## 4 Experiments
### Experimental Results  
The criteria taken to validate the results are as follows:  
1. The experiment is subject to a constant increase in the number of nodes, and the experiment is run for different time intervals. 2. Testing one-hop latency involves measuring the time it takes for data to travel from one point to another in a single hop or transmission. To perform this test, a source device sends a packet of data to a destination device, both of which are connected to the network. The time it takes for the packet to reach the destination device is recorded and measured using a network latency tool. The test is repeated multiple times to obtain an average value and to identify any outliers or anomalies. 3. Testing end-to-end latency involves measuring the round trip time (RTT) of a packet. A source device is made to send a packet to a destination device, and the time taken for the packet to make the round trip is recorded and measured using a network latency tool. The test was repeated multiple times to obtain an average value. 4. Success rate testing is performed by measuring the packet delivery ratio (PDR) and the end-to-end delay of the data packets. The data obtained from testing the success rate of a MANET are used to optimize the network's performance and identify any potential issues that may affect the network's ability to deliver data packets. 5. The admission rate is tested by simulating the addition of new nodes to the network and measuring the network's throughput and delay. The results obtained from testing the admission rate are used to optimize the network's performance, such as adjusting the maximum number of nodes that the network can support and determining the network's ability to handle high traffic loads without experiencing congestion or delay. 6. The experiment is carried out to simulate the network's traffic load and measure the amount of control traffic generated by the routing protocols. The test is performed by varying the network's traffic load and measuring the control traffic generated at each traffic load level for obtaining CBO. Table 3 displays the comparative analysis of 1-hop latency, End-End Latency, Success rate, Admission rate, and Control Byte Overhead of the proposed method with that of the state-of-the-art methods in the literature. Column 2 in the table denotes either the Time interval or the number of nodes taken for the experiment, and it is interpreted based on the metric. In order to test the scalability and consistency of the proposed method, experiments were conducted by increasing the number of nodes of the order of 500, and the results are tabulated as in Table 4. It is seen from the results that the proposed algorithm has a difference of 0.10 in terms of 1-hop latency when the no of nodes is increased from 500 to 2000. The End-End latency is found to have a total difference of 0.12 when the no of nodes is changed from 500 to 2000. Similarly, consistency is maintained for the Success rate, admission rate, and CBO with a tune of 0.7, 0.9, and 0.5 when the nodes increase from 500 to 2000, which proves the scalability and stability in the performance of the proposed method. Figure 3 displays the comparative analysis of the 1-Hop latency of the proposed method to that of the state-of-the-art methods discussed in Section 2. It is seen that the proposed ELQSSM-ML has the least latency time of all the other methods. The proposed method reduces the latency by 26.22% when compared to EECRM, 18.2% when compared to EECRP-PSO, and 8% of that of the QSSM-ML method.