## 4 Conclusions  
This study introduces a novel routing protocol designed for 3D UASNs, utilizing fuzzy logic reasoning to enhance performance, as it is well suited to handling fuzzy input variables related to node state information, such as \"high residual energy\", \"high advance distance\", etc. We first conducted a theoretical analysis and found that the fewer hops packets experienced, the better the energy efficiency of the network. Therefore, we establish a new forwarding area to maximize multihop forwarding efficiency and reduce redundant transmission. Equally important is how to adaptively select suitable forwarding nodes based on node spatial distribution and state information, which will directly affect the transmission delay and parameters related to the energy consumption of the network.  
Through numerous simulations and validations, our proposed protocol demonstrates clear advantages over existing protocols in terms of average energy consumption, packet delivery rate, and network lifespan. On the one hand, it is due to the forwarding area we have established, which fundamentally reduces the possibility of inappropriate nodes acting as forwarding nodes. On the other hand, it is due to the outer fuzzy inference system we have designed, which reduces the possibility of a single node frequently acting as a forwarding node. This is very effective for extending the network lifetime. However, the performance of the FLRAF routing protocol in average end-to-end delay is not yet outstanding because it is necessary to transmit probe packets to complete the task. This process introduces unnecessary processing delay, which offsets the advantage of the FLRAF protocol in reducing transmission delay. Another important source of propagation delay is the priority evaluation task within the candidate set. After a node completes the final fuzzy reasoning, it reports the priority evaluation result to the next-hop forwarding node. This forms a many-to-one data transmission scenario. The original Broadcast MAC protocol is unable to effectively coordinate data transmission conflicts, leading to data collisions and the accumulation of propagation delay. The next step is to design a MAC protocol that can coordinate data transmission. For instance, an RTS frame containing queue length and data transmission time information is sent to the central node. The central node calculates the waiting time for node data transmission and encapsulates the coordination scheme into a CTS frame. Nodes then follow the coordination scheme to perform data transmission, thereby minimizing data collisions as much as possible. This is a problem that needs to be addressed in our subsequent research.  
Figure 23: Effects of maximum speed of nodes on network lifetime.  
**Author Contributions:** Conceptualization, L.S. and Z.L.; Methodology, L.S.; Software, L.S.; Validation, L.S. and Z.L.; Formal analysis, L.S.; Investigation, L.S., J.D. and J.W.; Resources, L.S. and Z.L.; Data curation, L.S., J.D. and J.W.; Writing--original draft preparation, L.S.; Writing--review and editing, L.S., Z.L., J.D. and J.W.; Visualization, L.S. and J.W.; Supervision, Z.L.; Project administration, Z.L.; Funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 61871148 and in part by the Major Scientific and Technological Innovation Project of Shandong Province of China under Grant 2020 CXGC010705, Grant 2021 ZLGX05, and Grant 2022 ZLGX04 (corresponding author: [PERSON]).  
**Data Availability Statement:** The data are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.