The objective of GCN 2011 is to facilitate an exchange of information on best practices for the latest research advances in the area of green communications and networks, which mainly includes the intelligent control, or efficient management, or optimal design of access network infrastructures, home networks, terminal equipment, and etc. Topics of interests include network design methodology, enabling technologies, network components and devices, applications, others and emerging new topics.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks. Provides a detailed theoretical foundation of machine learning and computational intelligence algorithms Highlights the state of art machine learning-based solutions for communication networks Presents video demonstrations and code snippets on each chapter for easy understanding of the concepts Discusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networks Demonstrates applications of machine learning techniques for optical networks The text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications, efforts are required toward designing new machine learning protocols and modifying the existing ones, which consume less energy, i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols, this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.

Educational initiatives attempt to introduce or promote a culture of quality within education by raising concerns related to student learning, providing services related to assessment, professional development of teachers, curriculum and pedagogy, and influencing educational policy, in the realm of technology. Adapting Information and Communication Technologies for Effective Education addresses ICT assessment in universities, student satisfaction in management information system programs, factors that impact the successful implementation of a laptop program, student learning and electronic portfolios, and strategic planning for e-learning. Providing innovative research on several fundamental technology-based initiatives, this book will make a valuable addition to every reference library.

"This book offers concepts of the teaching and learning of computer networking and hardwar eby offering undamental theoretical concepts illustrated with the use of interactive practical exercises"--Provided by publisher.

Artificial Intelligence (AI) is rapidly becoming essential to large-scale communication networks. Driven by the need for greater efficiency, security, and optimization, AI has evolved into a powerful tool that processes vast data and delivers insights through real-time processing, predictive analysis, and adaptive learning. Because these advancements transform how we interact with data and services, applying AI to complex networks has never been more essential. AI for Large Scale Communication Networks explores how AI can enhance network performance, scalability, and security. With contributions from experts, this book covers topics such as algorithm optimization, machine learning improvements, and neural network applications. It also addresses critical challenges like fault tolerance and distributed computing, emphasizing the need for interdisciplinary collaboration. Designed for academics, practitioners, and students, this resource provides actionable insights and strategies to optimize communication networks using AI.

EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels.

Information networking has emerged as a multidisciplinary diversified area of research over the past few decades. From traditional wired telephony to cellular voice telephony and from wired access to wireless access to the Internet, information networks have profoundly impacted our lifestyles as they have undergone enormous growth. To understand this technology, students need to learn several disciplines and develop an intuitive feeling of how they interact with one another. To achieve this goal, the book describes important networking standards, classifying their underlying technologies in a logical manner and gives detailed examples of successful applications.The emergence of wireless access and dominance of the Ethernet in LAN technologies has shifted the innovations in networking towards the physical layer and characteristics of the medium. This book pays attention to the physical layer while we provide fundamentals of information networking technologies which are used in wired and wireless networks designed for local and wide area operations. The book provides a comprehensive treatment of the wired IEEE802.3 Ethernet, and Internet as well as ITU cellular 2G-6G wireless networks, IEEE 802.11 for Wi-Fi, and IEEE 802.15 for Bluetooth, ZigBee and ultra-wideband (UWB) technologies. The novelty of the book is that it places emphasis on physical communications issues related to formation and transmission of packets and characteristics of the medium for transmission in variety of networks.Material presented in the book will be beneficial for students of Electrical and Computer Engineering, Computer Science, Robotics Engineering, Biomedical Engineering, or other disciplines who are interested in integration of navigation into their multi-disciplinary projects. The book provides examples with supporting MATLAB codes and hands-on projects throughout to improve the ability of the readers to understand and implement variety of algorithms.