## 6 Discussion  
In general, all SNN methods made 10% random selections for exploring the globally optimal choices as part of the learning and selected unfavorable links randomly. The static method was configured to choose the earliest available link from the shortest path from h23 to h25 in all test scenarios. In snndp, we used \(k=100\), which gave us better results. We found it challenging to select a value for \(k\) to balance both metrics. In Scenario 1, the beneficial path was h26\(-\)h29\(-\)h27. All routing methods selected that path and performed equally. At higher bundle rates, the SNN algorithms preferred h30 to load balance, which led to better performance than the static method. In Scenario 2, a small percentage of packet loss at lower bundle rates on the less delay path made snnr select h29 and led to a slightly increased average response time than snnd. However, increased packet losses at higher bundle rates caused snnr to choose h30 more at the expense of increased delay. Since static used only h29, the increased packet loss decreased the throughput more than the SNN methods. Since snnp tried to optimize only packet loss, it selected h30 and performed better. In Scenario 3, configured rate limiting on the link to h30 slowed down the bundle delivery and raised the delay more than on the link to h27. Hence, by selecting h30 to avoid bundle losses while trying to reduce delay, snnr, snndp, and snnd performed worse than snnp, but better than static. snnp achieved better packet loss and throughput with increased average response time by selecting h30 more. Since the network conditions favored h29 as the profitable link, all routing methods selected the path to h29 in Scenario 4. All SNN methods sent some of the bundles to h30 at random for exploration and showed better performance than static at higher bundle rates by load sharing between the two links. In Scenario 5, the link to h30 delayed the bundle delivery and caused a higher delay. The snnr method selected h29 more times, similar to the other SNN methods, and showed similar performance at lower bundle rates. However, at higher bundle rates, snnr used h30 more than the other SNN methods and improved the throughput by increasing the average response time. All SNN methods showed better performance than static by using h30 randomly. In Scenario 6, though the delay on the path h26\(-\)h29\(-\)h27 was high, there was no packet loss, which caused the denominator in the snnr cost function to be high and caused it to pick h29 more. Hence, snnr resulted in less average response time, packet loss percentage, and high throughput. In Scenario 7, snnr showed slightly better performance than snndp and snnd. All SNN methods showed better packet loss and throughput performance than static. In Scenario 8, all SNN methods showed better average response times and packet loss percentages than static. The static method achieved the same throughput as the SNN methods, though it was configured to select the path with losses. Interrupted connections between h29 and h27 caused bundles to be stored and delivered later by the DTN protocol and caused the throughput to be the same. At lower bundle rates, snnr, snndp, and snnd preferred h29 over h26 and selected h27 or h30 according to the connection interruptions at h29. At higher bundle rates, snnr, snndp, and snnd sent more bundles to h30 from h26, then snndp and snnd selected h30\(-\)h29\(-\)h27, but snnr selected h30\(-\)h27. Thus, snnr achieved less average response time snndp and snnd. snnp achieved better throughput at lower bundle rates and decreased throughput at higher bundle rates by choosing the path h26\(-\)h30\(-\)h30\(-\)h29\(-\) most. In Scenario 9, snnr and snnp preferred the h26\(-\)h29\(-\)h30\(-\)h27 path at all bundle rates, whereas snnd and snndp preferred h26\(-\)h30\(-\)h27 at lower bundle rates and h26\(-\)h29\(-\)h30\(-\)h27 at higher bundle rates. Hence, snndp and snnd resulted in high packet losses and lower throughput, whereas snnr and snnp achieved better performances.