# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 3 Related Work
### Approaches for Key Devices Positioning
The concept of gateway node placement problem (GNP) was explored by [25] to establish the smallest possible number of gateways to satisfy QoS requirements in search-and-rescue environments in a wireless mesh network (WMN). The authors treat the gateway placement problem (GNP) in combination with the router placement problem (RNP). The techniques considered by the authors include clustering strategies, area decomposition, and a heuristic approach (heuristic graph clustering technique). The method proposed by the authors is performed in two steps: the first step of the algorithm ensures that the calculated placements of the router nodes for a given deployment region meet the objectives of the RNP problem and its constraints. This means that the resulting WMN backbone network configuration maximizes the network coverage while maintaining network connectivity and minimizes the number of router nodes (RNs) used. The second step (GNP) ensures that the number of assigned gateway nodes is minimal and the division of the network topology graph into a set of disjoint clusters (subnets) satisfies three QoS constraints: RQoS (maximum communication delay), LQoS (maximum relay load for each RN) and SQoS (gateway throughput). The authors proposed the RRT-WMN algorithm (where RRT = rapidly exploring random trees) and used it combined with a heuristic approach to graph clustering. They used the RRT-WMN algorithm to resolve router placement. Then, the resulting network topology graph, along with the QoS constraints, is used as input to a graph clustering approach (which integrates the Weighted Recursive Dominating Set algorithm). Among the characteristics considered by the methods are obstacles, signal range, delay, load on routers, and throughput/gateway capacity. The authors highlight that an important measure of WMN network performance is network connectivity, which quantifies how well the routers are interconnected. They indicate that connectivity is even more important than network coverage or customer coverage, as it ensures that the router nodes are interconnected. In short, the GNP problem is about finding a minimum number of GNs and their placements to ensure a sufficient level of QoS based on criteria that directly influence network performance measures, such as communication delay, router load, and the capacity limits of GNs. Throughput optimization is the objective of the study by [26], which addresses gateway placement in WMN. They consider the number of gateways to be placed and the interference model of the network. The method proposed by the authors can be extended to multi-channel and multi-radio mesh networks. The authors proposed the positioning based on a grid, evaluated different positions for the gateways, and selected the combination that ensures the highest throughput. The proposed strategy is compared with random placement and with fixed placement. According to the authors, the grid-based positioning showed the best result in the experiments performed. Regarding the techniques, the authorsuse mixed-integer linear programming for the optimization/maximization of throughput (routing problem), a greedy algorithm for interference-free link scheduling, and a grid-based gateway placement scheme (which uses the linear programming method used for throughput as an assessment tool) for position selection. For the positioning processing, the main information considered by the method includes the analysis of: range (for interference range analysis), achieved flow (relation between the flow obtained concerning the flow demanded, treated as a constraint), and the total scheduled traffic. Meta-heuristic approaches are explored by [27] for the positioning of gateways in WMN. For this, they use Genetic Algorithm (GA) and Simulated Annealing (SA), considering the number of gateways and the number of hops that packets need to travel between the source and the destination (router/gateway).