## 4 Experiments and Results
### Computational Cost, Scalability, and Deployment Considerations  
The proposed adaptive fuzzy and reinforcement learning methods incur moderate computational overhead due to online fuzzy inference and RL updates at each node, which remain practical for typical MANET devices. Method B's multi-profile approach introduces additional complexity by managing multiple decision profiles, but benefits scalability by localizing adaptations to relevant contexts. Simulation results demonstrate robust performance in scopes including up to 280 nodes. However, very large-scale networks may require hierarchical extensions to maintain efficiency. Deployment challenges include accurate local metric acquisition, environmental feedback, and potential instability during rapid topology changes. Nonetheless, with appropriate calibration and fine-tuning, these methods offer a balanced trade-off between performance gains and operational fea \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**40 Nodes**} & \multicolumn{4}{c}{**160 Nodes**} & \multicolumn{4}{c}{**280 Nodes**} \\ \hline
**Method** & **Red** & **Ovh** & **Cov** & **PDR** & **Red** & **Ovh** & **Cov** & **PDR** & **Red** & **Ovh** & **Cov** & **PDR** \\  & **(pkts)** & **(\% pkts)** & **(\%)** & **(\%)** & **(pkts)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline D-FL+RL & \(4.0\pm 0.4\) & \(4.8\pm 0.5\) & \(97.0\pm 0.6\) & \(90.0\pm 0.7\) & \(17.2\pm 1.2\) & \(16.0\pm 1.2\) & \(94.0\pm 0.6\) & \(93.0\pm 0.7\) & \(27.0\pm 1.5\) & \(24.6\pm 1.5\) & \(93.0\pm 0.6\) & \(90.0\pm 0.8\) \\ RL-PT & \(6.0\pm 0.5\) & \(5.7\pm 0.6\) & \(96.0\pm 0.6\) & \(94.0\pm 0.7\) & \(20.0\pm 1.3\) & \(18.7\pm 1.4\) & \(96.0\pm 0.6\) & \(91.0\pm 0.7\) & \(35.0\pm 1.7\) & \(26.1\pm 1.6\) & \(96.9\pm 0.6\) & \(89.0\pm 0.8\) \\ NFRL & \(6.0\pm 0.5\) & \(4.8\pm 0.5\) & \(98.0\pm 0.7\) & \(96.0\pm 0.6\) & \(20.0\pm 1.3\) & \(17.4\pm 1.3\) & \(95.0\pm 0.6\) & \(93.5\pm 0.7\) & \(35.0\pm 1.7\) & \(26.1\pm 1.6\) & \(93.0\pm 0.6\) & \(89.0\pm 0.8\) \\ Prop A & \(4.0\pm 0.4\) & \(38.0\pm 0.4\) & \(99.1\pm 0.3\) & \(98.2\pm 0.4\) & \(17.0\pm 1.2\) & \(14.5\pm 1.1\) & \(99.3\pm 0.2\) & \(98.7\pm 0.4\) & \(33.0\pm 1.6\) & \(24.8\pm 1.4\) & \(99.0\pm 0.3\) & \(96.5\pm 0.4\) \\ Prop B-P1 & \(3.0\pm 0.4\) & \(2.0\pm 0.3\) & \(98.0\pm 0.5\) & \(97.0\pm 0.5\) & \(10.0\pm 1.0\) & \(11.7\pm 1.0\) & \(97.0\pm 0.5\) & \(95.5\pm 0.5\) & \(15.0\pm 1.1\) & \(15.1\pm 1.2\) & \(99.0\pm 0.3\) & \(96.2\pm 0.5\) \\ Prop B-P2 & \(1.0\pm 0.3\) & \(2.0\pm 0.3\) & \(96.0\pm 0.6\) & \(96.5\pm 0.5\) & \(8.0\pm 0.9\) & \(12.7\pm 1.1\) & \(98.2\pm 0.4\) & \(97.0\pm 0.5\) & \(16.0\pm 1.2\) & \(13.6\pm 1.0\) & \(99.2\pm 0.3\) & \(98.0\pm 0.5\) \\ Prop B-P3 & \(3.0\pm 0.4\) & \(2.0\pm 0.3\) & \(97.0\pm 0.6\) & \(97.2\pm 0.5\) & \(11.0\pm 1.1\) & \(10.7\pm 1.0\) & \(98.5\pm 0.4\) & \(98.0\pm 0.5\) & \(18.0\pm 1.3\) & \(15.6\pm 1.1\) & \(99.5\pm 0.3\) & \(99.0\pm 0.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean\(\pm\) 95% CI for Redundancy, Overhead, Coverage, and PDR at 40, 160, and 280 nodes (10 runs each). sibility in real-world MANET environments. A complete 500 s virtual-time run requires approximately 8 min of wall-clock time, so our 30-seed averages incur 4 h per parameter configuration. Learning is performed online: Q-learning updates persist throughout evaluation, and no offline pretraining or frozen model is used. Empirically, the tabular Q-values converge after 1500 decision steps (25 min virtual time), corresponding to 50 full runs (6-7 h wall-clock) for stable policy acquisition. Per-node computation remains constant per broadcast interval (O(1) fuzzy-inference and Q-update), and profile selection adds only lightweight context checks (<5 ms per node per second). We validated scalability up to 280 nodes without observable slowdowns beyond the linear event-processing costs of OMNeT++.