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An Internet of Things' (IoT) connected society and system represents a tremendous paradigm shift. We present a framework for a decision-support system (DSS) that operates within the IoT ecosystem. The DSS leverages advanced analytics of electric smart meter (ESM) network communication-quality data to improve cost predictions for smart meter field operations and provide actionable decision recommendations regarding whether to send a technician to a customer location to resolve an ESM issue. The model is empirically evaluated using data sets from a commercial network. We demonstrate the efficiency of our approach with a complete Bayesian network prediction model and compare with three machine learning prediction model classifiers: 1) Naïve Bayes; 2) random forest; and 3) decision tree. Results demonstrate that our approach generates statistically noteworthy estimations and that the DSS will improve the cost efficiency of ESM network operations and maintenance.
Siryani et al. (2017) studied this question.