IoT-Enabled Predictive Analytics for Real-Time Decision-Making in Saudi Digital Service Platforms

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@article{1723953,
      author = {Ahmed Helmi},
      title = {IoT-Enabled Predictive Analytics for Real-Time Decision-Making in Saudi Digital Service Platforms},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {1686-1699},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723953.pdf},
      abstract = {Saudi digital service platforms are now becoming more reliant on connected devices, the streaming of telemetry data, and automated service orchestration, but the shift from simply observing events to being able to anticipate operational outcomes is still very uneven. This review looks at how Internet of Things (IoT)-based predictive analytics can assist with real-time decision-making in the various digital service environments in Saudi Arabia, focusing on the architectural, analytical and governance conditions needed for a reliable implementation. The study draws together evidence from peer-reviewed publications between 2020 and 2025 covering the topics of IoT, edge intelligence, machine learning, digital platforms, decision support, privacy, reliability and smart-city applications. Instead of viewing prediction as a separate algorithmic activity, the review presents it as a complete operational capability that involves sensing, data-quality control, contextual processing, model inference, decision policies, workflow execution and feedback on the outcomes. The evidence shows that the value obtained depends more on a low-latency architecture, trustworthy data, explainable decision thresholds, resilient connectivity and institutional ownership of the results than on the complexity of the models. For Saudi Arabia, these requirements are especially important since the country's digital transformation includes large-scale investments in infrastructure, high levels of mobile connectivity, smart-city projects and sector-specific service platforms. A reference architecture and a decision lifecycle are suggested in order to link IoT telemetry with predictive service operations while still ensuring security, privacy and human oversight. The review concludes that the most defensible approach is a federated, edge-cloud operating model in which predictive intelligence is incorporated into measurable service workflows and is governed by clear assurance controls.},
      month = {October},
  }