In [13], a geographic routing protocol and two topology control algorithms were introduced; they employed the greedy forwarding protocol as a foundation. When a data packet reaches a void node, the node has the capability to vertically adjust its position to establish connections with the non-void nodes and restore data forwarding. Nevertheless, the process of adjusting the sensor node's location consumes a substantial amount of energy. In [14], an adaptive location-based routing protocol (ALRP) was proposed. The ALRP introduces several key mechanisms to improve forwarding efficiency. First, it establishes a forwarding area to restrict the range of candidate forwarders. Second, it dynamically calculates the forwarding probability to minimize unnecessary redundancy in forwarding. Third, it adaptively determines the forwarding order based on the forwarding delay. To provide a decent performance, these algorithms need accurate 3 dimensional (3D) location information on the sensor nodes; this is difficult to obtain in the UASN [15]. For the depth-aware routing protocol, the routing decision is based solely on the depth information obtained from the sensor node's barometer. The depth-based routing (DBR) protocol, introduced in [16], represents the initial routing approach that utilizes the sensor node's depth for data forwarding in the UASN. Additionally, a holding time mechanism is implemented to facilitate the coordination of the forwarding candidates during transmission. However, in a sparse network, the greedy hop-by-hop forwarding may frequently encounter a communication void region, where the sensor node cannot find a next-hop node to deliver the data packet. The energy-efficient depth-based routing (EE-DBR) protocol, where both the residual energy and the depth of the sensor nodes are considered when selecting the next-hop node, was proposed in [17]. However, when the sensor nodes were deployed sparsely, the problem of the routing void was not resolved effectively. In [18], the distance vector-based opportunistic routing (DVOR) protocol was proposed. The DVOR protocol seeks the shortest routing path according to the hop count of the sensor nodes towards the destination. Additionally, a holding time mechanism was developed to regulate the scheduling of data packet forwarding. However, the DVOR protocol introduces an overhead in the UASN due to its reliance on periodic beacons for the dynamic establishment of routing paths. In [19], the adaptive power-controlled depth-based routing protocol (APCDBRP) was proposed to prolong the network lifetime. The protocol comprises two phases: route establishment and data transmission. Moreover, APCDBRP proposes a data protection and route reconstruction mechanism to address issues such as network topology changes. However, the power control and data protection mechanisms in APCDBRP introduce a certain level of end-to-end delay. In [20], a multi-layer cluster-based energy-efficient routing scheme was proposed. The proposed scheme encompasses three distinct stages. In the initial stage, the network is divided into multiple layers. Subsequently, in the second stage, the sensor nodes are organized into clusters within each layer. Finally, in the third phase, the data are efficiently forwarded towards the sink. To tackle the challenge of hotspots, a dynamic clustering approach is presented. This scheme can effectively balance network energy consumption and reduce end-to-end delay. A novel neighbor-based energy-efficient routing protocol was proposed in [21]. The protocol implementation comprises the deployment of random clusters and the relocation of nodes underwater. Notably, each node demonstrates the ability to detect, identify, and forward routing paths to the nearest neighboring node. These operations are facilitatedthrough the processes of route discovery and route maintenance. To enhance efficiency, the protocol employs a flooding mechanism, which effectively discovers the closest node by leveraging this approach.