A Novel Data Aggregation Point Placement Method for Smart Metering Service using LoRaWAN Technology

The operation of Advanced Metering Infrastructure (AMI) requires an adequate definition of the quantity and placement of Data Aggregation Points (DAPs) to ensure that data sent from Smart Meters (SMs) are received correctly and in a timely manner by operators of the electric utility. In this context, this paper proposes a method based on the K-Means clustering technique, named LoRaWAN Optimized Clustering Placement (LOCPlace), to define the quantity and positions of DAPs in a simulated network of Long Range Wide Area Network (LoRaWAN) technology, along with a comparative evaluation with three methods proposed in the literature: DPLACE, KMD, and KM. The simulated network consists of an urban scenario spanning 49 km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, with 1000 SMs, and the method assists in the planning of AMI for aggregating data from residential Intelligent Meter Reading (IMR) applications. The evaluation of the proposed method and related works is accomplished through metrics related to clustering, such as the number of clusters and average intra-cluster distances, and network performance metrics, including Packet Delivery Rate (PDR) and delay. The results demonstrate a superior performance of LOCPlace in relation to the alternative proposals, as it ensures the reliability requirements of packet delivery and maximum delay of the tested application with up to 20% less DAPs, besides achieving results concerning communication metrics similar to the alternative methods.