# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 3 Related Work
### Comparative of Key Devices' Positioning Approaches  
In this section, we compare our study to the characteristics of selected references which explore the key device positioning problem to highlight the main points of our research. Table 1 aimed to present the comparison of the main characteristics of the method proposed by this research to different approaches used by selected references found in the literature that explore the gateway/router positioning problem. In addition, Table 1 highlights some of the innovative features of the method, such as its detailed topographic analysis, the consideration of DA positions, and its experimentation with real large-scale data. Regarding the techniques applied by different references to solve the key devices positioning problem (see Table 1), the use of heuristic algorithms [13; 29; 30; 31; 32; 33] and clustering strategies [13; 16; 18; 21; 22; 30; 31; 32; 34] is common. This problem is generally classified as an optimization problem because of the number of equipment, connection possibilities, constraints, and objectives. Regarding the problem formulation, some authors cite that this problem can be modeled as a linear/non-linear programming problem, as expressed by [17; 21; 29; 30; 32]. In addition, some authors classify the problem as a set covering problem [29; 31; 33], or as a facility location problem [17; 18; 34]. Metaheuristic approaches, including genetic algorithms, are also explored [21; 34], demonstrating that various strategies can be used to solve the problem. Despite experiments considering real scenarios, it is common to observe simulations using small datasets [13; 16; 18; 22; 33], or small synthetic datasets as in [17; 21; 35]. However, large real scenarios with more than 230,000 smart meters are used in our experiments to evaluate our proposed method, checking whether the average received power values calculated for the links are within the established threshold. Most strategies presented in Table 1 employ simplified propagation models to evaluate the potential wireless connection between the devices during the planning process. Usually, they do not consider terrain-specific information to estimate the signal loss from an SM to another coordinate (another SM or a candidate position) but assume a general scenario for the region, classifying it as hilly or flat, with moderate or heavy vegetation, or indicating that the region is an urban, suburban, or rural area. Some of them model the planning process as a simple distance-based clustering problem. In practice, some deployment scenarios can be challenging by presenting dense and sparse regions with irregular terrain profiles in suburban, urban, or dense urban scenarios. In these cases, despite demanding greater computational complexity to be solved, the optimal placement of devices can effectively reduce deployment costs. Regarding the use of general propagation models to evaluate the path/diffraction loss in an SG key device positioning problem, the authors in [17] present a work that explores the placement of data concentrators for the expansion of smart grid communication networks and use the Stanford University Interim (SUI) propagation model [7] to classify the terrain. They select the most appropriate terrain type to evaluate path loss and establish the communication range. According to [17], the SUI model presents the following types of terrains: Category A (maximum path loss)-- mountainous terrain with moderate-to-heavy vegetation; Category B--flat terrain with moderate-to-heavy tree densities, or mountainous terrains with light tree densities; and Category C (minimum path loss)--flat terrain and light tree densities. The authors in [29] evaluated the link quality between SMs and poles using the link successful delivery rate (SDR). They applied the general extended Hata-SRD path loss model presented in [36] which only considered three scenario classifications: rural, suburban, and urban. In contrast, our study estimated the connectivity of an SM and a candidate position using a more detailed approach.