It is well known that underwater sensor networks (UWSNs) [1] are playing an increasingly important role in the field of ocean data collection, ocean resource exploration, and assisted navigation with the accelerating speed of ocean development. In recent years, the concept of intelligent ocean underwater IoT (Internet of Things) has also been proposed [2], with a wider range of applications.

In UWSNs, underwater sensor nodes are distributed in different areas to detect environmental parameters and collect them in a data processing center (Sink). However, these nodes are usually battery-operated, and the batteries need to be replaced by expensive and difficult operations in harsh marine environmental conditions. UWSNs often face energy limitations and short lifetimes, making energy saving a key factor in improving their performance and reliability [3].

Numerous efforts have been made to solve this problem. Firstly, in underwater sensor networks, data transmission is usually a key factor in energy consumption in UWSNs. The collected sensor data is compressed and aggregated to reduce transmission data and energy consumption by using data compression and optimization techniques [4].

Secondly, reasonable node deployment and routing strategies can also help improve the energy efficiency of underwater sensor networks. Based on the uneven distance and energy consumption between sensor nodes, adopting the optimal deployment and routing scheme can minimize energy consumption and extend the lifetimes of the network [5].

However, in these methods, once the battery is used up, it still needs to be replaced. Therefore, it is necessary to charge the underwater sensor nodes through technologies such as energy transmission to avoid the trouble of frequent battery replacement and achieve long-term monitoring and data transmission [6]. To overcome high water pressure and short-circuit, DeMauro et al. [7] designed a rechargeable lithium-ion battery module for underwater use. Due to the limited distance of energy transmission, it is necessary to use autonomous underwater vehicles (AUVs) to assist in charging, and path planning for AUVs is necessary.

The AUV is a kind of unmanned underwater self-propelled submersible for moderate activities without control [8]. The AUV is considered an economical and safe tool for seabed investigation, search, identification, and rescue, and they have been widely used in the fields of underwater resource exploration, underwater environmental monitoring, and marine safety [9]. However, due to the limited power carried by the AUV, the charging area is also limited, making it difficult to ensure its practicality when the detection area is larger (especially in marine environments), which leads to the problem of losing data from its subsequent nodes.

Xie et al. [10] proposed a scheme to make magnetic charging cars to charge the sensors in wireless rechargeable sensor networks (WRSNs). Unlike ground-based wireless rechargeable sensor networks, UWSNs are usually applied in a 3D framework, and the transmitting power greatly enlarges with the distance underwater.

Inspired by this idea, considering the special underwater environment, rwesearchers proposed an energy-efficient multi-AUV path planning scheme by using the genetic algorithm. ResearchersWe built a rechargeable underwater sensor network model with multiple AUVs to traverse and charge the sensing nodes to extend the subsea monitoring range and the sensor detection cycle. The scheme can minimize the navigation distance, expand the exploration range of the UWSN, and prolong the lifetime of the UWSN.

2. Energy-Efficient Protocol

Because of the limitations of battery technology, the communication protocol design can help save energy. Lee et al. presented a comparative analysis of various energy-efficient MAC protocols based on the network topology for UWSNs [11]. Zenia et al. reviewed energy-efficient and reliable MAC and routing protocols for UWSNs [12]. Khan et al. designed a communication protocol to send packets to reduce redundancy and improve channel quality [13]. Su et al. developed a hybrid-coding-aware routing protocol for underwater acoustic sensor networks (UASNs) [14], which can reduce the transmission overhead while ensuring reliability. Clustering also plays a vital role in underwater sensor networks by enhancing energy efficiency, promoting effective data aggregation, facilitating resource management, and prolonging the network’s lifetime [15]. It divides the network into smaller groups, or clusters, with each cluster having a designated cluster head (CH), which can aggregate and relay information from individual nodes within their respective clusters, reducing redundant transmissions [16]. This helps to conserve energy and reduce bandwidth usage in underwater environments where communication resources are limited [17]. In [18], Sun et al. designed a communication protocol that collects and sends data by clustering, which greatly reduced the energy consumption of each sensor node. Jin et al. proposed a topology control mechanism for underwater sonar detection networks (USDNs), which can obtain superior coverage performance and prolong the network lifetime with guaranteed coverage and connectivity [19]. Liu et al. designed a distributed node deployment algorithm based on virtual forces to increase the network coverage of a UWSN [20]. To prolong the UWSN lifetime and improve data delivery, Wei et al. [21] construct a network topology control model with multiple underwater factors such as topology, energy consumption balance, and strong robustness.

3. AUV-Aid Technology in WUSNs

Autonomous underwater vehicles have numerous applications in underwater environments, including data collection, charging, and more. One of the primary uses of AUVs is data collection. Equipped with various sensors and instruments, AUVs can navigate through underwater environments to collect data on water conditions, marine life, and geological features. Zhu et al. [22] applied the measure of AUV-assisted communication, where the AUV itself acts as a mobile node that can collect information for energy saving. Yan et al. [23] proposed a scheme that utilizes AUVs to collect data and carry out path planning using K-means algorithms [24]. In a three-dimension situation, Zhang et al. [25] tackle the three-dimensional path planning of AUVs based on a whale optimization algorithm, which avoids falling into the local optimum value. Multi-hop [26] and autonomous underwater vehicle-aided (AUV-aided) data collection methods [27] are both used in underwater detection. AUVs also play a crucial role in underwater communication and networking. They can act as relays, collecting data from stationary or mobile sensors and transmitting it to a central station or other AUVs. This facilitates seamless communication and enables real-time monitoring and control of underwater operations. Kan et al. [28] proposed a three-phase wireless charging system that could be used in a field-deployable charging station capable of rapid, efficient, and convenient AUV recharging. Ramos et al. [29] used dynamic system theory for navigation, which applies to 0–100 m depth of oceans and increases the battery life of AUVs by increasing the speed. Furthermore, AUVs are being developed with capabilities for autonomous docking and battery charging. This enables them to operate for extended periods without human intervention. AUVs can dock with a charging station or a surface vessel equipped with charging capabilities, replenishing their self-power supply and charging other sensor nodes. This eliminates the need for frequent retrieval and manual recharging, enhancing their autonomy and operational efficiency. AUV path planning is also an important method to improve energy efficiency. To reduce power consumption and prolong the network lifetime, Cheng et al. give global planning of the AUV’s path planning, avoiding underwater obstacles and analyzing its energy consumption model from its kinematic and dynamical models [30]. A new type of hybrid algorithm is used for subsea exploration using AUVs proposed by Kumar et al. [31], which greatly reduces their exploration range. Golen et al. divided the exploration area into several areas [32], and each area has its own data-receiving point. AUVs can save energy while collecting data through reasonable path planning. In [33], the authors proposed an energy balancing and path plan strategy for rechargeable UWSNs, which can extend the network lifetime while balancing the energy.