## 1 Introduction  
As one of the most influential technological inventions in the field of information technology, theInternet of Things (IoT) has attracted worldwide attention [1; 2]. Presently, IoT technology is widely used in various areas such as smart grid [3], smart home [4], military equipment logistics [5], intelligent medical treatment [6], ecological-environmental protection [7], and disaster warning [8]. The biggest breakthrough of IoT is providing a way to combine the physical world and information world to gradually change the interactions between humans and nature. IoT is regarded as the third wave of the global information industry after personal computer and Internet.  
One valuable application of IoT is utilizing three-dimension underwater acoustic sensor networks (3D UASNs) to perform marine environmental monitoring for the exploitation, utilization, and protection of marine resources [9]. Figure 1 shows a typical scenario of the 3D UASN application.  
Numerous sensor nodes are anchored at various depths in the ocean by buoys or cables, allowing information acquisition of the underwater surroundings. An autonomous underwater vehicle (AUV) is employed to assist information transmission. The monitoring information is finally delivered to the seashore control center via acoustic links and radio frequency (RF) links.  
Data collection is a very typical application scenario of UASNs. With the advancement of secure underwater acoustic communication technology [10; 11] and AUVs technology, AUVs have been widely adopted to facilitate data collection in UASNs. The utilization of AUVs can efficiently balance energy consumption of networks, reduce the energy costs of nodes, and thereby extend the survival time of UASNs. Furthermore, the use of AUVs can also address the frequent disconnection problem of networks caused by water currents. In the past few years, great advances have been made in the auxiliary data collection of AUVs for UASNs.  
However, since most existing studies of underwater data collection are based on 2D scenarios, related schemes cannot be directly applied to 3D UASNs. In addition, because of the specific propagation effects resulting from the use of acoustic communication channels, the successful information delivery probability decreases progressively with distance. In this work, we investigate two probabilistic neighborhood-based AUV auxiliary data collection algorithms based on a probabilistic acoustic communication model for 3D UASNs.  
The main contributions of this paper include: (1) design of the grid and probabilistic neighborhood-based data collection algorithm with layered-scan (GPN-LSCAN) for 3D UASNs with unknown deployment knowledge by partitioning the network into grids; (2) design of the probabilistic neighborhood covering set-based greedy heuristic algorithm (PNCS-GHA) for 3D UASNs with pre-known deployment knowledge; (3) extension of the probabilistic neighborhood-based minimum covering set construction algorithm to decrease data latency.  
The remainder of this paper is organized as follows. Section 2 presents the related work regarding the underwater data collection problem. Section 3 describes the network model and probabilistic communication model in our paper. Section 4 investigates two probabilistic neighborhood-based data collection algorithms for 3D UASNs. Simulation results and performance evaluations are shown in Section 5. Section 6 presents our conclusions.  
Figure 1: An application of 3D UASN for ocean monitoring.