This article investigates an unmanned-aerial-vehicle (UAV)-assisted Internet of Things system, where multiple UAVs take off from a data center (DC), collect data from multiple ground sensor nodes (SNs), distribute data for multiple users, and finally return to DC. A centralized information-sharing mechanism is designed among UAVs before data distribution, such that UAVs’ repeatedly visiting the same user can be avoided to save energy and time. A nonconvex Age-of-Information (AoI) minimization problem is formulated, where the task assignment, selection of interaction point (IPT), and UAVs’ trajectories are optimized jointly. It is decomposed into two subproblems: one is a joint task assignment and UAV trajectory optimization problem, which is solved by a multipopulation-based genetic algorithm (MPGA), as well as a hybrid algorithm combining dynamic programming (DP) with an improved $K$ -means clustering; and the other is an IPT selection problem, which is modeled as a Fermat–Weber location problem and is solved by the convex optimization technique. Simulation results show that the MPGA performs better than the hybrid algorithm as well as several benchmark algorithms.

Missing data are unavoidable in wireless sensor networks, due to weather, equipment failure or some uncontrollable human factors. This results in data sets incompleteness, which decreases the performance of related applications greatly. Although many related researches have been carried out to recover missing data, most of them are not effective enough when dealing with multiple sensor data series simultaneously. Moreover, few studies have made the best of the characteristics of these sensor data. In this paper, we have taken up this challenge by employing an efficient approach based on matrix factorization (MF) with any weights. First, to improve the accuracy of missing data recovery, we make full use of the interior patterns of each data series and the correlation information across multiple sources to constrain the objective function of MF. Then, to reduce the time complexity of solving the MF parameters, an elementwise alternating least squares algorithm is proposed. Further, we apply a series of caching strategies to optimize the learning process of parameters. Simulation results on real-world data sets demonstrate that the approach proposed is a promising approach to ensure the data quality in wireless sensor networks.

Data prediction research is very important for applications based on the Internet of things (IoT). However, the large-scale, high-dimension, and the inevitable missing values seriously hinder accurate prediction result. To meet this challenge, an integrated online prediction model is proposed, which can solve future values prediction and missing values imputation simultaneously in the low-dimensional space. Specifically, we embed the classic prediction models into the objective function of matrix factorization (MF) to obtain low-dimensional embedding parameters, which are then used to construct the prediction model. Furthermore, a rolling prediction mechanism is proposed to update the prediction model when new sampled data comes, based on the online MF with time-varying parameters. Simulation results on four UCI public data sets show the superior performance of the proposed method.

The existence of missing data severely affects the establishment of correct data mining model from the raw data. Unfortunately, most of the existing missing data prediction approaches are inefficient to predict missing data from multivariable time series due to the low accuracy and poor stability property. To address this issue, we propose an efficient method using the novel Kronecker compressive sensing theory. First, we exploit the spatial and temporal properties of the multivariable time series to construct the sparse representation basis and design the measurement matrix according to the location of missing data. Accordingly, the missing data prediction problem is modeled as a sparse vector recovery problem. Then, we verify the validity of the model from two aspects: whether the sparse representation vector is sufficiently sparse and the sensing matrix satisfies the restricted isometry property of compressive sensing. Finally, we investigate the sparse recovery algorithms to find the best suited one in our application scenario. Simulation results indicate that the proposed method is highly efficient in predicting the missing data of multivariable time series.