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

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Title: An Adaptive Algorithm for Cellular IoT Network Selection for Smart Grid Last-Mile Communications.
Authors: Sangsuwan, Tanayoot1 (AUTHOR), Pirak, Chaiyod1 (AUTHOR) chaiyod.p@tggs.kmutnb.ac.th
Source: Energies (19961073). Apr2026, Vol. 19 Issue 8, p1963. 19p.
Subject Terms: *Communication infrastructure, *Channel estimation, *Smart power grids, *Smart meters, *Machine-to-machine communications
Abstract: 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. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193438303
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: An Adaptive Algorithm for Cellular IoT Network Selection for Smart Grid Last-Mile Communications.
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  Data: <searchLink fieldCode="AR" term="%22Sangsuwan%2C+Tanayoot%22">Sangsuwan, Tanayoot</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pirak%2C+Chaiyod%22">Pirak, Chaiyod</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chaiyod.p@tggs.kmutnb.ac.th</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Apr2026, Vol. 19 Issue 8, p1963. 19p.
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  Data: *<searchLink fieldCode="DE" term="%22Communication+infrastructure%22">Communication infrastructure</searchLink><br />*<searchLink fieldCode="DE" term="%22Channel+estimation%22">Channel estimation</searchLink><br />*<searchLink fieldCode="DE" term="%22Smart+power+grids%22">Smart power grids</searchLink><br />*<searchLink fieldCode="DE" term="%22Smart+meters%22">Smart meters</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine-to-machine+communications%22">Machine-to-machine communications</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193438303
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        Value: 10.3390/en19081963
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1963
    Subjects:
      – SubjectFull: Communication infrastructure
        Type: general
      – SubjectFull: Channel estimation
        Type: general
      – SubjectFull: Smart power grids
        Type: general
      – SubjectFull: Smart meters
        Type: general
      – SubjectFull: Machine-to-machine communications
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      – TitleFull: An Adaptive Algorithm for Cellular IoT Network Selection for Smart Grid Last-Mile Communications.
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              Text: Apr2026
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              Y: 2026
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