Data gathering is the basis of monitoring applications in an underwater sensor network, and excellent network coverage and data transmission reliability are the guarantees for the quality of monitoring tasks. However, the energy consumption of the nodes is too fast due to the heavy load of the cluster heads closer to the sink when data is transmitted between cluster heads (CHs) and the sink by multihop, which leads to an energy hole problem in an underwater sensor network of clustering technology. Aiming to address this problem, we propose a dynamic hierarchical clustering data gathering algorithm based on multiple criteria decision making (DHCDGA) in a 3D underwater sensor network. Firstly, the entire monitoring network is divided into many layers. For selecting a cluster head in each layer, multiple criteria decision making of an intuitionistic fuzzy Analytic Hierarchy Process (AHP) and hierarchical fuzzy integration is adopted. Furthermore, a sorting algorithm is used to form a clustering topology algorithm to solve the problem that there is the only node in one cluster. Then, an energy-balanced routing algorithm between clusters is proposed according to the residual energy of the node, the depth, and the number of neighbor nodes. Finally, the simulation results show that DHCDGA can not only effectively balance the energy consumption of the network and prolong the network lifetime but also improve network coverage and data gathering reliability.

Classical decision trees such as C4.5 and CART partition the feature space using axis-parallel splits. Oblique decision trees use the oblique splits based on linear combinations of features to potentially simplify the boundary structure. Although oblique decision trees have higher generalization accuracy, most oblique split methods are not directly conducive to the categorical data and are computationally expensive. In this paper, we propose a multiway splits decision tree (MSDT) algorithm, which adopts feature weighting and clustering. This method can combine multiple numerical features, multiple categorical features, or multiple mixed features. Experimental results show that MSDT has excellent performance for multiple types of data.

Due to the imbalanced energy consumption among nodes, some nodes die prematurely, which decreases the network lifetime in wireless sensor networks, especially many nodes and one Sink. To solve this problem, in this paper, we propose an energy-efficie

Reversible data hiding (RDH) is a crucial information hiding technique for copyright protection and integrity verification of digital media, as it ensures the original content can be perfectly restored. However, current research on RDH remains predominantly focused on images, and relatively little attention has been paid to three-dimensional (3D) models. To address this gap, this paper proposes a RDH method for 3D mesh models based on spatial polygon prediction and dual sorting. First, a three-layer vertex division mechanism is introduced to enable a two-round embedding process, thereby expanding the number of embeddable vertices and increasing embedding capacity. Then, a spatial polygon prediction scheme is designed to balance the dual objectives of enhancing prediction accuracy and preserving embedding capacity. Furthermore, a dual sorting strategy that jointly considers angular smoothness and edge-length regularity is proposed to prioritize the smoother embedding units for data embedding, leveraging their smaller prediction errors to reduce geometric distortion. Finally, secret data is reversibly embedded into 3D mesh models using a prediction error expansion (PEE) technique guided by the dual sorting. The experimental results demonstrate that the proposed method achieves a better balance between high embedding capacity and low geometric distortion compared with other state-of-the-art spatial-domain RDH methods.