## 2 Literature Review  
The authors of a previous study [5] proposed a method for infrared and camera sensor fusion, which was applied to indoor positioning in intelligent spaces. The fused position was obtained with a maximum likelihood estimator from the independent observations of infrared and camera sensors. However, the authors did not take into account the intersection of the sensors' coverage areas. In [6], a method for optimal coverage of the territory by sensors with different coverage radii was proposed. To solve this problem, the authors modified the least squares method. The criterion for optimality is the maximum coverage of the territory with a minimum intersection of coverage areas. The possibility of changing the intersection value and square of the uncovered territory was not mentioned. In [7], an algorithm for an aerial sensor network composed of fixed-wing unmanned aerial vehicles that perform surveillance and detect the early signs of wildfire in a given territory was presented. The main goal was to cover a given area while prioritizing areas of higher fire hazard risk. The authors did not take into account the value of the uncovered area. The authors of [8] focused on optimizing the placement of sensors to solve the problem of critical network coverage with two goals: accuracy and cost. However, the authors did not take into account the cost of the networks and elements of the networks. In order to enhance the network coverage range, in [9], an instance (node) redeployment-based Bodacious-instance coverage mechanism (BiCM) was proposed. The proposed mechanism creates new instance positions in the coverage area; however, the authors did not take into account the uncovered area and the network's cost. According to the authors of [10], the use of low-cost sensor networks is becoming increasingly popular in the environmental sciences, and the unprecedented amounts of monitoring data that are generated enable research across a wide spectrum of disciplines and applications. In [11], a mechanism based on probabilistic blacklisting is proposed for Internet of Things networks, which results in lowering power consumption. In particular, channels suffering from non-negligible disturbance may be skipped based on the perceived quality of communication so as to increase the reliability and decrease the likelihood that retransmissions have to be performed. In [12], the authors considered the optimization of the neighbor discovery to reduce the power consumption in wireless sensor networks and proposed an energy-efficient neighbor discovery scheme by adapting symmetric block designs, combining block designs, and utilizing the concept of activating nodes based on the multiples of a specific number. However, the network's survivability was not mentioned. As shown in other research [13; 14], modern sensor networks have become more and more expensive. Due to the challenges of the modern technological world, survivability is an important parameter for information systems. Survivability is a property that characterizes the ability of a system to function effectively in the presence of damage or to restore this ability over a period of time. There is structural and functional survivability. A structure-based identification method to identify the influential nodes in industrial networks was proposed in [15]. The authors only identified vulnerable nodes, but connections between nodes can also be vulnerable. The authors of [16] present an approach to estimate the survivability of a sensor network based on Dijkstra and Lee algorithms; however, they did not take the bandwidth of network elements into account. The estimation of network viability using Markov chains is presented in [17]. In this work, the sensor network's survivability was not taken into account. Table 1 presents a comparison of the studies found in the literature review, the main focus/outcome of the study and the factors that were not taken into account.