Air quality monitoring is a critical aspect of urban management. While poor air quality negatively impacts public health and well-being, implementing effective monitoring systems often involves significant costs. This paper addresses the optimization of air quality monitoring systems by balancing cost-effectiveness with citizen satisfaction. The core objective is to identify an optimal trade-off between user satisfaction and budgetary constraints. To achieve this, we optimize the number of clusters, where each cluster represents a group of users served by the nearest air quality sensor. Additionally, we present a penalty function that emphasizes prompt air pollution warnings, facilitating preventive actions to reduce exposure to polluted areas while ensuring a cost-effective solution. This approach enables the formulation of well-founded performance requirements for AI-driven algorithms tasked with analyzing air quality data. The findings contribute to the development of efficient, user-centric air quality monitoring systems, highlighting the delicate balance between infrastructure investment, AI algorithm efficiency, and user satisfaction.

Relevance. Necessary condition for the systems of safety and process control functioning in a mine is to provide power supply to the relevant facilities. The paper deals with one of the tasks of designing a power supply network in a mine within the framework of a hierarchical approach to organizing the network structure. Within this approach, power controllers are connected to mine lighting breaker. To supply base stations to power controllers, a multi-core cable is used. The number of such cores, as well as the number of such cables emanating from a power controller, are the parameters of the problem. Aim. To consider the problem of choosing routes for connecting base stations of wireless communication in a mine to power centers. It is assumed that base stations and mine lighting breakers are already located in a mine, having the ability to connect a given number of power controllers to them. The connection scheme must be optimal in terms of cost, which is determined by the cost of the cable used. Methods. The authors have proposed several algorithms for solving the mathematical problem, including a greedy algorithm, based on the “go to the nearest point” strategy, and a simulated annealing method. Results. To solve the problem, several approximate methods were proposed and tested. The number of cores is parameter of the problem. The best of the considered algorithms is the annealing simulation algorithm. However, if power centers need to be placed as well, brute force enumeration in the algorithm also gives good results with an appropriate combination of the number of power controllers and the number of possible locations for their placement. Practical relevance. The mathematical problem stated and the mathematical methods make it possible to find minimum cost routes for connecting wireless base stations by multi-core cables to power sources.