In recent years, deep learning-based multi-source data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, has gained significant attention in the field of remote sensing. However, the traditional convolutional neural network fusion techniques always provide poor extraction of discriminative spatial–spectral features from diversified land covers and overlook the correlation and complementarity between different data sources. Furthermore, the mere act of stacking multi-source feature embeddings fails to represent the deep semantic relationships among them. In this paper, we propose a cross attention-based multi-scale convolutional fusion network for HSI-LiDAR joint classification. It contains three major modules: spatial–elevation–spectral convolutional feature extraction module (SESM), cross attention fusion module (CAFM), and classification module. In the SESM, improved multi-scale convolutional blocks are utilized to extract features from HSI and LiDAR to ensure discriminability and comprehensiveness in diversified land cover conditions. Spatial and spectral pseudo-3D convolutions, pointwise convolutions, residual aggregation, one-shot aggregation, and parameter-sharing techniques are implemented in the module. In the CAFM, a self-designed local-global cross attention block is utilized to collect and integrate relationships of the feature embeddings and generate joint semantic representations. In the classification module, average polling, dropout, and linear layers are used to map the fused semantic representations to the final classification results. The experimental evaluations on three public HSI-LiDAR datasets demonstrate the competitiveness of the proposed network in comparison with state-of-the-art methods.

Wireless sensor network is the momentous part of the Internet of Things. Data aggregation technology is the most practical method to reduce the amount of communication among nodes. Adding a privacy protection mechanism to data aggregation is one of the important means for privacy protection and security in wireless sensor networks. Aiming at certain performance defects of the existing SMART (Slice-Mix-AggRegaTe) privacy protection algorithms, a fat tree-based data aggregation privacy protection algorithm is proposed in this paper, which is referred to as the FTSMART (Fat Tree Slice-Mix-AggRegaTe) algorithm. Concerning the innovative algorithm, the fat tree (FT) is introduced, the fat tree structure is adopted to optimize the data slicing scheme and the aggregation tree generation scheme, and the allocation of fixed time intervals is employed for nodes to reduce the transmission collision in the data aggregation process and to guarantee the completion of the data transmission. The simulation experiment results demonstrate that the FTSMART algorithm has presented the favorable performance in terms of the privacy protection, the network communication overhead, and the data aggregation accuracy.

The data acquisition of Internet of Things (IoT) is mostly brought out by wireless sensor networks (WSNs), and the efficiency of IoT is directly affected by the time delay in the process of data acquisition, which is researched mainly by the data aggregation of WSNs. Minimizing the delay in the data aggregation process is one of the most important operations. In order to reduce the data aggregation delay, a hybrid delay‐minimization data aggregation scheduling (HDDAS) algorithm is proposed, which optimizes the delay from the two main aspects that affect the delay, namely, the transmission path and the collision rate. Accordingly, the algorithm is divided into two phases: the aggregation tree construction and the data transmission scheduling. In the aggregation tree construction phase, the fat tree (FT) is introduced to generate an optimal aggregation tree with the shortest path; in the data transmission scheduling phase, the maximum parallel transmission set (MPTS) is adopted to increase the amount of the collision‐free data transmission. The simulation experiment results demonstrate that the HDDAS algorithm proposed in the paper has performed favorably in terms of the data aggregation delay.