## 5 Discussion  
Based on the developed method, appropriate cross-platform software was created. It finds the optimal parameters that achieve maximum network coverage at minimum cost and has the ability to assess the survivability of the network. A genetic algorithm was used to solve the problem of finding the optimal parameters that provide maximum coverage at a minimum cost. The parameters of the genetic algorithm are: population size is equal to 200; tournament selection with size 2; reproduction with crossover fraction of 0.8; crossover function is intermediate with ratio of 1; forward migration with fraction of 0.2 and interval of 20; count of generations is equal to 200; stall generation is equal to 100; and the function tolerance is equal to \(10^{-4}\). The results are presented in Figures 7-11. The Pareto optimal combination of the coverage radius and intersection level, which can be used to find the maximum coverage with minimum network's cost are presented in Figure 7. The number of sensors with the appropriate coverage radius required to cover the area is shown in Figure 8. One of the objective functions of the presented problem is the network cost function. The relationship between the uncovered area, the number of sensors, and the network cost's minimization function is shown in Figures 9 and 10. In Figure 11, the cost of the network is shown depending on the coverage radius of the sensors costing 7 conventional units each. After analyzing the results, we can conclude that increasing the radius of coverage decreases the number of sensors, which reduces the cost of the network. However, it should also be noted that increasing the radius of coverage leads to an increase in uncovered areas. To reduce the uncovered area, the cross-section of the sensor coverage areas should be taken into account. Therefore, the presented results allow you to choose the necessary network parameters depending on the needs and preferences of the user. According to Figure 7, when the coverage radius is increased, less sensor nodes will be required, but increasing the radius of coverage requires an increase in signal strength and received signal strength indicators, which in turn leads to an increase in the energy consumption of the sensor. This fact must be taken into account when choosing the optimal combination of network parameters in the design. According to the designed method, nearly zero uncovered area can be achieved by changing sensors' coverage radiuses, intersection level, position of sensors (mobility of sensor nodes). Analyzing the results of the assessment of the survivability of the incoming network (Figure 12), we can conclude that the network is vulnerable, as its final survivability is 6%. This is due to the vulnerability of the network to the loss of sensors and communication channels between sensors. The components of the assessment are as follows: network survivability relative to sensor loss--50%, network survivability relative to communication channel loss--66.7%, network survivability relative to the loss of sensors that provide maximum information flow--25%, network survivability relative to channel loss communications that provide the maximum flow of information--71.4%. Half of all network sensors are vulnerable to the loss of elements that provide maximum data flow. To increase the survivability of the network, the user needs to add backup sensors and communication channels, as well as ensure the same bandwidth of all network elements. According to the method presented in [16], the total survivability of the network presented in Figure 11 is equal to 33%. However, using the method presented in this article, the total survivability of the same network is equal to 6%. This means that the accuracy of estimating network survivability increased by 18% after taking the bandwidth of network elements into account. Further research will be aimed at ensuring the possibility of using sensors of different radii at the same time during the construction of the coverage area.