Wireless sensor networks (WSNs) are being applied not only in the traditional way with a limited number, but also allow connecting a large number of high-quality sensor network nodes for practical problems as shown in Figure 1. Technical issues of the WSNs are broad, including spectrum sensing [11], broadband data transmission [12], and recharging rechargeable WSNs [13; 14]. Due to their rapid development in recent years, deep learning techniques allow us to improve the performance of sensor networks used in communication protocols, intelligent transportation systems, and smart charging schemes, especially for vehicles [15; 16]. To date, light detection and ranging (LiDAR) is considered one of the most outstanding sensor systems helping autonomous vehicles with perception of their surroundings. LiDAR scans the objects creating sets of data called point clouds, which are continuously aligned with each other to create 3D models through point cloud registration. 3D point cloud registration plays a fundamental role in robotics applications. Many systems use it for applications of autonomous driving, such as LiDAR-based SLAM [17; 18], or archaeological inspection via 3D model reconstruction. Given two partially overlapped sets of 3D points, the goal of registration is finding the best Euclidean relative transformation to align these fragments according to a common frame, thus obtaining a successful point cloud registration. Existing solutions can be categorized into two main methods. Methods that iteratively minimize the distance between nearest-neighboring points with good initialization, e.g., iterative closest point (ICP) [19] and its variants [20; 21; 22] are popular approaches. Other methods randomly sample corresponding points through robust descriptor matching, e.g., using random sample consensus (RANSAC) [23]. Unfortunately, the key challenge of finding 3D-3D data correspondences is non-trivial. This problem is particularly challenging because (1) different point clouds can be captured with different angles, and (2) the raw 3D scans are noisy and have various densities; thus, descriptor-based matching often increases robustness across different scenarios. To compute compact 3D descriptors, there are one-stage [24; 25; 26; 27; 28] or two-stage [29; 30; 31] methods. The former directly encodes the local geometric information of raw points (e.g., collection of coordinates). On the other hand, the latter firstly determines a local reference frame  
Figure 1: Autonomous vehicles and their wireless sensor networks. (LRF) from the points to transform the whole patch into suitable representations; they then encode the information of these new representations into a generalized descriptor. Recent descriptors based on deep learning [25; 26; 32] have outperformed their hand-crafted counterparts [29; 33] because of the availability of large-scale labeled datasets. The methods based on one-stage learning can achieve rotation invariance by encoding the local geometric information with a set of geometric relationships, and then by learning descriptors via a deep network to achieve generalization ability and permutation invariance. In two-step methods, the LRFs can be computed and transformed into a voxel grid, where each voxel contains the information of inside points. Finally, descriptors can be encoded from the voxelization by using ConvNet [34]. The SpinNet [35] is a new neural architecture which projects the point cloud into a cylindrical space; then the descriptor is invariably extracted rotationally by a special convolutional neural layer. Inspired by the success of GAT for computer vision tasks, [PERSON] et al. [4] proposed a multiplex dynamic graph attention network matching method (MDGAT-matcher). It uses a novel and flexible graph network architecture on the point cloud and achieves the enriched feature representation by recovering local information. Although MDGAT-matcher finds high-quality data associations, its architecture provides space for improvements.