In this paper, we leverage unmanned aerial vehicles (UAVs) to enhance mobile crowd sensing (MCS) by addressing two critical challenges: uncontrollable data quality and inevitable unsensed points of interest (PoIs). We introduce a UAV-assisted method to deal with these challenges. To ensure the accuracy of sensing data contributed by human participants, the proposed truth discovery method utilizes UAV-collected sensing data as few-shot samples to train the truth discovery model, which is then employed to calibrate sensing data solely collected by human participants. Additionally, to meet the sensing coverage requirement, we present a method that predicts data values for unsensed PoIs by utilizing their historical sensing data and the sensed neighboring PoIs information. The method employs a graph neural network to capture spatio-temporal relationships of the sensing data, facilitating accurate estimation of unsensed PoIs. Through extensive simulations, our approaches demonstrate superior performance compared to existing methods, showcasing the potential of UAV-assisted MCS for overcoming challenges and enhancing data collection efficiency in various domains.

Multi-planar magnetic resonance imaging (MRI) can provide comprehensive 3D structural information for disease diagnosis. Compared to multi-source MRI, multi-planar MRI scans target areas in the human body from different directions. This atypical difference between directions may lead to poor performance of traditional domain generalization methods, especially when MRI from different planes also comes from different sources. In this paper, we propose ADDG, an Adaptive Domain Generalization framework for accurate cross-plane MRI segmentation. ADDG significantly mitigates the impact of information loss caused by slice spacing by injecting 3D shape prior to the segmentation target and capturing domain-agnostic feature differences from heterogeneous data sources through an adaptive data partitioning strategy. In addition, we propose a mesh deformation-based organ segmentation network to simultaneously delineate 2D boundary and 3D volume of organ, which could guide more accurate mesh deformation. We also develop an organ-specific mesh template and employ Loop subdivision for generating smoother 3D organ mesh. Furthermore, we design a flexible meta-learning paradigm to adaptively partition data domains based on invariant learning, which can learn domain-agnostic features from multi-source data to enhance the overall generalization ability. Experimental results show that ADDG outperforms several medical image segmentation, single-view 3D shape reconstruction, and domain generalization methods.

Anisotropy arises due to the influence of scanning equipment and parameters, resulting in a distance between slices that is often much greater than the actual distance represented by a single pixel within each slice. This can lead to inefficiency or ineffectiveness in 3D convolution. To address the anisotropy issue, we propose FOrViG, an asymmetric vision graph neural network (GNN) framework that captures the correlation between different slices by constructing a graph for multi-slice images and aggregating information from adjacent nodes. This allows FOrViG to efficiently extract 3D spatial scale information, and effectively identify feature nodes associated with small lesions efficiently, thereby improving the accuracy of lesion segmentation on anisotropic 3D multimodal MR images. As far as we know, this is the first study that adopts GNN to address anisotropy issues. Additionally, we also design a factorized omnidirectional representation method and a supervised multi-perspective contrastive learning strategy to enhance the capability of FOrViG in learning multi-scale omnidirectional presentation information, graphics construction, and distinguishing foreground from background. Extensive experiments on the PI-CAI dataset demonstrate that FOrViG significantly outperforms several state-of-the-art 3D segmentation algorithms.

Mobile crowd sensing (MCS) with human participants has been proposed as an efficient way of collecting data for smart cities applications. However, there often exist situations where humans are not able or reluctant to reach the target areas, due to for example traffic jams or bad road conditions. One solution is to complement manual data collection with autonomous data collection using unmanned aerial vehicles (UAVs) equipped with various sensors. In this paper, we focus on the scenarios of UAV-assisted MCS and propose a task allocation method, called “UMA” (UAV-assistedMulti-taskAllocation method) to optimize the sensing coverage and data quality. The method incentivizes human participants to contribute sensing data from nearby points of interest (PoIs), with a limited budget. Meanwhile, the method jointly considers the optimization of task assignment and trajectory scheduling. It schedules the trajectories of UAVs, considering the locations of human participants, other UAVs and PoIs which are rarely visited by human participants. In detail, UAVs take care of two tasks in our proposal. One is to calibrate the data collected by the human participants whom the UAVs come across along their trajectories. The other is to collect data from the PoIs which are not covered by other UAVs or human participants. We apply deep reinforcement learning to schedule UAVs moving trajectories and sensing activities in order to minimize the overall energy cost. We evaluate the proposed scheme via simulation using two real data sets. The results show that our proposal outperforms the compared methods, in terms of coverage completed ratio, calibrating ratio, energy efficiency, and task fairness.

Mobile crowd sensing (MCS) acts as a key component of Internet of Things (IoT), which has attracted much attention. In an MCS system, participants play an important role, since all the data are collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high-quality data. In this article, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants' profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations in which relatively few data have so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), which employs semi-Markov model and game theory to predict the quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality, and truthfulness. We evaluate the proposed scheme via simulation using three real data sets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with the other two methods.

Mobile crowd sensing is a new paradigm that enables smart mobile devices to collect and share various types of sensing data in urban environments. However, new challenges arise: one is how to evaluate the quality of data each mobile user potentially is capable of providing; another is how to allocate a satisfactory yet profitable amount of reward to mobile users in order to keep them participating in crowd sensing tasks. In this paper, we first introduce a mathematical model for characterizing quality of sensing data to be contributed by mobile users. Then, we present a utility function and formulate an optimization problem for the platform, who recruits participants to contribute sensing data, to maximize the amount of high quality sensing data under a limited task budget. We next present an effective and quality-aware incentive mechanism to solve this problem for online scenarios where participants may arrive or leave at any random time. Moreover, the proposed incentive mechanism allows the platform to provide selected participants with an extra bonus according to task completion level and their previous performance to motivate them further. We formally show the proposed mechanism has the desirable properties of truthfulness, individual rationality, budgetary feasibility, and computational efficiency. We compare the proposed scheme with existing methods via simulation using a real dataset. Extensive simulation results well justify the effectiveness and robustness of the proposed approach, e.g, compared with another online method “OMG”, the gap to the optimum for our proposed Online-QIM approach is reduce by 33.3 percent when budget B = 1000.

Highly resolved and accurate air pollution maps are valuable resources for many issues related to air quality including exposure modeling and urban planning. Due to the high equipment costs, there are limited high quality monitoring stations (HQMS) in cities. In order to achieve high resolution air pollution maps, a large number of mobile sensors are required. Besides, mobile sensors require frequent calibrations with the HQMS to maintain data accuracy. Existing work on route design for mobile sensors largely focuses on data reconstruction, which either ignores calibration or views it as an independent problem. To improve the accuracy of data reconstruction, this paper proposes a novel scheme that jointly considers sensor calibration and data reconstruction in route design for mobile sensors. We formulate a novel sensor route planning problem (SRPP) which aims to maximize the mutual information and guarantee the accuracy of measurements through sensor calibration. A heuristic algorithm is proposed to solve the SRPP, which supports calibration between mobile sensors and HQMS in route planning. Simulation results show that, compared with traditional approach, our approach can reduce 83% root mean square error (RMSE) on average.

Mobile Crowd sensing is a new paradigm that encourages ordinary people to collect and share sensing data with their smart devices. However, the uncontrollable data quality is one of the critical problems that is potentially harmful to guarantee the availability and preciseness of mobile crowd sensing based services. In this paper, we propose a quality aware data collection mechanism based on a realistic scenario where participants arrive and report their conditions sequentially one by one. When a participant arrives, the mechanism first forecasts the amount of high quality data he#x002F;she may contribute by employing the expectation of Binomial-Poisson distribution, and then a two-level iterative algorithm is employed to calculate its parametric values. After, our designed mechanism decides to select the participant or not by combining with his#x002F;her requested reward. Extensive simulation results well justify the effectiveness and robustness of our approach, compared with another schemes.