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The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.
By the end of the decade, approximately 50 billion devices will be connected over the internet using multiple services such as online gaming, ultra-high definition videos, and 5G mobile services. The associated data traffic demand in both fixed and mobile networks is increasing dramatically, causing network operators to have to migrate the existing optical networks towards next-generation solutions. The main challenge within this development stems from network operators having difficulties finding cost-effective next-generation optical network solutions that can match future high capacity demand in terms of data, reach, and the number of subscribers to support multiple network services on a common network infrastructure. Design, Implementation, and Analysis of Next Generation Optical Networks: Emerging Research and Opportunities is an essential reference source that discusses the next generation of high capacity passive optical access networks (PON) in terms of design, implementation, and analysis and offers a complete reference of technology solutions for next-generation optical networks. Featuring research on topics such as artificial intelligence, electromagnetic interface, and wireless communication, this book is ideally designed for researchers, engineers, scientists, and students interested in understanding, designing, and analyzing the next generation of optical networks.
Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.
As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.
This book provides a thorough introduction of 5G and B5G wireless networks, as well as cutting-edge technologies that aid in network design and development. This book also covers machine learning techniques for advanced communications. 5G and Beyond Wireless Communications: Fundamentals, Applications, and Challenges discusses the newest technologies for 5G and future networks, including CR networks, D2D networks, UAV-assisted communications, RIS-assisted communications, and ML for communication networks. Additionally, it discusses using antenna systems for advanced communications networks. It also explores various security issues and their solutions, as well as power and interference management and machine learning for optimization of network parameters. The book also examines the design of 5G antennas from a materials perspective, and a thorough analysis of the materials utilized to create innovative antennas for advanced communication network is discussed. The book concludes by discussing the advancement of ML-based communication networks and their future opportunities and challenges. This book will be helpful for researchers and master students who want to focus their research work in the area of next-generation advanced wireless communications.
Currently, the demand by consumption of agricultural products may be predicted quantitatively; moreover, the variation of harvest and production by the change of a farm's cultivated area, weather change, disease, insect damage, etc. is a challenge that has led to improper control of the supply and demand of agricultural products. Advancements in IoT and wireless sensor networks in precision agriculture and the cloud computing technology needed to deploy them can be used to address and solve these issues. IoT and WSN Applications for Modern Agricultural Advancements: Emerging Research and Opportunities is an essential research book that focuses on the development of effective data-computing operations on agricultural advancements that are fully supported by IoT, cloud computing, and wireless sensor network systems and explores prospective applications of computing, analytics, and networking in various interdisciplinary domains of engineering. Featuring a range of topics such as power monitoring, healthcare, and GIS, this book is ideal for IT practitioners, farmers, network analysts, researchers, professionals, academicians, industry experts, and students.
This book includes high-quality research on various aspects of intelligent interactive multimedia technologies in healthcare services. The topics covered in the book focus on state-of-the-art approaches, methodologies, and systems in the design, development, deployment, and innovative use of multimedia systems, tools, and technologies in healthcare. The volume provides insights into smart healthcare service demands. It presents all information about multimedia uses in e-healthcare applications. The book also includes case studies and self-assessment problems for readers and future researchers. This book proves to be a valuable resource to know how AI can be an alternative tool for automated and intelligent analytics for e-healthcare applications.
We anticipate that there will be an enormous amount of wireless devices connected to the Internet through the future-generation wireless networks. Those wireless devices vary from self-driving vehicles to smart wearable devices and intelligent house- hold electrical appliances. Under such circumstances, the network resource optimization faces the challenge of the requirement of both flexibility and performance. Current wireless communication still relies on one-size-fits-all optimization algorithms, which require meticulous design and elaborate maintenance, thus not flexible and cannot meet the growing requirements well. The future-generation wireless networks should be "smarter", which means that the artificial intelligence-driven software-level design will play a more significant role in network optimization. In this thesis, we present three different ways of leveraging artificial intelligence (AI) and machine learning (ML) to design network optimization algorithms for three wireless Internet of things network optimization problems. Our ML-based approaches cover the use of multi-layer feed-forward artificial neural network and the graph convolutional network as the core of our AI decision-makers. The learning methods are supervised learning (for static decision-making) and reinforcement learning (for dynamic decision-making). We demonstrate the viability of applying ML in future- generation wireless network optimizations through extensive simulations. We summarize our discovery on the advantage of using ML in wireless network optimizations as the following three aspects: 1. Enabling the distributed decision-making to achieve the performance that near a centralized solution, without the requirement of multi-hop information; 2. Tackling with dynamic optimization through distributed self-learning decision- making agents, instead of designing a sophisticated optimization algorithm; 3. Reducing the time used in optimizing the solution of a combinatorial optimization problem. We envision that in the foreseeable future, AI and ML could help network service designers and operators to improve the network quality of experience swiftly and less expensively.
Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
BIG DATA ANALYTICS FOR INTERNET OF THINGS Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc. A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics A treatment of machine learning techniques for IoT data analytics Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.