The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks. The book is directed at the sensing, signal processing, and ICTs research communities. The contents will be of particular use to researchers (from academia and industry) and practitioners working in wireless sensor networks, IoT, E-health and Industry 4.0 applications who wish to understand the basics of inference problems. It will also be of interest to professionals, and graduate and PhD students who wish to understand the fundamental concepts of inference algorithms based on intelligent and energy-efficient protocols.

The digital transformation of healthcare delivery is in full swing. Health monitoring is increasingly becoming more effective, efficient, and timely through mobile devices that are now widely available. This, as well as wireless technology, is essential to assessing, diagnosing, and treating medical ailments. However, systems and applications that boost wellness must be properly designed and regulated in order to protect the patient and provide the best care. Optimizing Health Monitoring Systems With Wireless Technology is an essential publication that focuses on critical issues related to the design, development, and deployment of wireless technology solutions for healthcare and wellness. Highlighting a broad range of topics including solution evaluation, privacy and security, and policy and regulation, this book is ideally designed for clinicians, hospital directors, hospital managers, consultants, health IT developers, healthcare providers, engineers, software developers, policymakers, researchers, academicians, and students.

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics. Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source. This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

The second in the Women Securing the Future with TIPPSS series, this book provides insight and expert advice from seventeen women leaders in technology, healthcare and policy to address the challenges of Trust, Identity, Privacy, Protection, Safety and Security (TIPPSS) for connected healthcare, and the growing Internet of Medical Things (IoMT) ecosystem. The ten chapters in this book delve into trust, security and privacy risks in connected healthcare for patients, medical devices, personal and clinical data, healthcare providers and institutions, and provide practical approaches to manage and protect the data, devices, and humans. Cybersecurity, technology and legal experts discuss risks, from data and device hacks to ransomware, and propose approaches to address the challenges including new frameworks for architecting and evaluating medical device and connected hospital cybersecurity. We all need to be aware of the TIPPSS challenges in connected healthcare, and we call upon engineers, device manufacturers, system developers and healthcare providers to ensure trust and manage the risk. Featuring contributions from prominent female experts and role models in technology, cybersecurity, engineering, computer science, data science, business, healthcare, accessibility, research, law, privacy and policy, this book sets the stage to improve security and safety in our increasingly connected world.

Autonomic networking aims to solve the mounting problems created by increasingly complex networks, by enabling devices and service-providers to decide, preferably without human intervention, what to do at any given moment, and ultimately to create self-managing networks that can interface with each other, adapting their behavior to provide the best service to the end-user in all situations. This book gives both an understanding and an assessment of the principles, methods and architectures in autonomous network management, as well as lessons learned from, the ongoing initiatives in the field. It includes contributions from industry groups at Orange Labs, Motorola, Ericsson, the ANA EU Project and leading universities. These groups all provide chapters examining the international research projects to which they are contributing, such as the EU Autonomic Network Architecture Project and Ambient Networks EU Project, reviewing current developments and demonstrating how autonomic management principles are used to define new architectures, models, protocols, and mechanisms for future network equipment.

Smart City and sensing platforms are considered some of the most significant topics in the Internet of Things (IoT). Sensors are at the heart of the IoT, and their development is a key issue if such concepts are to achieve their full potential. This book addresses the major challenges in realizing smart city and sensing platforms in the era of the IoT and the Cloud. Challenges vary from cost and energy efficiency to availability and service quality. To tackle these challenges, sensors must meet certain expectations and requirements such as size constraints, manufacturing costs and resistance to environmental factors. This book focuses on both the design and implementation aspects for smart city and sensing applications that are enabled and supported by IoT paradigms. Attention is also given to data delivery approaches and performance aspects.

With the advent of wavelength routing and dynamic, reconfigurable optical networks, new demands are being made in the design and operation of optical amplifiers. This book provides, for the first time, a comprehensive review of optical amplifier technology in the context of these recent advances in the field. It demonstrates how to manage the trade-offs between amplifier design, network architecture and system management and operation. The book provides an overview of optical amplifiers and reconfigurable networks before examining in greater detail the issues of importance to network operators and equipment manufacturers, including 40G and 100G transmission. Optical amplifier design is fully considered, focusing on fundamentals, design solutions and amplifier performance limitations. Finally, the book discusses other emerging applications for optical amplifiers such as optical networks for high data rate systems, free space systems, long single span links and optical digital networks. This book will be of great value to R&D engineers, network and systems engineers, telecommunications service providers, component suppliers, industry analysts, network operators, postgraduate students, academics and anyone seeking to understand emerging trends in optical networks and the consequent changes in optical amplifier design, features and applications.

Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gerard Govaert is Professor at the University of Technology of Compiegne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabilistic approaches are established. A combination of algorithms are proposed and evaluated on simulated and real data. Chapter 5 considers a co-clustering or bi-clustering as the search for coherent co-clusters in biological terms or the extraction of co-clusters under conditions. Classical algorithms will be described and evaluated on simulated and real data. Different indices to evaluate the quality of coclusters are noted and used in numerical experiments.

The communication field is evolving rapidly in order to keep up with society's demands. As such, it becomes imperative to research and report recent advancements in computational intelligence as it applies to communication networks. The Handbook of Research on Recent Developments in Intelligent Communication Application is a pivotal reference source for the latest developments on emerging data communication applications. Featuring extensive coverage across a range of relevant perspectives and topics, such as satellite communication, cognitive radio networks, and wireless sensor networks, this book is ideally designed for engineers, professionals, practitioners, upper-level students, and academics seeking current information on emerging communication networking trends.