep Reinforcement Learning (DRL) technique and modeling the two-stage Stackelberg game as a Markov decision process, we propose a DRL-based Incentive Mechanism (DIM) with AoI guarantees, which makes each participant effectively seek its optimal strategy through trial and error. Meanwhile, the system can guarantee that the AoI values of all data uploaded to the platform are not larger than a given threshold. Finally, numerical experiments on real-world traces are conducted to validate the efficacy and efficiency of AIM and DIM.

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI.