## 4 Performance Evaluation  
### Numerical Simulations and Results  
The efficiency and applicability of the proposed algorithm were tested by means of a number of simulation scenarios to test the performance in various channel setups. Both the BS and nodes run the same algorithm, but in order to concentrate to the convergence of the automaton at the node, the BS runs a simplified version of this, and always has the best possible values without changes. The BS, in the case of no received response from node A, resends the message with a higher number of molecules. Akin to that, it ensures the reception from the node, as the node senses the increased concentration and amends the appropriate parameters accordingly. Upon the first successful reception of a message from node A, in the next message, the BS sends the time of its transmission and starts a Berkeley-like sync sequence of messages [23]. Node receives the message and responds, including its internal time. The BS then calculates the time drift and sends back only the time margin that every node should adjust internally. Utilizing this protocol, in the forthcoming transmissions, node and BS will have the same time-out period with obvious gains in energy and performance, as lost messages will be handled by time-out interrupts. All the different values for the simulation parameters are shown in Table 1.  
The performance of the network is presented in Figure 6. In this figure we can discern the minimum, the maximum and the average values of the time that a transmitted pulse \"is on air\" (Bs to node) for different numbers of signaling molecules. In Figure 6a, the distance between transmitter and receiver is 150 \(\upmu\)m. In Figure 6b, the same distance is 300 \(\upmu\)m. As demonstrated, lower intensity pulses have a higher variance but result in faster responses. Increasing the number of molecules per pulse causes the variance to decrease while the mean value increases. Consequently, the perception of the pulses becomes more consistent. The discrepancy for the pulse with the 2200 information molecules is owed to the saturated receptors in the surface periphery of the receiver (160 is the total number of receptors). Therefore, the node is not able to sense the decrease in a timely manner. In order to shed light to the advantage of the LA approach over an established work in the literature, we amended the simulation framework in a way that the BS sends messages to the node having a statistical distribution for the number of diffused signaling molecules per message. More specifically, we feed the system through the BS, with s pulses of signaling molecules, the number of which follows the normal distribution, for various configurations. In simulations of this type, the BS does not wait for the response from the node in order to fire the next message; rather, it diffuses the next message based on predefined time intervals ranging from 100 s to 150 s. Below 100s, the system is unstable, as the stochastic nature of the diffusion does not guarantee the delivery inside this margin. We opted for the algorithm in [14], as this model has a variable threshold and can be directly compared to the logic of the LA. The specified protocol is named the adaptive threshold detector (ATD) and keeps the number of received molecules from the previous bit period and compares them to the molecules received in the current bit period. If the number of molecules received in the current bit period is larger that the one in the previous bit period, the ATD algorithm will output one as the received message; otherwise, it will output zero. Figure 7 shows the bit error rate (BER--the number of message errors divided by the total number of transmitted messages). We recall that each transmitted biological message represents one bit of information. In the first set of simulations, the BS diffuses the message in the medium under a normal distribution \(N(\mu,\sigma^{2})\) with mean value 1000 and standard deviation equal to 50.