# Brain-Inspired Data Transmission in Dense Wireless Network
## 6 Discussion  
In this paper, we analyze the reliability of biology-inspired dense wireless networks. Our considered network is characterized by extreme simplicity of data transmission, i.e., no advanced channel coding is applied. It means that network reliability has to be achieved in a different way. First, we can gain from the fact that the density of deployed nodes is high  
Figure 8: Distribution of energy consumption during transmission. Figure 7: Distribution of delivery confirmation delay. and in most cases the line-of-sight transmission is possible. It has been proved that such a simplified transmission scenario can achieve reasonable performance expressed in terms of observed message delivery delay or overall energy consumption for a given reliability level. Second, besides the deployment of only simple neuron-nodes (whose basic functionality is to relay messages as only the received signal-to-interference-plus-noise ratio is above some threshold), one may also deploy some nodes with improved functionality. If we look at the results of the percolation experiment, we can see that it is a very good method for assessing the reliability of a wireless network. For example, when we analyze the impact of the number of nodes on the reliability of the network, we see that increasing the number of nodes (in this case from 30 to 100) significantly lowers the percolation threshold--that is, it is possible to use less reliable devices in this case. However, the analysis of the power of the nodes shows that increasing the power, in fact, increases the range of the nodes, which improves the reliability (as a single node has the chance to connect through more distant nodes). On the other hand, however, we do not observe a significant decrease in the percolation threshold (increase in reliability) because higher power also means a greater level of observed interference--which prevents (or at least limits) reliable transmission. Thanks to the use of percolation, we can analyze the behavior of the wireless network--what is important--regardless of its topology (we assume that it is a random value). In this area of the conducted experiments, it would definitely be worth considering a network where we have the ability to control power levels, which would allow to some extent to manage interference throughout the network. Of course, this approach would improve the performance of the network, but at some cost--increasing the complexity of the entire network. Another aspect worth investigating is the analysis of the radio environment in which there is not always a direct line of sightline. The analysis of the part concerning the simulation of a specific wireless network shows that, in terms of both--message delay and energy consumption--a network using only basic nodes inspired by neurons performs satisfactorily. Median message delay does not significantly deviate from the minimum values (given the presence of interference in the network); however, some large or extreme delays also occur (e.g., 95 th percent delays). In some specific applications or network configurations, however, it is possible to improve the underlying performance of the neurons themselves by applying some kind of support or intelligence in the network (using devices other than neurons as part of the devices). The same situation occurs in the human nervous system where transmission between neurons takes place and is possible; however, it is improved by the use of glial cells. In the case of the simulations carried out, it can be noticed that, for example, the use of additional devices inspired by microglia (scenario two) reduces the lowest network delays so far, however at the same time extending the longest delays in the network--i.e., it is possible to obtain lower delays but at the price of lower delay stability. In general, these simulations have been able to show that replacing some devices with more extensive ones (but not all devices) allows improving or adjusting the behavior of the entire network.