• Sunita Adavimath
• Dr. Kailash Aseri

The Internet of Things (IoT) connects sensors, devices, machines, and services to collect and exchange information. Traditional IoT systems often send sensor data to cloud servers for analysis. Although cloud computing provides large processing capacity, continuous cloud communication can increase latency, bandwidth consumption, energy use, and privacy risks. Edge Artificial Intelligence (Edge AI) addresses these limitations by moving data processing and machine-learning inference closer to the data source. Tiny Machine Learning (TinyML) extends this idea to very small, low-power devices such as microcontrollers. TinyML enables devices to perform useful machine-learning tasks locally while operating with limited memory, computing power, and energy. This paper presents a simplified overview of TinyML and Edge AI for intelligent IoT systems. It explains their architecture, hardware requirements, machine-learning models, optimization techniques, development workflow, applications, security issues, and future research directions. Techniques such as quantization, pruning, knowledge distillation, efficient neural-network design, and optimized kernels help machine-learning models operate on resource-constrained devices. Applications include healthcare monitoring, smart agriculture, predictive maintenance, smart homes, environmental monitoring, and smart cities. The paper also discusses the cooperation of TinyML devices, edge servers, and cloud platforms. The study shows that combining TinyML with Edge AI can make IoT systems faster, more private, energy-efficient, and reliable.

TinyML, Edge AI, Internet of Things, Edge Computing, Machine Learning, Microcontrollers, Embedded AI, Intelligent IoT, Federated Learning, Energy-Efficient AI.

"TinyML and Edge AI for Intelligent IoT Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.b310-b315, October-2026, Available :http://www.jetir.org/papers/JETIR2610131.pdf

"TinyML and Edge AI for Intelligent IoT Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppb310-b315, October-2026, Available at : http://www.jetir.org/papers/JETIR2610131.pdf