Description du livre
Tackle the critical challenge of sustainability in modern technology with this essential book, which provides a comprehensive, expert-led exploration of energy-aware methodologies and machine learning strategies for optimizing IoT-based wireless sensor networks.
With the rapid expansion of IoT applications in diverse domains such as smart cities, agriculture, healthcare, and industry, energy constraints pose significant challenges to maintaining sustainable operations. This book addresses these challenges by presenting a comprehensive exploration of methodologies, technologies, and strategies aimed at optimizing energy usage in IoT-based wireless sensor networks. Authored by experts from academia and industry, the book covers topics such as energy-aware routing protocols, edge computing, energy harvesting technologies, machine learning applications, and blockchain-based energy management frameworks. Each chapter provides cutting-edge insights and practical approaches to fostering energy efficiency while ensuring robust and scalable IoT solutions. This book serves as a valuable resource for researchers, professionals, and policymakers, offering actionable knowledge to navigate the evolving landscape of IoT and wireless sensor network technologies.
Readers will find the volume: Explores the intersection of Internet of Things, wireless sensor networks, and energy efficiency across fundamental concepts, protocols, applications, and advanced solutions; Features chapters by leading researchers and industry professionals, providing authoritative perspectives; Offers actionable insights for implementing sustainable and energy-efficient IoT solutions in diverse fields like healthcare, agriculture, and smart cities; Highlights emerging trends, including AI, machine learning, and blockchain integration in wireless sensor networks.
Audience
Researchers, academics, engineers, system architects, technology developers, and policymakers specializing in IoT, wireless communications, and energy-efficient systems.