# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 3 Related Work
### Approaches for Key Devices Positioning
First, they establish an optimization model for the location of the AP that minimizes the cost of installing APs, while satisfying the constraints of reliability, network delay, and resiliency. Then, they propose an improved genetic algorithm (GA) to solve the optimization problem. As for the aspects of design constraints, they mention (a) the minimization of the cost of building the network, mainly the costs of installing the APs, (b) the maximization of the average level of reliability under normal operating conditions, (c) to ensure that the ENs (end-nodes) are always connected to at least one AP to keep the network running at a proper level even if a link becomes unavailable (i.e., ensuring that the network reliability remains above a predefined threshold), and (d) reduction in the communication delay to meet application requirements. The algorithm used to solve the planning problem is an improved GA. The density function is introduced based on the standard GA to avoid the local optimum and maintain population diversity. The analysis of the topologies of a power grid and the communication network can be done together, as demonstrated by the authors in [21]. This study has as its main objectives to establish the coordinates of the DAPs and to minimize the total average delay of the system, considering the volume of data traffic and the minimization of costs. They aim to do this without compromising QoS. This is achieved by focusing on the idea of aggregating and compressing the data associated with the same power feed in the appropriate DAP before being sent to the utility center (UC). The authors point out that having smart meters fed by different feeders of the energy network, connecting to the same DAP in the communication network, can lead to inefficient data aggregation. Therefore, the problem of positioning DAPs should not consider the communication network isolated from the electrical network. The authors formulate the problem as a mixed-integer non-linear optimization problem, and the optimization is done with a genetic algorithm. Network latency minimization is a common need in different studies and is addressed by [22]. The authors formulated a DAP problem and then used a clustering approach to network partitioning to minimize the maximum latency of data propagation between each DAP and the associated meters. They used a Dijkstra algorithm to calculate the shortest path between two nodes and consider the Haversine distance between the elements. Then, they used a clustering-based DAP placement (CDP) approach. The main network characteristics the authors considered include the number of meters, the position of meters, the transmission range, and the number of DAPs. Gateway positioning taking energy efficiency into account is explored by the authors in [23]. For the gateway positioning, they use an optimization method mainly based on distance (Euclidean and Manhattan), also considering the distance between coordinating devices (CDs) and gateways, the throughput, power consumption, load balancing, and link capacity. The method is performed in two stages: in the first stage, the candidate position selection is made using the Euclidean Distance, and in the second stage, the Gateway location selection is made using the Manhattan distance. The positioning of gateways to increase the capacity of the backhaul network by minimizing the average number of hops (ANH) is addressed by the authors in [24]. The study has applications in 5G ultra-dense networks but is included in our analysis because of our interest in controlling the number of hops. The methods used by the authors include clustering (using k-means and k-medoids algorithms) and the Dijkstra algorithm, used to find the average number of hops and to associate small cells with gateways (by identifying the shortest path). The network characteristics evaluated by the proposed method include the number of hops, the throughput, and the number of simultaneous transmissions.