## 4 Performance Evaluation
### Discussion  
Unlike existing algorithms in the literature, the proposed algorithm has an elastic perception of the pulse due to the number of highs and lows it detects. Driven by the absence of a well-defined threshold for a pulse, the algorithm keeps track of the concentration values, and by counting the decreases after a local maximum, it can therefore sense pulses with long tails and shallow slopes. In combination with the LA feedback, in case of an erroneous reading, it adapts its parameters, conforming again to the new context. We should note here that each network configuration affects the communication between two entities in various ways. High-density pulses of slow moving molecules may result in decreased performance in comparison to lower density pulses of high velocity molecules, as in cases where the BS and the node are close. This happens because the fast molecules will increase the concentration at the node side, providing ample time for the algorithm to sense the message. On the other hand, slow moving molecules would provide a more distinguishable pulse, but their low velocity will create a long tail and will demand more time from the receiver side to detect the incoming message. For that reason, the use of an adaptive algorithm would have advantages over a fixed threshold algorithm in cases of a time-variant channel.  
Furthermore, while it seems a trivial task to identify a local maximum, this can only be achieved by having all the information available, and it takes place in an asynchronous way (we detect the local maximum by knowing all the other nearby maxima). Calculating such a maximum in a real-time way is not possible, and that is why the proposed solution exhibits a delay in the export of the outcome. (Nodes sense the maximum concentration at time x, but there should be plenty of time to realize if this is the pulse global-maximum concentration or it is just a fluctuation of an even higher concentration pulse. This is the reason for the apparent high time of convergence of the LA solution.  
A high transfer data rate relies on the reduction of the message error rates, and in biological nanonetworks with low signal propagation speeds and high turnaround times, the notion of reliability is of significant importance. The proposed solution, as shown from the simulation scenarios, provides a way to improve reliability with a plethora of network and channel characteristics, and increase the usable transfer data rate.  
Lastly, it should be noted that the proposed LA solution has a significantly better performance that the one that was shown in Section 3, but this comes with the cost of more complex logic.  
Figure 6b reveals that this type of study should take into consideration a plethora of parameters, such as the number of receptors in the cells periphery, originating from the peculiarities of the biological environment. Future work should investigate the effect of chemotaxis and the way it affects the network performance. Injecting the operation of the proposed LA with the ability to respond with cumulative messages would highly assist  
Figure 9: Speed of convergence for the LA.  
with the minimization of interference noise and would provide valuable insights to future nanonetwork protocol designers.