# Brain-Inspired Data Transmission in Dense Wireless Network
## 5 Bio-Inspired Wireless Network Reliability--Analysis by Means of Computer Experiment  
As the previous section discussed some theoretic limits for the reliability of the dense wireless network, in this part we analyze it by means of computer simulations and experiments. We have extended the prior works, by incorporating the self-interference in the research, and thus, this section describes the results of the simulation in which the proposed concepts were tested in the network where the messages are sent. This network consists of many nodes and enables the communication between users who are at the edge of the network using the LOADng routing protocol. In particular, a single random network topology that includes 400 basic network nodes (neurons) and 80 additional network nodes (microglia). These devices are distributed over an area of 30 km by 24 km on a grid with a side of \(\Delta=1\) km. The adopted carrier frequency for this system is \(f_{c}=2.45\) GHz, the band is \(B=5\) MHz, the required signal-to-noise ratio and interference is \(\theta^{*}=3\) dB, and the transmission power is 2 dBm. We assume that the connection between any two points may be corrupted with some probability, mainly the probability of a node breakdown is arbitrarily fixed to 0.05, and the mean time to failure is set to 5000 iterations. The simulation time is 10,000 iterations. The average time between messages is 50 iterations. As above, for the sake of clarity, we collect all important simulation parameters used in this part of our work in one table (Table 2). To deliver data from source to the destination, the well-known LOADng algorithm was proposed. Mainly, at the beginning, the network nodes do not have any information about neighbor nodes. When the necessity of some node discovery occurs, a broadcast message is sent to find specific node. Nodes which are not a destination of such message simply transmit this broadcast message further and the node which is a destination of such message replies with different types of message. Such reply selects a route based on some metric (commonly the distance in the form of hop count) and each node on this route updates its own routing table. In the case of some problems in the existing routing map, e.g., one node does not response, another type of message is sent--which informs all nodes on the route about the problem. The reaction to this message can be turning on microglia-based nodes and dropping routing entities due to such node activation. Then, a new route to the destination needs to be discovered in the same way as described. However, to deal with interference problem in the network, the applied LOADng algorithm was modified. Mainly, the Binary Exponential Backoff algorithm was implemented. In the case of collision (i.e., when at least two transmitters start their transmission at the same time causing harmful interference and degrading effective SINR), the current value of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(N\) & number of the neuron nodes & 400 \\ \hline \(M\) & number of the microglia nodes & 80 \\ \hline \(A\) & number of the astrocyte nodes & 1 \\ \hline \(x\times y\) & sizes of the considered area & \(30\times 24\) km \\ \hline \(\Delta\) & grid size & 1 km \\ \hline \(f_{c}\) & center frequency & 2.45 GHz \\ \hline \(B\)  
the backoff-window is randomly selected from the allowed range of integer values, and another try for data transmission is repeated after the back-off time is over. Such binary exponential back-off time increases the network reliability, as it introduces some sort of collision mitigation, but it is achieved at the price of longer message processing time. Thus, it increases the average data delivery time within the network, as well as degrades the overall energy consumption. For performance evaluation, we consider four scenarios:  
* Scenario I: Only neuron-nodes are deployed; this scenario is also used as reference. * Scenario II: Both neuron- and microglia-nodes are available.