Optimizing IoT Data Modeling: Advanced Frameworks for Real-Time Analytics, Scalability, and Security

Abstract

The rapid expansion of the Internet of Things devices led to overwhelming data production, making real-time inspection harder and requiring improvements in data organization, scalability, and protection. Traditional data management systems encounter difficulties when they face the processing demands of fast-streaming IoT data with many formats and extensive sizes, so optimized frameworks must be developed. Integrating efficient data modeling methods ensures that IoT-produced information is securely transmitted to the right destinations while it processes efficiently. This research examines modern technological frameworks that optimize real-time analysis processes by streamlining system performance and expanding network capacity while safeguarding IoT systems from cyberattacks. The study systematically evaluates current methods alongside case examples to reveal essential weaknesses before presenting an optimal solution framework. Industry implementation of IoT solutions benefits from the research findings, which deliver practical knowledge to establish better-performing IoT infrastructure systems. The research field explores Artificial Intelligence development in IoT data optimization while establishing improved security procedures for modern IoT systems.