## 4 Experiments and Results
### Key Insights and Expected Outcomes  
Our experiments highlight several points regarding adaptive fuzzy/RL broadcasting in MANETs:  
1. Advanced adaptation improves delivery: All methods that adapt membership functions, parameter weights, or both (i.e., D-FL+RL, RL-PT, NFRL, Proposed A/B) outperform the static or simplistic solutions found in the older literature. Coverage remains high (90-99%), even in challenging densities.
2. Fuzzy + RL tends to be more stable than pure RL weight tuning: RL-PT sometimes experiences short periods of suboptimal performance if it flips parameter priorities (e.g., from link duration to queue length) too drastically. Fuzzy membership logic provides a smoother state space.
3. Multi-profile approaches handle heterogeneity: Proposed Method B shows an edge in highly dynamic or energy-constrained conditions, switching to specialized fuzzy profiles (e.g., one focusing on \(\{E_{\text{res}},NN\}\) or \(\{velocity,link\_time\}\)) to minimize Overhead or collisions. This profile switching Overhead is minimal compared to the performance gains.
4. Neuro-fuzzy RL is powerful, but riskier: NFRL can converge to near-optimal solutions, but sometimes exhibits \"overfitting\" or slower adaptation to abrupt mobility changes. If properly tuned, NFRL could outperform simpler fuzzy RL, but it demands more computational resources and careful hyperparameter tuning.
5. Energy conservation is enhanced by parameter switching: In denser networks with partial battery depletion, Proposed Method B's ability to select an energy-aware profile  
Figure 3: (**Top row**): (**a**) Redundancy vs. NN, (**b**) Overhead vs. NN. (**Bottom row**): (**c**) Redundancy vs. Mobility Speed, (**d**) Overhead vs. Mobility Speed. Proposed Model B uses three parameter presets (P1–P3) to adapt based on conditions.  
can reduce total energy usage by up to 10-15% compared to single-profile dynamic membership approaches (D-FL+RL).  
Table 4, reports the mean values and associated 95% confidence intervals (CIs) for Redundancy, Overhead, Coverage Ratio, and PDR at sparse (40 nodes), medium (160 nodes), and dense (280 nodes) network densities. As node density increases, Redundancy and Overhead grow for all methods, but our proposed schemes (Prop A and Prop B P2/P3) maintain substantially lower Redundancy and Overhead compared to baseline approaches (D-FL+RL, RL-PT, NFRL). Coverage and PDRs remain above 95% in all scenarios, with Prop A and Prop B variants achieving near-perfect reliability, even at high densities. The 95% CIs are sufficiently narrow (e.g. \(\pm 0.4\) pkts for Redundancy at 40 nodes, \(\pm 1.3\) pkts at 280 nodes), demonstrating that these performance differences are statistically significant. Overall, we can see that our adaptive parameter presets in Prop B consistently yield lower Overhead without sacrificing delivery performance across a wide range of network scales.  
Overall, the results confirm that combining fuzzy logic with reinforcement learning is highly beneficial in MANET broadcasting and that multiple parameter set profiles with RL-based adaptation (Proposed Method B) further improve robustness and energy efficiency under extreme scenarios.  
We also measured how often Method B switches between its three parameter set profiles during a single simulation run. On average, at 40 nodes, the network triggered only 3-4 profile transitions over a 500 s virtual-time run, rising to 7-8 switches at 160 nodes and 10-12 at 280 nodes, as mobility and contention events become more frequent. These transitions incur negligible processing delay each profile check, switching takes under 5 ms per node, and we observed no destabilizing oscillations or convergence issues in end-to-end metrics. Indeed, Table 4 demonstrates that PDR and Coverage remain above 95% even at the highest switching rates, confirming network stability. Thus, while Method B's adaptive profiles add a small runtime cost, they do not introduce measurable delays or impair broadcast reliability, making them practical for real-world MANET deployments.