## 2 Related Works  
Location-Based Routing Protocols rely on the geographic positions of sensor nodes to make routing decisions, aiming to improve routing efficiency and reduce communication overhead [5]. For instance, [PERSON] et al. [6] proposed Vector-Based Forwarding (VBF), which establishes a virtual routing pipe between source and destination nodes, allowing only nodes within the pipe to forward data. Although VBF reduces redundant transmissions, it suffers from packet delivery degradation in sparse networks due to communication voids and lacks path recovery mechanisms. To address these issues, [PERSON] et al. [7] extended VBF by introducing Hop-by-Hop VBF (HH-VBF), which dynamically constructs local virtual pipes at each hop to improve robustness. However, this enhancement increases the computational and communication overhead, leading to higher delays and energy consumption. Similarly, [PERSON] et al. [8] presented Focused Beam Routing (FBR), which uses directional beam control based on node and destination locations to adaptively forward data. Although effective in stable environments, the FBR's reliance on precise location information limits its adaptability in dynamic underwater networks. [PERSON] and [PERSON] [9] proposed Directional Flooding Routing (DFR), which uses location and link quality to constrain flooding angles, improving delivery success and reducing void effects, but it still faces high energy costs and scalability issues. [PERSON] et al. [10] introduced the Energy-Balanced and Lifetime-Extension (EBLE) protocol, employing a cost function to select energy-balanced paths and extending network lifetime, showing superior performance in both regular and random deployments. Despite these advancements, location-based protocols generally depend on accurate positional information and struggle with sparse or dynamic topologies. Location-Free Routing Protocols, on the other hand, do not require geographic position information. [PERSON] et al. [11] proposed Depth-Based Routing (DBR), a receiver-driven routing scheme that relies on depth comparisons between the node and the depth value carried in the data packet to make forwarding decisions, facilitating hop-by-hop progression toward the surface. Despite its simple structure and suitability for environments lacking location information, DBR can suffer from routing voids and concentrated energy depletion in sparse networks or areas near the sink nodes. To address these issues, subsequent protocols have been developed to optimize DBR. [PERSON] et al. [12] improved DBR by introducing Energy-Efficient DBR (EEDBR), which selects forwarding nodes considering both residual energy and depth, aiming to balance energy consumption. However, EEDBR lacks multipath forwarding, resulting in higher error rates and limited load balancing in noisy multipath fading environments. [PERSON] and [PERSON] [13] further proposed Energy-Efficient Probabilistic DBR (EEPDBR), which integrates neighbor density alongside depth and energy, using a probabilistic forwarding mechanism to enhance robustness. The EEPDBR also incorporates two-hop neighborhood awareness to improve routing decisions. Despite these advances, the EEPDBR remains mostly localized and does not address global coordination or adaptive power control. [PERSON] et al. [14] introduced Adaptive Power Control Routing (APCR) to improve connectivity and energy use through dynamic transmission power adjustment. Later, [PERSON] et al. [15] proposed the APCDBRP, which combines power control with link-quality awareness based on received signal strength, balancing energy efficiency and load distribution. However, these protocols often consider either link reliability or power control, lacking a unified framework that jointly optimizes both across the networks. They also primarily rely on single-hop local information without global relay selection, which limits their adaptability and efficiency in dynamic underwater environments. In addition, [PERSON] et al.