# Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
## 1 Introduction  
With the emergence of the Internet of Things (IoT) integrating a large number of heterogeneous end systems, the marine industry has been reconstructing itself. Accordingly, the digitalization and modernization of marine applications via the rapid advancement of IoT technologies have been covered in recent literature [1; 2; 3; 4; 5]. For the realization of maritime IoT, acoustic communication has received considerable attention as a promising technology for the construction of underwater sensor networks, because it can cover a distance of several hundred meters unlike radio frequency, optical, or magnetic induction-based communication techniques. Therefore, underwater acoustic sensor networks (UASNs) are expected to support a variety of marine applications such as oceanographic data acquisition, environmental monitoring, climate information recording, disaster prediction, assisted navigation, military surveillance, and reconnaissance [6; 7]. However, there are many unsolved challenges associated with UASNs. Compared to terrestrial wireless networks, underwater acoustic communication environment has unique characteristics such as propagation delay, severely impaired channel condition, limited available bandwidth, energy constraint [8; 9; 10; 11; 12; 13]. Many researchers have been engaged in investigating UASNs for managing harsh underwater environments [14; 15; 16; 17; 18; 19; 20; 21; 22]. In [14; 15; 16], propagation models for underwater acoustic communication were investigated and characterized in terms of the attenuation, time-varying multipath, and Doppler effect. To demonstrate the time-varying characteristics of underwater environments, the authors of [17] performed link performance measurement experiments and demonstrated the relationship between thecurrent velocity of water and link performance. In [18], the authors constructed a wide-area network containing four fixed sensor nodes, two autonomous underwater vehicles, and one mobile node mounted on a supporting research vessel, to measure the various metrics such as the channel impulse response, signal-to-interference-plus-noise ratio, round trip time, and probability of packet loss. In [19], a novel acoustic interference channel model was developed based on the fact that acoustic signals have inconsistent transmission ranges in the vertical and horizontal directions. The authors of [20] presented several techniques such as multichannel equalization, phase tracking, symbol synchronization, Doppler tracking, and spatial multiplexing in multiple-input multiple-output systems, from the point of view of signal processing. In [21], a novel time synchronization technique was presented for UASNs where the propagation delay could affect the synchronization accuracy. The authors showed that the proposed technique achieved more precise time-synchronization performance with minimal energy consumption, when compared to conventional techniques. In [22], slotted floor acquisition multiple access was proposed by adding multiple timeslots to limit the effect of propagation delay in underwater communication. In underwater environment, as most of the underwater IoT devices are powered by batteries, energy-efficient operations should be considered in UASNs. Therefore, many studies have been investigated energy-efficient techniques for UASNs [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. In [23; 24; 25], several medium access control (MAC) protocols were introduced to save energy by avoiding collisions and reducing the number of unproductive transmissions in UASNs. The authors of [26] studied the minimization problem of transmit power while guaranteeing connectivity between each node and data sink for the energy-efficient operation of UASNs. In [27; 28], the density of data sinks was minimized while satisfying the desired quality of service when two different MAC protocols were considered for channel access.