Novel Semiconductor Chip-Based Smart Sensor Technology Design and Integration of IoT in AI-ML Hyperscale Infrastructure

Abstract

The integration of semiconductor chip-based smart sensors into hyperscale infrastructures, powered by IoT, AI, and ML, is essential for developing intelligent, data-driven systems. These sensors primarily use MEMS and silicon materials, measure physical parameters like temperature, pressure, and motion, generating critical data for real-time monitoring across industries such as healthcare, automotive, and smart cities. This paper discusses the design principles of these sensors, addressing challenges like miniaturization, energy efficiency, accuracy, scalability, and integration with AI-ML systems. The design process balances performance with power consumption, ensuring suitability for energy-constrained IoT devices. The role of sensors within IoT ecosystems is explored, focusing on edge computing for data preprocessing before transmission to centralized AI-ML systems for deeper analysis. This approach reduces latency and bandwidth, enabling real-time processing and predictive analytics for applications like predictive maintenance and anomaly detection. The paper also examines challenges integrating sensors into hyperscale AI-ML infrastructures, such as data volume, latency, security, and privacy issues. The architecture proposed comprised data acquisition, edge computing, data transfer, analytics, and action layers with an emphasis on data management. Finally, the paper highlights future advancements in energy-efficient, scalable, and secure IoT and AI-ML systems, which will drive the evolution of intelligent infrastructures.