## 1 Introduction  
Ocean Big Data (OBD) is an emerging research area that benefits ocean environmental monitoring, offshore exploration, disaster prevention, and military surveillance. Since the oceans, rivers and lakes cover 71% of our planet, and the traditional \"deploy, sense, retrieve, and post-process\" routine for these sensing activities is difficult and costly for humans in large area applications. Deploying an underwater sensor network is very efficient for monitoring physical factors (e.g., temperature, salty, light) by a multitude of sensing modalities. Since it is impractical to connect every possible underwater sensor by wire, wireless communication is currently the dominant data delivery technique for numerous underwater applications. Current underwater communication fails in three categories, acoustic, electromagnetic and optical [1]. Benefits and drawbacks of these methods are compared in Table 1.  
Due to serious attenuation, except for acoustic waves, the effective communication range in sea water is no more than 100 m, which leads to acoustic communication being the dominant data delivery technique for numerous underwater applications. However, acoustic communication is the most difficult obstacle to the realization of these networks, which is caused by limited bandwidth (currently, few hundreds of bits per second), long propagation delay and unchangeable power-battery [2; 3; 4; 5]. Fortunately, for underwater environments, magnetic-induction (MI) as an alternative wireless transmission mode besides acoustic communications obtains high bandwidth connectivity (up to 10 Mbps) with energy per-bit orders of magnitude lower than that of acoustic communications [6]. However, for MI, it allows nodes to robustly communicate only when they are a few meters from each other (usually less than 10 m) [7]. Since mobile autonomous underwater vehicles (AUVs) can hover close enough to a sensor node [8; 9], using an AUV and multi-modal communications (e.g., acoustic and MI) can enhance the performance of UWSNs and can enable many critical applications. In this scenario, as shown in Figure 1, which leverages the advantages of different wireless underwater transmission modes and the mobility of AUVs, AUVs could be made aware of the sensing task and visit part of the sensors (sink nodes) for data gathering, while the rest of the sensors could acoustically forward their sensing data to sink nodes. Although AUVs can act as data mules and travel from node to node across a sparsely deployed sensor network to collect data [10], the slow mobility and limited energy that constrain them means that they can only visit a very small number of the sensors. Thus, much sensing data should be acoustically forwarded sensor by sensor. In such a procedure, packets may experience multiple relay nodes before reaching a sink node, which means that an enormous amount of energy is consumed on data forwarding along the path [11]. In addition, with the increasing number of acoustic communications, the probability of package collision and transmission energy consumption could be notably magnified, which will reduce the network performance. Moreover, reducing the total energy consumption and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communication Method** & **Data Rates** & **Communication Ranges** & **Channel Dependency** \\ \hline acoustic & Kpps & km & Multi-path, Doppler, temperature, \\  & & pressure, salinity, environment sound noise \\ electromagnetic & Mbps & 10–100 m & Conductivity \\ optical & Mbps & 10–100 m & Light scattering, Line-of-sight \\  & & & communication, Ambient light noise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of underwater transmission modes. Figure 1: AUV(autonomous underwater vehicles)-aided data gathering. acoustic communications is not enough for prolonging the network lifetime, as some popular sensors, e.g., some neighbours around the sink, may run out of the energy faster than the others, which will cause energy consumption unbalance.