## 1 Introduction  
Technological advances in wireless sensor networks and seamless integration with diverse technologies have led to the proliferation of underwater wireless sensor networks and their application areas. Submarine missions are increasingly utilizing underwater sensor gear [1]. Numerous vital applications, such as early warning systems, search and rescue missions, and ecosystem monitoring, can be carried out by these devices. With the collection and analysis of data in aquatic environments, sensors are crucial assets for intelligent systems that enable autonomous decision-making and data analysis. To fully realize the potential of the underwater realm, research focuses on addressing the particular limitations of the underwater medium to enable sustainable data collection in such applications. Underwater sensor nodes primarily employ acoustic communication technology to transmit data over long distances across water. However, acoustic waves have high bit-error rates; therefore, sensors need large transmission powers to avoid packet loss [2]. Considering sensor nodes are typically deployed in sub-sea zones and mostly run on batteries, energy management is crucial for building underwater sensor networks due to maintenance, and human intervention is not practical. As energy efficiency has been the main focus of underwater communication in the past decade, there have been considerable improvements in this research field to prolong network lifetime by suggesting MAC and routing protocols to improve the utilization of limited energy resources of acoustic sensor nodes to cope with the high energy demand of underwater wireless mediums. The primary reason for energy depletion in UASNs is environmental factors such as acoustic waves. The underwater environment is particularly prone to noise from various sources, such as shipping activities, wind, and aquatic life. Consequently, this leads to a low signal-to-noise ratio (SNR), resulting in a high bit error rate (BER). Since acoustic communication is a dominant technology underwater, the high energy demand of acoustic links is usually decreased by clustering methods. On the other hand, autonomous underwater vehicles (AUVs) are common application assets to gather information collected by clusters in underwater applications. The use of autonomous robots for data collection is another interesting viewpoint in the literature, and it has been thoroughly investigated in terrestrial wireless sensor networks. By minimizing the energy consumption of cluster heads and UAV robots, several research projects suggested mobile sinks for data gathering from clustered networks [3; 4; 5; 6]. In the study [7], authors explore data collection in a single-hop wireless sensor network, employing a myopic policy based on a Round Robin structure. Findings reveal the suboptimality of this approach for various energy harvesting processes, emphasizing limitations while achieving optimality in specific instances. Yet, in comparison to terrestrial wireless sensors, the underwater system has higher financial costs, even though the assistance of autonomous robots lowers the energy cost of sensor equipment. To maximize UASN lifetime, several studies in the literature focus on improving network clustering and cluster head selection methods [8; 9; 10]. Furthermore, recent studies present that harvesting ambient energy sources is a promising method to improve the life cycle of underwater sensors [11; 12; 13; 14]. Recent studies [15; 16] have demonstrated that UASN applications must optimize energy management in routing by using ambient energy to provide sustainable data collection from underwater sensor nodes. The main obstacle to optimizing energy management in submerged communication channels is the constraints of sensing apparatus and harvesting machinery.