# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 1 Introduction  
Accompanied by the increasing demand for ocean exploration and protection, underwater sensor networks (UWSNs) have received more attention as these play an important role in diverse marine applications, such as coastal monitoring and protection, marine resource exploration, disaster warning and military operations [1, 2, 3, 4]. However, due to the harsh hydrographic and geographical environment, it is difficult to collect data from underwater sensor devices via a long-range routing path. Even if the monitored data can be transmitted through multi-hop routing technologies, there may be heavy workload near the sink with extra energy consumption [5]. Furthermore, as the battery power of underwater sensor nodes is severely limited and difficult to be redcharged underwater, it is not energy-efficient to upload large volume of ocean monitoring data to the sink directly. Moreover, with marine security operations, it is better to collect secret data nearby the monitoring sensors. To solve the aforementioned problems, autonomous underwater vehicles (AUVs) have been rapidly developed in recent years in terms of data storage and signal processing capabilities, which can better enable underwater mobile data collection. Moreover, the durability and mobility of AUVs alleviate the unbalanced energy consumption problem of underwater sensors [6, 7]. To collect data in an efficient manner, various underwater communication technologies have been investigated, such as acoustic and optics [8]. Currently, although underwater acoustic communication (UAC) has become the most widely used technology due to its unique advantages (e.g., long-range communication), it is limited by its shortcomings (e.g., low bandwidth, slow speed, high bit error rate and large delay) [9]. To address these issues, underwater optical communication (UOC) has emerged as an alternative solution, as it has a higher propagation speed (2.255 \(\times\)10\({}^{8}\) m/s) and higher data rate (up to hundreds of Mbit/s) over short to medium-range transmissions [10; 11]. As both acoustic and optical communication have their pros and cons, employing multi-modal underwater communication systems in UWSNs has become a potential approach to improve network performance [12; 13]. To facilitate mobile data collection in such multi-modal networks, it is necessary to satisfy the varying requirements of marine applications by combining the potential advantages of AUVs [14]. Combined multi-modal data collection via AUV is divided into two categories, such as acoustic multi-modal and acoustic-optical multi-modal. In acoustic multi-modal data collection, the sensor node transmits control information using low-frequency acoustic waves and guides the AUV to the designated area, and then, it switches to a high-frequency UAC modem to transmit the data [15]. In this case, the high energy consumption of the UAC shortens the lifetime of the sensor node when transmitting large volume of network data. Whereas, in the acoustic-optical multi-modal data collection, the UAC provides the capability for the AUV to approach the sensor node through long-range guidance and assists with alignment for the optical communication. The subsequent proximity of the UOC data transmission not only improves data transmission efficiency but also saves energy for transmission [16]. However, limited by the UOC range, it is necessary for the AUV to move slowly close to the sensor node to build an optical link reliably, which increases traveling time. A promising solution to the above problem is to engage both the UAC and UOC in data collection, such as transmitting a small volume of data over long distances using the UAC and retrieving and offloading large amounts of data using the UOC [17; 18]. Although the aforementioned pioneering studies have laid a solid foundation for multi-modal data collection, there are still some issues when applied to mobile data collection.