Sparse Mobile CrowdSensing (SMCS) is an emerging distributed data collection framework. As one of the core methods,data completion uses the collected data to fill in the missing data. However, this approach inevitably poses significant privacy risks, since the traditional data completion methods require users' time and location information. In this paper, we propose a federated learning-based distributed data completion framework, which employs matrix factorization (MF) for local completion model training and federated learning to aggregate parameters of the MF model. This enables the construction of a global completion model without requiring private data, thereby mitigating privacy concerns. To address the challenges posed by the sparsity and asynchrony of distributed data, we incorporate time-aware deep neural network architectures with an asynchronous mechanism to enable federated training under irregularly gathered local data. Experimental results demonstrate that the proposed model achieves high data completion accuracy while ensuring robust privacy protection.

Mobile crowdsensing (MCS) is a paradigm for data collection with the limitation of budgets and worker availability. The central strategy of MCS is recruiting workers to sense a part of data and subsequently infer the unsensed data. To infer unsensed data, prior research has proposed several algorithms that do not require historical data, but their inference accuracy is very limited. More effective works are training a model with sufficient historical data. However, such methods can’t infer data with few to none historical data. A more promising strategy is training models from other similar datasets that have been sensed. However, such datasets are different in terms of sensing locations, numbers of sensed data and data types. Such variance introduces the complex issue of integrating knowledge from these datasets and then training inference models. To solve these, we propose a meta-knowledge based multiple data inference framework named META-MCS. In META-MCS, we propose a similarity evaluation model TMFS. Following this, we cluster similar datasets and train generalized models for each cluster. Finally, META-MCS selects an appropriate model to infer unsensed data. We validate our proposed methods through extensive experiments using ten different datasets, which substantiate the effectiveness of our framework.

Sparse Mobile Crowdsensing is an emerging paradigm for data collection with budgets and workers' limitations' which recruits workers to sense a part of spatio-temporal data and infer what is unsensed. In order to achieve high inferring accuracy in all spatio-temporal areas, we need to measure the importance level of each area and sense some important ones. Existing works usually use the average distance or the difficulty level inferred by historical data to measure the area's importance. However, we argue that neither distance nor difficulty level is suitable for measuring the importance. A better approach is inspired by the data itself, i.e., data similarity among different areas. Furthermore, there usually exist multiple data types in sparse mobile crowdsensing, which leads to a more complex inference from two-dimensional data (spatial and temporal) to three-dimensional data (spatial, temporal, and data type). In this paper, we study worker recruitment in a multi-task scenario, which aims to recruit workers to sense important data for a three-dimensional inference. Specifically, we first present the SWDTW method to calculate data similarity, which is used to evaluate data importance. Based on this, we further propose an evaluation model for three-dimensional data similarity and measure the importance of each area. Finally, inspired by generalized greedy and simulated annealing, we propose a worker recruitment method named WRGSA, the target of which is selecting workers to sense important areas to enhance the inference accuracy. Extensive experiments have been conducted over three real-world datasets with multiple data types, which can verify the effectiveness of our proposed methods.

The prosperity of industrialization urges modern ride-sharing platforms to gain profit from efficient management of their resources. Although ride-sharing allows sharing costs and promotes the traffic efficiency by making better use of vehicle capacities, dealing with large amounts of online taxi orders is an inevitable challenge in the current transportation systems, where all drivers have to make immediate and irrevocable decisions about whether to accept current order in a parallel way. Furthermore, in order to achieve global fairness, it is critical for an algorithm to function whenever the first order gets on-line without any observation stage. In this paper, we formulate this online user selection problem as a prophet inequality for independent identically distributed random variables from an unknown distribution. We construct a sample set to avoid the observation stage in an online decision process. Considering the driver-centered ride-sharing scenario, a route schedule algorithm and a sample-driven algorithm with a guarantee of lower bound are proposed to concurrently guide taxi drivers to accept taxi orders and achieve global fairness at the meantime. Finally, we conduct extensive evaluations based on three real-world data sets. The results verify the effectiveness of our proposed algorithm on improving the overall profit, increasing accepted orders and reducing the unoccupied time of the vehicle under the valid ride-sharing constraints.