## 3 Learning-Automaton-Based Communication Protocol for a High Data Rate  
The main objective of the proposed protocol is to provide an efficient way to enhance network performance, while retaining the ability to adapt to environmental changes. Contributing to this aim, a simple (so as to be easily adopted from the biological nodes) yet effective algorithm was developed, in order to detect an incoming pulse without the use of any predefined threshold. The algorithm supervises the number of times the moving average of the perceived concentration decreases after a significant (relevant to the total concentration) rise in the concentration. That way, the local maximum is determined for each pulse (rise phase), followed by the appropriate number of decreases in the concentration (drop phase). Specific initial values for these two parameters (numbers of ascents and descents) are not considered as a prerequisite due to the operation of the LA. Figure 2: Biological communication system. Three nodes send signaling molecules to the BS (at the center). As some of the previous works in the field based their detection algorithms on predefined thresholds, we wanted a solution that could be applied with an \"ad hoc\"-like manner, meaning that there should be no a priori knowledge of any of the operational parameters of the system. That being the case, the proposed algorithm has two basic parameters: Firstly, the number of required ascensions after a \"local maximum\". While this maximum may not be the absolute maximum, yet it may be the absolute one for the received pulse. In regard to this, in Figure 3 the number of received molecules having various velocities and the same BS-node distance were simulated to show the importance of the velocity in the perceived concentration. As shown from the red line, the highest local maximum was at around 32 s. Yet, the node was able to sense the message only after 55 s, as the algorithm tried to find a sufficient number of ascendings followed by equivalent descendings. This is the novelty of this work, along with the learning automaton procedure, as it does not rely on any knowledge from the network to discern an incoming pulse-message. Likewise, for the blue line, the decreased speed of the information molecules resulted in a long tailed reception pattern. The highest local maximum was perceived at around 75 s, and the algorithm was not able to discern a pulse until the point of 200 s due to the slow decay. Finally, in the last case shown in the figure, the green line shows the result of an even smaller velocity (18 \(\upmu\)m/s) with a local maximum at around 110 s. Lower velocities for the signaling molecules mean that the received pulse will have a lower local maximum and will need more time to clear the channel. Due to the operation of the proposed detection algorithm, the node is not able to discern the incoming message up to the point of 270 s that is shown in the Figure, as the number of declines related to the inclines is not sufficient for the message detection and requires more time to decrease the perceived density from the the remaining molecules. Due to the long tailed graph, in this simulation case, the operation of a learning automaton is essential in order to adapt its parameters. Learning automata (LA) [22] are artificial intelligence tools that can be applied to learn the characteristics of a system's environment. LA do not need any knowledge of the environment they operate in or any analytical knowledge of the task to be optimized in order to function properly. A LA is a finite state machine tool which improves its performance by providing feedback from environments with mercuni conditions, such as the BNs. As shown in Figure 4a, there is a set of possible actions \(B_{i}(n)\), along with the corresponding probabilities \(P_{i}(n)\). \(p_{1}(n)\)\(p_{2}(n)\)\(\ldots\)\(p_{m}(n)\) constitute a vector which represents the probabil  
Figure 3: Different velocities for the same BS–node configuration.