Introduction

Data Analytics for IoT Applications explains how advanced computational methods support real-world IoT solutions. The book offers a clear and practical overview of how AI, machine learning, and big data analytics strengthen IoT systems across healthcare, agriculture, security, smart environments, and industrial safety. Going from intrusion detection and neural network optimization to smart sensing, energy-efficient communication, and early warning systems. It also showcases applied use cases such as disease detection, facial recognition, livestock monitoring, robotics in forensics, fraud detection, and smart water management, blending theory with hands-on applications.

Key Features

• - Integrates AI, machine learning, and big data with IoT systems.
• - Demonstrates practical applications through real-world case studies.
• - Examines privacy, security, and ethical considerations in IoT networks.
• - Explores emerging trends including nature-inspired algorithms, edge computing, and robotics.

Target Readership:

Researchers, postgraduate students, practitioners and professionals in computing and engineering looking to adopt intelligent IoT solutions.