# Highly Reliable Fuzzy-Logic-Assisted AODV Routing Algorithm for Mobile Ad Hoc Networks
## 4 Simulation and Results
In order to better compare the overall performance of these three algorithms on reliability and delay, we construct the ratio of reliability to delay, where it is proportional to the average reliability, and inversely proportional to the average end-to-end delay. As can be seen from the comparison of reliability to delay ratio in Figure 12, as the number of nodes is gradually increased, the comprehensive performance of FL-AODV performs better, while the FLRA performance is worse. This confirms that FL-AODV is better to achieve high reliability and low latency performance, although the reliability of FLRA is higher than the FL-AODV algorithm as shown in Figure 9. Especially, the FL-AODV algorithm is about 10% higher than the AODV algorithm and 25% higher than the FLRA algorithm when the number of nodes is greater than 100 in terms of ratio of reliability to delay. In short, compared traditional AODV and FLRA, the proposed algorithm FL-AODV has a better overall performance. Moreover, in order to verify that the proposed algorithm has better link connectivity, longer route life, and scalability in terms of speed at the same time, in this paper, we compare the average link connectivity and route life of these three algorithms at different moving speeds of nodes. It can be seen from Figure 13 that the average link connectivity determined mainly by link interruption probability and routing reliability of these three algorithms decrease  
Figure 11: Comparison of average path hops. Figure 12: Comparison of average reliability to delay ratio. with the increase in maximum moving speed owing to the greater randomness of selection of relay nodes and the routing of source and destination nodes in a network with rapidly changing nodes. Compared with AODV and FLRA, the FL-AODV algorithm performs better. It is because that the reliability of AODV algorithm is worse than the other two algorithms optimized by fuzzy logic, which results in lower link connectivity, and the link interruption probability of FLRA algorithm is more prominent due to greater randomness of selection of nodes when the speed is greater than 25 m/s, which accelerates the decline of the link connectivity. However, the reliability and link interruption probability of FL-AODV change slower, therefore, with the increase of node speed, the FL-AODV algorithm achieves the better average link connectivity. In Figure 14, the average route life determined mainly by residual energy and routing reliability of the three algorithms are compared. It can be seen that, compared to AODV and FLRA, the route life of the FL-AODV algorithm performs better with the increase in maximum moving speed. It occurs because, as the speed increases, the nodes change rapidly, the routing reliability, and residual energy of the FLRA algorithm that only selects the best reliable node is greatly affected, thereby reducing the route life rapidly. In addition, since the residual energy and reliability of the node is not considered, the routing life of AODV is shorter. However, the route reliability of FL-AODV algorithm based on AODV is less affected owing to the feature of the shortest path. Therefore, with the increase of node speed, the FL-AODV algorithm achieves the longer route life. In summary, the overall performance of the FL-AODV algorithm is better since it has higher reliability including better link connectivity and longer route life under the condition of low delay and low hop count. Besides low complexity, fuzzy logic comprehensively considers multiple indicators for routing selection, and thus can better meet the increasing demand for higher communication service quality. Figure 13: Comparison of average link connectivity at different speeds.