With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, this paper proposes a task allocation model considering the attributes of social networks for mobile crowdsourcing system. Starting from the homogeneity of human beings, the relationship between friends in social networks is applied to mobile crowdsourcing system. A task allocation algorithm based on the friend relationships is proposed. The GeoHash coding mechanism is adopted in the process of calculating the strength of worker relationship, which effectively protects the location privacy of workers. Utilizing synthetic dataset and the real-world Yelp dataset, the performance of the proposed task allocation model was evaluated. Through comparison experiments, the effectiveness and applicability of the proposed allocation mechanism were verified.

In this letter, we propose a robust cross-drone multitarget association algorithm that aims to associate the identities of targets detected by different drones with overlapping FOVs. Unlike other methods using image features (target appearance features and background features), our approach makes full use of the three-dimensional spatial consistency (TSC) between targets. It has wider applicability in deployed overlapping crossdrone systems with unreliable image features. Specifically, we first explore target correlation from the perspective of three dimensional space, and propose an association method that integrates position and altitude consistency constraints, without relying on image features. After that, we mine topological mapping relationships (TMR) between cameras from the preliminary results and propose a TMR-based re-association approach using cost evaluation to optimize the preliminary results, which effectively improve association accuracy. Extensive experimental evaluations on Airsim-based dataset verify the effectiveness and robustness of our proposed method. The code is available at https://github.com/pantingwei/TSCTMR .

This paper develops a framework for geolocating a ground moving target with images taken from an unmanned aerial vehicle (UAV). Unlike the usual moving target geolocation approaches that rely heavily on a laser rangefinder, multiple UAVs, prior information of the target or motion assumptions, the proposed framework performs the geolocation of a moving target with monocular vision and does not have any of the above restrictions. The proposed framework transforms the problem of moving target geolocation to the problem of stationary target geolocation by matching corresponding points. In the process of corresponding point matching, we first propose a Siamese-network-based model as the base model to match corresponding points between the current frame and the past frame. Besides the introduction of a base model, we further designed an enhanced model with two outputs, where a row-ness loss and a column-ness loss are defined for achieving a better performance. For the precision of corresponding point matching, we propose a compensation value, which is calculated from the outputs of the enhanced model and improves the accuracy of corresponding point matching. To facilitate the research on corresponding point matching, we constructed a dataset containing various aerial images with corresponding point annotations. The proposed method is shown to be valid and practical via the experiments in simulated and real environments.

This paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of the known target’s altitude, the proposed framework does not have the same limitations and performs geolocation of moving targets utilizing dual UAVs equipped with the low-quality navigation state sensors. Considering the Gaussian measurement errors and yaw-angle measurement bias provided by low-quality sensors, we first propose an epipolar constraint-based corresponding-point-matching method, which enables the historical measurement data to be used to estimate the current position of the moving target; after that, we propose a target altitude estimation method based on multiview geometry, which utilizes multiple images, including historical images, to estimate the altitude of the moving target; finally, considering the negative influence of yaw-angle measurement bias on the processes of target altitude estimation and parameter regression, we take advantage of multiple iterations among the two processes to accurately estimate the moving target’s two-dimensional position and the yaw-angle measurement biases of two UAVs. The effectiveness and practicability of the framework proposed in this paper are proved by simulation experiments and actual flight experiments.

With the widespread use of GPS-enabled smart devices and the increased availability of wireless networks, mobile crowdsourcing system (MCS) has recently been proposed as a framework that automatically requests workers to perform location sensitive tasks. In the task allocation problem of MCS, existing algorithms lack consideration of the impact of nonadjacent and discontinuous execution of tasks on task allocation. Workers may have different task preferences in different time periods. Nonadjacent and discontinuous tasks provide important correlations for understanding workers’ behavior. This article introduces a novel task allocation algorithm based on spatiotemporal attention network (STATA). STATA takes into account factors such as the spatiotemporal distribution of tasks and workers as well as the location preferences and abilities of workers, and integrates them into a unified network for modeling. First, all historical tasks performed by workers are aggregated to obtain the correlation of all historical tasks. Then, the most plausible candidate tasks are recalled from the weighted representation for allocation. STATA utilizes the spatiotemporal attention mechanism to capture the relationship between these factors, ultimately improving the accuracy of task allocation. Extensive experiments demonstrate that the STATA model exhibits superior performance in terms of task allocation accuracy and practical application capabilities.