Cognitive Decision Modeling for Quality of Service in Domestic Pipeline Network

The Internet of Things (IoT) has transformed the industrial sector. This study presents a novel framework for real-time evaluation of service quality in residential gas pipeline networks. IoT devices collect critical operational and environmental data, which are processed through a fog-cloud architecture using Bayesian modeling. This enables the calculation of a comprehensive Quality of Service Measure (QSM) and a Service Quality Delivery Value (SQDV) to assess and interpret service performance. A two-level decision-tree model further supports decisions by regulators and end-users. The framework was validated using 73,462 service interaction records, showing significant improvements over existing approaches: reduced data delay (179.01 s), high classification performance (Specificity: 94.22%, Sensitivity: 92.78%, Precision: 93.15%), enhanced decision-making (Accuracy: 95.45%, Error Rate: 1.29%), improved reliability (93.69%), and robust system stability (73.25%).