Harry G. Perros – författare

This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with a good understanding of their inner workings. This is an introductory book for readers who have no familiarity with these techniques.The techniques presented in An Introduction to IoT Analytics come from the areas of machine learning, statistics, and operations research. Machine learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data and dimensionality reduction of data sets. Operations research is concerned with the performance of an IoT system by constructing a model of the system under study and then carrying out a what-if analysis. The book also describes simulation techniques.Key Features IoT analytics is not just machine learning but also involves other tools, such as forecasting and simulation techniques. Many diagrams and examples are given throughout the book to fully explain the material presented. Each chapter concludes with a project designed to help readers better understand the techniques described. The material in this book has been class tested over several semesters. Practice exercises are included with solutions provided online at www.routledge.com/9780367686314Harry G. Perros is a Professor of Computer Science at North Carolina State University, an Alumni Distinguished Graduate Professor, and an IEEE Fellow. He has published extensively in the area of performance modeling of computer and communication systems.

This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with a good understanding of their inner workings. This is an introductory book for readers who have no familiarity with these techniques.The techniques presented in An Introduction to IoT Analytics come from the areas of machine learning, statistics, and operations research. Machine learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data and dimensionality reduction of data sets. Operations research is concerned with the performance of an IoT system by constructing a model of the system under study and then carrying out a what-if analysis. The book also describes simulation techniques.Key Features IoT analytics is not just machine learning but also involves other tools, such as forecasting and simulation techniques. Many diagrams and examples are given throughout the book to fully explain the material presented. Each chapter concludes with a project designed to help readers better understand the techniques described. The material in this book has been class tested over several semesters. Practice exercises are included with solutions provided online at www.routledge.com/9780367686314Harry G. Perros is a Professor of Computer Science at North Carolina State University, an Alumni Distinguished Graduate Professor, and an IEEE Fellow. He has published extensively in the area of performance modeling of computer and communication systems.

A thorough knowledge of modern connection-oriented networks is essential to understanding the current and near-future state of networking.This book provides a complete overview of connection-oriented networks, discussing both packet-switched and circuit-switched networks, which, though seemingly different, share common networking principles. It details the history and development of such networks, and defines their terminology and architecture, before progressing to aspects such as signaling and standards. There is inclusive coverage of SONET/SDH, ATM networks, Multi-Protocol Label Switching (MPLS), optical networks, access networks and voice over ATM and MPLS.Connection-oriented Networks:* Provides in-depth, systematic coverage of several connection-oriented networks in a single volume* Explains topics such as the Generic Framing Procedure, Label Distribution Protocols, Wavelength Routing Optical Networks, Optical Burst Switching, and Access Networks in detail* Illustrates all concepts with problems and simulation projects to test and deepen your understanding* Includes an accompanying website with solutions manual and complete set of PowerPoint presentations for each chapterSenior undergraduate and graduate students in telecommunication and networking courses, as well as networking engineers, will find this comprehensive guide to connection-oriented packet-switched and circuit-switched networks useful for their training. The book presents tried and tested material based on an existing, successful course.

Asynchronous Transfer Mode (ATM) has revolutionized telecommunications, and has become an integral part of the networking infrastructure. This introductory well-structured text on ATM networks describes their development, architecture, congestion control, deployment, and signalling in an intuitive, accessible way. It covers extensive background information and includes exercises that support the explanations throughout the book. The networking expert Harry G. Perros explains ATM networks, including such hot topics as:* ATM adaptation layer 2* Quality of Service* Congestion control* Tag switching and MPLS (Multi-Protocol Label Switching)* ADSL-based access networks* Signalling* PNNI (Private Network Node Interface)An Introduction to ATM Networks is a textbook for graduate students and undergraduates in electrical engineering and computer science as well as a reference work for networking engineers. An Online solutions Manual is now available.

There has been a phenomenal growth in video applications over the past few years. An accurate traffic model of Variable Bit Rate (VBR) video is necessary for performance evaluation of a network design and for generating synthetic traffic that can be used for benchmarking a network. A large number of models for VBR video traffic have been proposed in the literature for different types of video in the past 20 years. Here, the authors have classified and surveyed these models and have also evaluated the models for H.264 AVC and MVC encoded video and discussed their findings.