Energy Efficient Deep Learning Approaches In Iot Fog And Green Blockchain Revolution
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Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution
The convergence of Internet of Things (IoT), fog computing, and blockchain technology can be used to revolutionize energy efficiency and sustainability. The implementation of deep learning (DL) techniques may optimize the energy consumption of these interconnected systems. Thus, they can be used to create green, energy-efficient solutions for various industries, including smart cities, healthcare, finance, and industrial IoT (IIoT). Focusing on the energy efficiency and environmental impact of these technologies, they provide valuable insights into creating sustainable and scalable systems. Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution bridges the knowledge gap between traditional IoT and blockchain research and the emerging need for energy-efficient and green technologies. It influences future research directions, encourages collaboration across disciplines, and inspires innovations that prioritize sustainability. Covering topics such as software-defined networking (SDN), ecosystem conservation, and monitoring systems, this book is an excellent resource for computer scientists, policymakers, technologists, industry practitioners, engineers, environmentalists, sustainability advocates, professionals, researchers, scholars, academicians, and more.
Responsible Innovation in Smart Healthcare: AI, IoT, and Ethical Sustainability Practices
The advancement of smart healthcare technologies has transformed how medical services are delivered, monitored, and personalized. As these innovations improve patient outcomes, operational efficiency, and predictive care, they also raise critical ethical and sustainability concerns. Responsible innovation requires a balance between technological progress and the ethical, social, and environmental implications of intelligent tools. This includes ensuring data privacy, addressing algorithmic bias, promoting equitable access, and minimizing environmental impact. By integrating ethical sustainability practices into the design and usage of AI and IoT in healthcare, organizations can foster an inclusive, transparent, and resilient healthcare system. Responsible Innovation in Smart Healthcare: AI, IoT, and Ethical Sustainability Practices explores the integration of AI and IoT in smart healthcare systems, focusing on how these technologies improve care delivery and patient outcomes. It explains the importance of responsible innovation by addressing ethical, social, and environmental considerations to ensure sustainable and equitable healthcare solutions. This book covers topics such as machine learning, ethics and law, and patient monitoring, and is a useful resource for medical professionals, computer engineers, academicians, researchers, and scientists.
Emerging Technologies and the Application of WSN and IoT
The Internet of Things (IoT) has numerous applications, including smart cities, industries, cloud-based apps, smart homes, and surveillance. The Internet of Things (IoT) enables smarter living by connecting devices, people, and objects. As networking became a crucial aspect of the Internet, rigorous design analysis led to the development of new research areas. The Internet of Things has revolutionized daily living in countless ways. It enables communication between buildings, people, portable gadgets, and vehicles, facilitating mobility. Smart cities and cloud-based data have transformed corporate practices. With billions of connected gadgets, everything will soon be able to communicate remotely. IoT networks, whether public or private, rely significantly on machine learning and software-defined networking. Indian and other governments have approved various research projects on IoT-based networking technologies. This field of study will significantly impact society in the future. Researchers are concerned about the many application areas and driving forces behind smart cities. The authors aim to provide insights into software-defined networking, artificial intelligence, and machine learning technologies used in IoT and networking. The framework focuses on practical applications and infrastructures. The book includes practical challenges, case studies, innovative concepts, and other factors that impact the development of realistic scenarios for smart surveillance. It also highlights innovative technology, designs, and algorithms that can accelerate the creation of smart city concepts. This resource includes real-world applications and case studies for smart city technology, enormous data management, and machine learning prediction, all with confidentiality and safety problems.