TINY MACHINE LEARNING (TINYML) FOR INTELLIGENT INTERNET OF THINGS DEVICES: ARCHITECTURES, APPLICATIONS, CHALLENGES, AND FUTURE PERSPECTIVES

The rapid growth of the Internet of Things (IoT) has created an increasing demand for intelligent data processing directly on resource-constrained devices. Traditional cloud-based artificial intelligence solutions often introduce latency, bandwidth consumption, and privacy concerns, limiting their effectiveness in real-time applications. Tiny Machine Learning (TinyML) has emerged as an innovative computing paradigm that enables machine learning models to execute efficiently on microcontrollers and low-power embedded systems. This paper presents a comprehensive review of TinyML, covering its architecture, enabling technologies, optimization techniques, practical applications, implementation challenges, and future research opportunities. The study also explores the integration of TinyML with edge computing, federated learning, wireless sensor networks, and energy-efficient hardware accelerators. The findings demonstrate that TinyML significantly enhances intelligent decision-making while reducing energy consumption, communication overhead, and deployment costs, making it a promising technology for next-generation smart devices.