Computational Intelligence In Telecommunications Networks

Telecommunications has evolved and grown at an explosive rate in recent years and will undoubtedly continue to do so. As its functions, applications, and technology grow, it becomes increasingly complex and difficult, if not impossible, to meet the demands of a global network using conventional computing technologies. Computational intelligence (CI) is the technology of the future-and the future is now. Computational Intelligence in Telecommunications Networks offers an in-depth look at the rapid progress of CI technology and shows its importance in solving the crucial problems of future telecommunications networks. It covers a broad range of topics, from Call Admission Control, congestion control, and QoS-routing for ATM networks, to network design and management, optical, mobile, and active networks, and Intelligent Mobile Agents. Today's telecommunications professionals need a working knowledge of CI to exploit its potential to overcome emerging challenges. The CI community must become acquainted with those challenges to take advantage of the enormous opportunities the telecommunications field offers. This text meets both those needs, clearly, concisely, and with a depth certain to inspire further theoretical and practical advances.

This book focuses on the use of Artificial Intelligence and Machine Learning (AI/ML) based techniques to solve issues related to communication networks, their layers, as well as their applications. The book first offers an introduction to recent trends regarding communication networks. The authors then provide an overview of theoretical concepts of AI/ML, techniques and protocols used in different layers of communication. Furthermore, this book presents solutions that help analyze complex patterns in user data and ultimately improve productivity. Throughout, AI/ML-based solutions are provided, for topics such as signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction, software-defined networking, congestion control, communication network optimization, security, and anomaly detection. The book features chapters from a large spectrum of authors including researchers, students, as well as industrials involved in research and development.

Telecommunications has evolved and grown at an explosive rate in recent years and will undoubtedly continue to do so. As its functions, applications, and technology grow, it becomes increasingly complex and difficult, if not impossible, to meet the demands of a global network using conventional computing technologies. Computational intelligence (CI) is the technology of the future-and the future is now. Computational Intelligence in Telecommunications Networks offers an in-depth look at the rapid progress of CI technology and shows its importance in solving the crucial problems of future telecommunications networks. It covers a broad range of topics, from Call Admission Control, congestion control, and QoS-routing for ATM networks, to network design and management, optical, mobile, and active networks, and Intelligent Mobile Agents. Today's telecommunications professionals need a working knowledge of CI to exploit its potential to overcome emerging challenges. The CI community must become acquainted with those challenges to take advantage of the enormous opportunities the telecommunications field offers. This text meets both those needs, clearly, concisely, and with a depth certain to inspire further theoretical and practical advances.

This book discusses applications of computational intelligence in sensor networks. Consisting of twenty chapters, it addresses topics ranging from small-scale data processing to big data processing realized through sensor nodes with the help of computational approaches. Advances in sensor technology and computer networks have enabled sensor networks to evolve from small systems of large sensors to large nets of miniature sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological advances, sensor networks still face the challenges of communicating and processing large amounts of imprecise and partial data in resource-constrained environments. Further, optimal deployment of sensors in an environment is also seen as an intractable problem. On the other hand, computational intelligence techniques like neural networks, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the emerging research areas and presenting first-hand experience of a variety of computational intelligence approaches in sensor networks.

This book emphasizes the increasingly important role that Computational Intelligence (CI) methods are playing in solving a myriad of entangled Wireless Sensor Networks (WSN) related problems. The book serves as a guide for surveying several state-of-the-art WSN scenarios in which CI approaches have been employed. The reader finds in this book how CI has contributed to solve a wide range of challenging problems, ranging from balancing the cost and accuracy of heterogeneous sensor deployments to recovering from real-time sensor failures to detecting attacks launched by malicious sensor nodes and enacting CI-based security schemes. Network managers, industry experts, academicians and practitioners alike (mostly in computer engineering, computer science or applied mathematics) benefit from th e spectrum of successful applications reported in this book. Senior undergraduate or graduate students may discover in this book some problems well suited for their own research endeavors.

COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.

As other complex systems in social and natural sciences as well as in engineering, the Internet is hard to understand from a technical point of view. Packet switched networks defy analytical modeling. The Internet is an outstanding and challenging case because of its fast development, unparalleled heterogeneity and the inherent lack of measurement and monitoring mechanisms in its core conception. This monograph deals with applications of computational intelligence methods, with an emphasis on fuzzy techniques, to a number of current issues in measurement, analysis and control of traffic in the Internet. First, the core building blocks of Internet Science and other related networking aspects are introduced. Then, data mining and control problems are addressed. In the first class two issues are considered: predictive modeling of traffic load as well as summarization of traffic flow measurements. The second class, control, includes active queue management schemes for Internet routers as well as window based end-to-end rate and congestion control. The practical hardware implementation of some of the fuzzy inference systems proposed here is also addressed. While some theoretical developments are described, we favor extensive evaluation of models using real-world data by simulation and experiments.