# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 5 Conclusions  
In UANETs, nodes often exhibit high mobility characteristics. Assuming node deployment remains relatively fixed, the emergence of \"hotspot nodes\" in the network due to mission requirements can lead to network congestion and a decline in network performance, among other issues. To mitigate these potential problems, we can address the situation by considering both node mobility and actual load conditions. By taking into account both mobility and load aspects, considering the influence of multiple factors, and making decisions based on comprehensive impact factors, we can enhance link stability, balance network load, and improve network performance. Considering the importance of MPR nodes in OLSR, this paper takes a holistic approach from the perspective of mobility and load. It designs a comprehensive MPR evaluation algorithm based on link state awareness, selecting five evaluation metrics: Link Duration (LD), Stability Degree of Link (SDL), Average Neighbor Set Change Rate (ANSCR), Load of Link (LL), and Load of Neighbor Set (LNS). The weight coefficients are determined using the entropy weight method, and a comprehensive evaluation is conducted using the method of TOPSIS. Additionally, an optimized routing protocol named CEL-OLSR is proposed, which is aimed to solve the problem of single metric for MPR node selection in OLSR routing protocol. Compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR, respectively, improved packet delivery rates by an average of 2.56%, 6.89%, 11.11%, and 22.04%. Within the node speed range of 10 m/s to 50 m/s, CEL-OLSR exhibited the maximum average end-to-end delay of around 11.07 ms, with minimal fluctuation. In comparison to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR reduced the overall average end-to-end delay by 4.66 ms, 9.03 ms, 12.98 ms, and 24.82 ms, respectively. Furthermore, CEL-OLSR increased network throughput by 8.04%, 22.71%, 45.55%, and 93.19% \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Speed Stage** & **Speed** & **CEL-OLSR** & **PM-OLSR** & **ML-OLSR** & **LD-OLSR** & **OLSR** \\ \hline Low speed stage: 10 m/s-20 m/s & 10 m/s & 36.73\% & 37.23\% & 37.83\% & 38.17\% & 40.31\% \\  & 15 m/s & 37.31\% & 38.48\% & 39.31\% & 40.05\% & 42.89\% \\  & 20 m/s & 38.12\% & 39.49\% & 40.89\% & 41.93\% & 43.96\% \\ \hline Mid low-speed stage: 25 m/s-35 m/s & 25 m/s & 38.28\% & 40.19\% & 41.42\% & 43.28\% & 44.32\% \\  & 30 m/s & 39.04\% & 40.97\% & 42.83\% & 44.16\% & 45.26\% \\  & 35 m/s & 40.28\% & 42.01\% & 43.51\% & 46.02\% & 47.71\% \\ \hline Mid high-speed stage: 40 m/s-50 m/s & 40 m/s & 41.14\% & 43.07\% & 44.86\% & 47.62\% & 49.06\% \\  & 45 m/s & 43.37\% & 44.76\% & 46.27\% & 49.04\% & 51.98\% \\  & 50 m/s & 45.96\% & 47.41\% & 49.15\% & 51.22\% & 54.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of control overhead rate for comparison protocols at different speed stages. compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, respectively. Overall, the average cost ratios of control messages for CEL-OLSR, PM-OLSR, ML-OLSR, LD-OLSR, and OLSR protocols are 40.02%, 41.51%, 42.90%, 44.61%, and 41.16%, respectively. As the node speed increases, the control overhead of CEL-OLSR also gradually increase. Overall, CEL-OLSR presents a considerable improvement in terms of delivery rates, latency, throughput, and routing efficiency over the compared protocols. Route communication is a multifaceted challenge, wherein network communication quality and overall performance are shaped by link statuses, influenced by various factors. Concurrently, drones have the potential to function as mobile base stations or relay nodes, facilitating the establishment and extension of ground-based 5G networks. Consequently, by considering task-specific needs and the distinctive characteristics of drone ad hoc networks, optimizing the integration of drone networks with 5G technology can enhance data transmission efficiency, elevate packet delivery rates, minimize transmission delays, and boost network throughput.