# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
###### Abstract
It initially requires knowledge about the positioning of smart meters (SMs), distribution automation (DA) devices, and poles. Knowing the technical information about the communication technology and the geographic area is also important. The planning of an AMI network entails particular concerns regarding the costs and overall performance of the communication network because the adequate positioning of key devices can significantly reduce the total deployment cost. Therefore, when designing an SG communication network, in addition to prioritizing smart meters' connectivity, we must also take into account the positions of distribution automation (DA) devices. The communication network for managing DA equipment must have high performance and reliability, and thus their connection to the backhaul network is a requirement. Additionally, a more detailed evaluation of inter-device connections in the planning stage helps to anticipate the occurrence of potentially unreliable links during the implementation phase. In our research, the interest is towards proposing a method for positioning communication devices, presenting a good trade-off between network coverage and the number of communication devices. The existing literature explores the theme by presenting different techniques for ideal device placement. Still, rare are the references that explore real large-scale scenarios or that explore the communication feasibility between meters and key devices meticulously, considering the detailed topography between the devices. Furthermore, we are not aware of device positioning methods that prioritize candidate positions with DAs. Thus, the main contributions of this work are the following:  
* The presentation of an efficient AMI planning method with a large-scale focus: Four real large AMI network scenarios, including urban and rural areas, are used in the experiments to evaluate the method's performance in large-scale projects. These scenarios include more than 230,000 smart meters. Experimentation with large-scale real data is not common in the existing literature. We believe that exploring large scenarios allows us to evaluate the proposed method under real conditions, verifying the method's behavior for regions with different concentrations of meters and poles which demand a large coverage area and can present very different terrain characteristics. * The use of a propagation model that does not depend on empirical terrain classification: A detailed propagation model, including terrain diffraction loss, is applied for the link budget calculation. Instead of using a standard and general link budget approach to compute wireless link losses, the proposed method employs a detailed terrain profile analysis between the smart meters and positions of routers and gateways, leading to a more accurate link quality estimation. An additional experiment compares AIDA (AI-driven AMI network planning with DA-based information and a link-specific propagation model) to the classic Erceg-SUI/IEEE 802.16.3 Suburban Path Loss model [6; 7]. The analysis shows that AIDA with its proposed path loss model can propose topologies with fewer routers because it applies a detailed terrain profile in the path loss link analysis. It is rare in the literature research that explores the complete analysis of the topography profile for the path between the devices, mainly because of the number of meters and poles to be evaluated. However, in our proposed method, we make this viable using different strategies (heuristic and grid-based approaches) to minimize the number of connections to be computed. * The use of a heuristic approach based on a spanning tree and clustering, capable of evaluating a smaller number of connections and resulting in efficient topologies that use fewer routers and gateways: This research proposes a heuristic (AI-driven approach) for planning key devices' positioning in large-scale AMI wireless networks.