Machine Learning for CIoT Network Selection in AMI Networks

The evolution of Advanced Metering Infrastructure (AMI) requires reliable, energy-efficient, and scalable communication technologies for connecting large numbers of smart meters and gateways with utility backend systems. Among 3GPP Cellular Internet of Things (CIoT) technologies, Narrowband IoT (NB-IoT) and LTE-M are promising candidates due to their extended coverage, low cost, and power efficiency. However, selecting between them remains challenging because performance depends on deployment environments, spatial distribution, and radio signal conditions. This study addresses the CIoT network selection problem in AMI networks by applying machine learning to predict the appropriate communication technology from smart meter location and Reference Signal Received Power (RSRP). Three supervised learning algorithms, namely Decision Tree, Support Vector Machine, and XGBoost, were evaluated using field measurement datasets from two AMI deployment areas. A spatial holdout strategy was applied to assess performance in unseen geographical regions. Decision Tree achieved the best performance in Area 1, with an accuracy of 0.7143 and an F1-score of 0.6154. In Area 2, XGBoost achieved the highest performance, with an accuracy of 0.9732 and an F1-score of 0.9388. The results demonstrate the feasibility of ML-based CIoT selection under spatially heterogeneous and imbalanced deployment conditions.