## 4 Performance Evaluation
### Numerical Simulations and Results
For the first set of simulations, one can extract two pieces of information from the Figure. Firstly, the ATD algorithm seems to have a balanced error \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline simulation area dimensions & non confined \\ \hline Dataset Size & 2000 values (related to the probing rate) \\ \hline step for ascensions & \(+100/-100\) \\ \hline step for descensions & \(+30/-30\) \\ \hline number decreases of the descensions & \(-10\) every algorithm iteration \\ \hline Bacterial Diffusion Coefficient & 12,000–114,000 \(\mu\)m\({}^{2}\)/min \\ \hline Informatio Pulse ( number of bacteria) & 500–2200 bacteria \\ \hline Bacterial speed & 27 \(\mu\)m/s \\ \hline Tumble Duration & Exponential with mean 0.15 s \\ \hline Number of BS messages transmitted per scenario & 100 \\ \hline Message input distribution & Normal distribution-various values \\ \hline Move forward duration & Exponential with mean 1/4 s \\ \hline volume exclusion effect shape & Spherical with 2 \(\mu\)m radius \\ \hline Node sensing radius & 70 \(\mu\)m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters and values. Figure 6: Reception performance for different distances between BS and node (150 \(\mu\)m (**a**), 300 \(\mu\)m (**b**)). (**a**) Time-on-air deviation for the system. Increasing the number of molecules results in more consistent receptions. (**b**) Time-on-air deviation for the system with increased distance (300 \(\mu\)m). Higher concentration pulses demand more time to be perceived. output at the level of 45%, and the LA approach improves its performance as the message rate decreases. Both solutions have the same performance around the 100 s area, and the LA algorithm outperforms the ATD solution mainly due to ability to detect the optimum parameters in order to improve the error rate and consequently increase the usable message rate. The second set of simulations, shown in Figure 7, proves that the ATD approach keeps its performance level for even more fluctuated input signals (the input signal distribution has a higher deviation in relation to the previous simulation set). Meanwhile, the LA approach had an increased error rate related to the previous one, as the higher deviation resulted in increased corrective actions, resulting in degraded performance. Yet, around the point of 130 s, the proposed solution drastically decreased its error rate, as less dense messages provided ample time to find the best possible parameter configuration, achieving higher performance. After the point of 150 s, the LA minimized the error rate in the current system configuration, as the time interval was high enough to fade out the possible fluctuations and provide smoother curves. Aiming to point out the performance of the LA algorithm for a time-variant channel in comparison to a fixed threshold way of determining the message, another set of simulations was conducted, as shown in Figure 8. In this scenario, the BS sends a message, having a variable number of signaling molecules based on a normal distribution and waits for the response from the node, in order to release the next pulse of molecules. As a consequence, the two-way communication system depends on the reception capabilities of the receiver node. In case of a missed or double-received message, we consider this transmission as an erroneous one. Having the ability to adapt to the different channel characteristics, the LA approach has significantly lower BER compared to the solution with the predefined thresholds. As expected, the latter solution, for which the threshold for each scenario was set equal to the mean value of the distribution used for each case, follows the molecule distribution, as half pulses were below the threshold and half were above it, resulting in a stable BER.