This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.

Features:

Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subject Discusses advanced data gathering and sharing/ distributed sensing techniques with its business applicability Includes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow charts Provides pervasive background knowledge including both wireless communications and WSNs Covers wireless networks as well as sensor network models in detail

This book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.

Features:

Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subject Discusses advanced data gathering and sharing/ distributed sensing techniques with its business applicability Includes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow charts Provides pervasive background knowledge including both wireless communications and WSNs Covers wireless networks as well as sensor network models in detail

This book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.Features:Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subjectDiscusses advanced data gathering and sharing/ distributed sensing techniques with its business applicabilityIncludes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow chartsProvides pervasive background knowledge including both wireless communications and WSNsCovers wireless networks as well as sensor network models in detailThis book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.Features:Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subjectDiscusses advanced data gathering and sharing/ distributed sensing techniques with its business applicabilityIncludes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow chartsProvides pervasive background knowledge including both wireless communications and WSNsCovers wireless networks as well as sensor network models in detailThis book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also: Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications. Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing. Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0. Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability. Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.

This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also: Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications. Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing. Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0. Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability. Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.

This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also:Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications.Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing.Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0.Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability.Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.

This book constitutes the refereed proceedings of the Second International Conference on Emerging Wireless Technologies and Sciences, ICEWTS 2024, held in Uttarakhand, India, during October 6–7, 2024.The 8 full papers and 4 short papers included in this book were carefully reviewed and selected from 430 submissions. They focus on multidisciplinary framework of communication engineering, wireless sensor networks, electrical engineering, renewable energies, materials science, physics, mathematics, computer, environment, and many more.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.

Features:

Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subject Discusses advanced data gathering and sharing/ distributed sensing techniques with its business applicability Includes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow charts Provides pervasive background knowledge including both wireless communications and WSNs Covers wireless networks as well as sensor network models in detail

This book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.

Features:

Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subject Discusses advanced data gathering and sharing/ distributed sensing techniques with its business applicability Includes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow charts Provides pervasive background knowledge including both wireless communications and WSNs Covers wireless networks as well as sensor network models in detail

This book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.Features:Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subjectDiscusses advanced data gathering and sharing/ distributed sensing techniques with its business applicabilityIncludes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow chartsProvides pervasive background knowledge including both wireless communications and WSNsCovers wireless networks as well as sensor network models in detailThis book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book covers wireless communication, security issues, advanced wireless sensor networks (WSNs), routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs.Features:Covers technologies supporting advanced wireless communication systems, sensor networks, and the conceptual development of the subjectDiscusses advanced data gathering and sharing/ distributed sensing techniques with its business applicabilityIncludes numerous worked-out mathematical equations and formulas, as well as essential principles including figures, illustrations, algorithms, and flow chartsProvides pervasive background knowledge including both wireless communications and WSNsCovers wireless networks as well as sensor network models in detailThis book is aimed at graduate students, researchers, and academics working in the field of computer science, wireless communication technology, and advanced WSNs.

This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also: Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications. Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing. Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0. Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability. Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.

This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also: Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications. Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing. Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0. Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability. Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.

This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also:Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications.Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing.Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0.Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability.Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.

This book constitutes the refereed proceedings of the Second International Conference on Emerging Wireless Technologies and Sciences, ICEWTS 2024, held in Uttarakhand, India, during October 6–7, 2024.The 8 full papers and 4 short papers included in this book were carefully reviewed and selected from 430 submissions. They focus on multidisciplinary framework of communication engineering, wireless sensor networks, electrical engineering, renewable energies, materials science, physics, mathematics, computer, environment, and many more.