## 4 Experiments and Results
### Benchmark Schemes for Comparison  
In order to focus on advanced or \"complex\" solutions that are broadly comparable to our approach, we selected the following references:  
* D-FL+RL (dynamic fuzzy with RL): A baseline scheme where membership functions for fuzzy parameters (e.g., Neighbors, Connection Time, RSSI) adapt to average \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation Area & \(1000\,\mathrm{m}\times 1000\,\mathrm{m}\) \\ Number of Nodes & \(40,80,120,160,200,240\) \\ Mobility Model & Random Waypoint (0–30 s pause time) \\ Node Speed & Varying (0–20 m/sec vel) m/s \\ Transmit Power & 15–20 dBm \\ Traffic Model & Constant Bit Rate (CBR) \\ Packet Size & 512 bytes \\ Broadcast Interval & 1 s \\ Simulation Time & 500 s \\ Number of Runs (Seeds) & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. network conditions and a Q-learning agent fine-tunes the broadcast probability \(\beta\). This method is similar to the approach in [22], while in [24; 25] they considered broadcast probability, but only used a single set of fuzzy rules. It does not alter which parameters are considered across time, but it does dynamically adjust membership ranges. * RL parameter tailoring (RL-PT): An alternative advanced RL scheme that dynamically adjusts which parameters should have higher/lower weight in the broadcast decision. For instance, when mobility is high, RL-PT prioritizes link stability and connection duration; when traffic load is high, it increases the weighting of queue length and channel busy ratios. However, RL-PT does not employ fuzzy inference; it uses a piecewise function for decisions [26]. * Neuro-fuzzy RL (NFRL): A hybrid method based on a neural approximation of fuzzy membership functions [27]. Reinforcement learning trains the neural parameters to shape the membership boundaries, aiming to reduce collisions while maintaining Coverage. Although the approach is powerful, it can be computationally heavier. * Proposed Method A (fuzzy + RL w/ dynamic membership): Our first approach, described in previous sections of this paper, uses a single fuzzy profile (covering, e.g., \(\{NN,SS,CT\}\)), but adapts membership range boundaries at runtime. RL is applied primarily to set broadcast (BC) or do-not-broadcast (DNB), thus fine-tuning the network-wide broadcast ratio. * Proposed Method B (multi-profile fuzzy + RL): Our second approach extends the concept further by switching among multiple distinct fuzzy profiles (each with a different set of parameters) depending on macro-level conditions. One profile may emphasize energy and neighbor count, another might highlight node velocity and link duration, etc. An RL agent still refines the BC/DNB actions inside each profile context. We intentionally exclude naive flooding or basic probabilistic gossip from the comparative analysis, since those simpler methods have been studied extensively in the prior literature and are generally dominated by advanced fuzzy/RL approaches in terms of Overhead. Table 2 can be used as a reference for more general explanation of pros and cons of each model used for comparison.