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A comprehensive treatment of Age of Information, this cutting-edge text includes detailed exposition and real-world applications.
Modern, current, and future communications/processing aspects motivate basic information-theoretic research for a wide variety of systems for which we do not have the ultimate theoretical solutions (for example, a variety of problems in network information theory as the broadcast/interference and relay channels, which mostly remain unsolved in terms of determining capacity regions and the like). Technologies such as 5/6G cellular communications, Internet of Things (IoT), and mobile edge networks, among others, not only require reliable rates of information measured by the relevant capacity and capacity regions, but are also subject to issues such as latency vs. reliability, availability of system state information, priority of information, secrecy demands, energy consumption per mobile equipment, sharing of communications resources (time/frequency/space), etc. This book, composed of a collection of papers that have appeared in the Special Issue of the Entropy journal dedicated to “Information Theory for Data Communications and Processing”, reflects, in its eleven chapters, novel contributions based on the firm basic grounds of information theory. The book chapters address timely theoretical and practical aspects that constitute both interesting and relevant theoretical contributions, as well as direct implications for modern current and future communications systems.
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Advances in wireless communications and networking technology have taken us towards a pervasively connected world in which a vast array of wireless devices, from mobile phones to environmental sensors, seamlessly communicate with each other. In many of these systems the freshness of the transmitted information is of high importance. Characterization of time-critical information can be achieved through the so-called real-time status updates that are messages, encapsulated in packets, carrying the timestamp of their generation. Status updates track time-varying content that needs to be transmitted from the generation point to a remote destination in a network. To quantify the freshness of information in networked systems, a novel metric, different from delay or latency, termed as “age of information” (AoI) has been introduced. In this thesis, we focus on characterizing and controlling age under various communication system setups. The first part of the thesis considers multiple access communication systems and comprises two papers. The first paper, investigates AoI in relation with throughput in a shared access setup with heterogeneous traffic. More specifically, we consider a shared access system consisting of a primary link and a network of secondary nodes, with multipacket reception (MPR) capabilities. To study the joint throughput-timeliness performance, we formulate two optimization problems considering both objectives and provide guidelines for the design of such a multiple access system satisfying both timeliness and throughput requirements. In the second paper, we study the AoI performance in various multiple access schemes, including scheduling and random access. We present an analysis of the AoI with and without packet management at the transmission queue of the source nodes, considering that packet management is the capability to replace unserved packets in the queue whenever newer ones arrive. We incorporate the effect of channel fading and network path diversity in such a system and provide simulation results that illustrate the impact of network operating parameters on the performance of the considered access protocols. The second part of the thesis considers the characterization of AoI and other freshness performance metrics in a point-to-point communication link, again comprising two papers. In the third paper of this thesis, we expand the concept of information ageing by introducing the cost of update delay (CoUD) metric to characterize the cost of having stale information at the destination. Furthermore, we introduce the value of information of update (VoIU) metric that captures the degree of importance of the update received at the destination. We employ queue-theoretic concepts and provide a theoretical analysis and insights into the prospects of cost and value. Finally, in the last paper, we study the properties of a sample path of the AoI process, and we obtain a general formula of its stationary distribution. We relate this result to a discrete time queueing system and provide a general expression of the generating function of AoI in relation with the system time, and the peak age of information (PAoI). To illustrate the applicability of the results, we analyze the AoI in single-server queues with different disciplines and assumptions. We build upon these results to provide a methodology for analyzing general non-linear age functions for this type of systems.
The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g. computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 53rd issue of Transactions on Large-scale Data and Knowledge-centered Systems, contains six fully revised selected regular papers. Topics covered include time series management from edge to cloud, segmentation for time series representation, similarity research, semantic similarity in a taxonomy, linked data semantic distance, linguistics-informed natural language processing, graph neural network, protected features, imbalanced data, causal consistency in distributed databases, actor model, and elastic horizontal scalability.
Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning concepts and techniques applied to key areas within optical communications and networking, reflecting the state-of-the-art research and industrial practices. The book gives knowledge and insights into the role machine learning-based mechanisms will soon play in the future realization of intelligent optical network infrastructures that can manage and monitor themselves, diagnose and resolve problems, and provide intelligent and efficient services to the end users. With up-to-date coverage and extensive treatment of various important topics related to machine learning for fiber-optic communication systems, this book is an invaluable reference for photonics researchers and engineers. It is also a very suitable text for graduate students interested in ML-based signal processing and networking. - Discusses the reasons behind the recent popularity of machine learning (ML) concepts in modern optical communication networks and the why/where/how ML can play a unique role - Presents fundamental ML techniques like artificial neural networks (ANNs), support vector machines (SVMs), K-means clustering, expectation-maximization (EM) algorithm, principal component analysis (PCA), independent component analysis (ICA), reinforcement learning, and more - Covers advanced deep learning (DL) methods such as deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) - - Individual chapters focus on ML applications in key areas of optical communications and networking
This volume includes the main contributions by the plenary speakers from the ISAAC congress held in Aveiro, Portugal, in 2019. It is the purpose of ISAAC to promote analysis, its applications, and its interaction with computation. Analysis is understood here in the broad sense of the word, including differential equations, integral equations, functional analysis, and function theory. With this objective, ISAAC organizes international Congresses for the presentation and discussion of research on analysis. The plenary lectures in the present volume, authored by eminent specialists, are devoted to some exciting recent developments in topics such as science data, interpolating and sampling theory, inverse problems, and harmonic analysis.
This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses topics covering a wide range of modern and practical ML techniques, frameworks and tools.
This book constitutes the refereed proceedings of the 26th Australasian Conference on Information Security and Privacy, ACISP 2021, held in Perth, WA, Australia, in November 2021. The 35 full papers presented were carefully revised and selected from 157 submissions. The papers present and discuss all aspects of information security and privacy as well as machine learning for privacy and much more.
This volume comprises the select proceedings of the 2nd International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC 2022). The content focuses on big data and cloud computing and the application of artificial intelligence, robotics and engineering, the Internet of things, and sensor technology. Some of the topics covered include robot control, perception systems, dexterous manipulation, robot sensing, and data fusion, technology architecture of IoT, information analysis and processing of IoT, dynamic resource supply and consumption, IoT chips and sensors, industrial IoT, and Big Data, the architecture of Big Data, security and privacy of Big Data, management and operation of cloud computing, etc. This volume will be of interest to those working in academia and industry in the fields of computation, communication, and engineering.