## 6 Conclusions  
In this study, a method for the maximum coverage of the territory by sensors with minimization of cost and assessment of survivability was developed. The developed method consists of two steps: finding the value of the optimal combination of coverage radius and the intersection of coverage radiuses and a survivability assessment. The intersection of coverage areas is taken into account as the main condition for data transmission in the network and to maximize coverage. To find the optimal network parameters, a nonlinear multicriteria optimization problem was solved. A genetic algorithm was used to find the solution to the given problem. For survivability assessment, an improvement in the known sensor networks survivability assessment method was proposed. The improvement is that in addition to the loss of the most important network elements, the loss of network elements with the highest bandwidth is also taken into account. The improved method increases the accuracy of finding the viability estimate by up to 18%. In case of insufficient information on the bandwidth of network elements, methods for finding the bandwidth of sensors and communication channels were proposed. The software that implements the developed method was presented. The results of the computer experiments confirmed that reducing the radius of coverage of the sensors leads to an increase in the number of sensors and increases the cost of the network, and with the help of the developed software it is possible to choose the optimal combination of the radius of coverage and the level of the intersection of the coverage areas in the territory. The developed software assesses the survivability of the sensor network and identifies vulnerable sensors and data transport channels. The novelty of the proposed approach is the simultaneous maximization of coverage of the territory while minimizing the network's cost, and it has the ability to assess the survivability of the network and identify vulnerable sensors and data transport channels.  
The paper presents Big Data good practice in relation to the stage of data ingestion with the help of an optimal sensor network. This was fulfilled within the framework of the iBigWorld project [22], which is devoted to gaining skills for developing innovative solutions based on Big Data in real-world applications. Working to build an optimal sensor network to cover the area while minimizing the cost of the network and maximizing the covered territory is be a good opportunity to gain competencies while using cutting-edge technologies based on Big Data and data science.  
Conceptualization, O.B. and V.M.; methodology, V.P.; software, V.S.; validation, V.P. and V.S.; formal analysis, V.P.; investigation, V.S.; resources, V.P.; data curation, V.S.; writing--original draft preparation, V.P., O.B., V.S., V.M. and M.B.; writing--review and editing, V.P., O.B., V.S., V.M. and M.B.; visualization, V.S.; supervision, M.B.; project administration, V.P.; funding acquisition, V.M. All authors have read and agreed to the published version of the manuscript.  
The work was co-funded by the European Union's Erasmus + Programme for Education under KA2 grant (Project no. 2020-1-PL01-KA203-082197 \"Innovations for Big Data in a Real World\"). The APC was funded by the University of Bielsko-Biala.  
Not applicable.  
The authors thank reviewers for comments that allowed to improve the paper significantly.