An Adaptive Algorithm for Cellular IoT Network Selection for Smart Grid Last-Mile Communications

Reliable last-mile connectivity at the cell edge remains a central challenge for Advanced Metering Infrastructure (AMI) in smart grids. This work addresses how to select between LTE-M and NB-IoT communications under weak-coverage conditions by combining field measurements with distribution-based channel modeling. We analyze multi-month Reference Signal Received Power (RSRP) datasets from three areas of a real AMI deployment (N = 30, 35, and 38 m, respectively) and fit canonical fading surrogates—Rayleigh, Rician, and Nakagami—to the normalized measurements. The principal decision statistic is the probability that RSRP falls below a practical threshold (−105 dBm), obtained from empirical and modeled CDF and translated into the predicted number of meters requiring fallback to NB-IoT. Across areas, Nakagami consistently provides the lowest or near-lowest Root Mean Square Error (RMSE) against empirical CDF and the closest agreement with observed fallback counts at −105 dBm, whereas Rayleigh tends to underestimate deep fade tails and Rician degrades when line-of-sight is weak. A threshold sweep sensitivity study (−110 to −89 dBm) using Area 3 illustrates how the predicted fallback population changes monotonically with the decision threshold and supports policy tuning. Overall, a CDF-anchored, Nakagami-guided rule at −105 dBm aligns technology selection with measured channel statistics, improving the robustness of Cellular IoT (CIoT) last-mile communications.