The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.

This unique book provides excellent examples of ongoing, leading-edge research related to viruses, especially COVID-19. It is written from the viewpoint of various scientific fields including materials science. It introduces and describes viruses (submicroscopic infectious agents that replicate inside the living cells of an organism), various infections caused by viruses (human to human, human to other organisms to humans, humans to materials to humans, etc.), not only from the viewpoint of medical research but also from other scientific disciplines. A major focus of the book is the COVID-19 virus. Highlighted topics include the evolution of COVID-19, transmission of virus particles through the air, virus spread through various materials, detection of the virus by testing wastewater, the development and testing of vaccines and therapeutic drugs, and the preparation for future viruses and pandemics. This includes reform in funeral services to properly and safely accommodate very large numbers of bodies in a pandemic, like those seen in New York City when it was the epicenter for the virus in the United States. This book serves as an excellent and very informative guide (practical book) for engineers and researchers of various backgrounds and as a great academic textbook.

As networks of video cameras are installed in many applications like security and surveillance, environmental monitoring, disaster response, and assisted living facilities, among others, image understanding in camera networks is becoming an important area of research and technology development. There are many challenges that need to be addressed in the process. Some of them are listed below: - Traditional computer vision challenges in tracking and recognition, robustness to pose, illumination, occlusion, clutter, recognition of objects, and activities; - Aggregating local information for wide area scene understanding, like obtaining stable, long-term tracks of objects; - Positioning of the cameras and dynamic control of pan-tilt-zoom (PTZ) cameras for optimal sensing; - Distributed processing and scene analysis algorithms; - Resource constraints imposed by different applications like security and surveillance, environmental monitoring, disaster response, assisted living facilities, etc. In this book, we focus on the basic research problems in camera networks, review the current state-of-the-art and present a detailed description of some of the recently developed methodologies. The major underlying theme in all the work presented is to take a network-centric view whereby the overall decisions are made at the network level. This is sometimes achieved by accumulating all the data at a central server, while at other times by exchanging decisions made by individual cameras based on their locally sensed data. Chapter One starts with an overview of the problems in camera networks and the major research directions. Some of the currently available experimental testbeds are also discussed here. One of the fundamental tasks in the analysis of dynamic scenes is to track objects. Since camera networks cover a large area, the systems need to be able to track over such wide areas where there could be both overlapping and non-overlapping fields of view of the cameras, as addressed in Chapter Two: Distributed processing is another challenge in camera networks and recent methods have shown how to do tracking, pose estimation and calibration in a distributed environment. Consensus algorithms that enable these tasks are described in Chapter Three. Chapter Four summarizes a few approaches on object and activity recognition in both distributed and centralized camera network environments. All these methods have focused primarily on the analysis side given that images are being obtained by the cameras. Efficient utilization of such networks often calls for active sensing, whereby the acquisition and analysis phases are closely linked. We discuss this issue in detail in Chapter Five and show how collaborative and opportunistic sensing in a camera network can be achieved. Finally, Chapter Six concludes the book by highlighting the major directions for future research. Table of Contents: An Introduction to Camera Networks / Wide-Area Tracking / Distributed Processing in Camera Networks / Object and Activity Recognition / Active Sensing / Future Research Directions

By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. Networked Filtering and Fusion in Wireless Sensor Networks introduces the subject of multi-sensor fusion as the method of choice for implementing distributed systems. The book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case studies. Complete with research supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for WSNs. The book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help you broaden your understanding of prospective applications and how to address such problems in practice. After reading the book, you will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.

A systematic survey of many of these recent results on Gossip network algorithms.

This book investigates observer-fault estimation techniques in detail, while also highlighting recent research and findings regarding fault estimation. Many practical control systems are subject to possible malfunctions, which may cause significant performance loss or even system instability. To improve the reliability, performance and safety of dynamical systems, fault diagnosis techniques are now receiving considerable attention, both in research and applications, and have been the subject of intensive investigations. Fault detection – the essential first step in fault diagnosis – is a binary decision-making process used to determine whether or not a fault has occurred. In turn, fault isolation is used to identify the location of the faulty component, while fault estimation is used to identify the size of the fault online. Compared with the problems involved in fault detection and isolation, fault estimation is considerably more challenging.

The electrical demands in several countries around the world are increasing due to the huge energy requirements of prosperous economies and the human activities of modern life. In order to economically transfer electrical powers from the generation side to the demand side, these powers need to be transferred at high-voltage levels through suitable transmission systems and power substations. To this end, high-voltage transmission systems and power substations are in demand. Actually, they are at the heart of interconnected power systems, in which any faults might lead to unsuitable consequences, abnormal operation situations, security issues, and even power cuts and blackouts. In order to cope with the ever-increasing operation and control complexity and security in interconnected high-voltage power systems, new architectures, concepts, algorithms, and procedures are essential. This book aims to encourage researchers to address the technical issues and research gaps in high-voltage transmission systems and power substations in modern energy systems.

This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.

This unique introduction to the foundational concepts of cyber-physical systems (CPS) describes key design principles and emerging research trends in detail. Several interdisciplinary applications are covered, with a focus on the wide-area management of infrastructures including electric power systems, air transportation networks, and health care systems. Design, control and optimization of cyber-physical infrastructures are discussed, addressing security and privacy issues of networked CPS, presenting graph-theoretic and numerical approaches to CPS evaluation and monitoring, and providing readers with the knowledge needed to operate CPS in a reliable, efficient, and secure manner. Exercises are included. This is an ideal resource for researchers and graduate students in electrical engineering and computer science, as well as for practitioners using cyber-physical systems in aerospace and automotive engineering, medical technology, and large-scale infrastructure operations.