Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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Distributed Optimization and Learning
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.
R 4,414.00
Distributed Optimization: Advances in Theories, Methods, and Applications
This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization; recent developments in distributed algorithms for various types of underlying communication networks; the implementation of computation-efficient and communication-efficient strategies in the execution of distributed algorithms; and the frameworks of convergence analysis and performance evaluation. On this basis, the book then thoroughly studies 1) distributed constrained optimization and the random sleep scheme, from an agent perspective; 2) asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks, from a communication network perspective; and 3) accelerated algorithms and stochastic gradient algorithms, from an algorithm perspective. Finally, the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.
R 3,891.48
Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments
This book offers a concise and in-depth exposition of specific algorithmic solutions for distributed optimization based control of multi-agent networks and their performance analysis. It synthesizes and analyzes distributed strategies for three collaborative tasks: distributed cooperative optimization, mobile sensor deployment and multi-vehicle formation control. The book integrates miscellaneous ideas and tools from dynamic systems, control theory, graph theory, optimization, game theory and Markov chains to address the particular challenges introduced by such complexities in the environment as topological dynamics, environmental uncertainties, and potential cyber-attack by human adversaries.The book is written for first- or second-year graduate students in a variety of engineering disciplines, including control, robotics, decision-making, optimization and algorithms and with backgrounds in aerospace engineering, computer science, electrical engineering, mechanical engineering and operations research. Researchers in these areas may also find the book useful as a reference.
R 1,986.09
Distributed Optimization, Game and Learning Algorithms
This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.
R 6,938.78
Distributed Optimization, Game and Learning Algorithms
This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.
R 6,669.78
Distributed Computing and Optimization Techniques
This book introduces research presented at the International Conference on Distributed Computing and Optimization Techniques (ICDCOT–2021), a two-day conference, where researchers, engineers, and academicians from all over the world came together to share their experiences and findings on all aspects of distributed computing and its applications in diverse areas. The book includes papers on distributed computing, intelligent system, optimization method, mathematical modeling, fuzzy logic, neural networks, grid computing, load balancing, communication. It will be a valuable resource for students, academics, and practitioners in the industry working on distributed computing.
R 6,228.00
Optimization Algorithms for Distributed Machine Learning
This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
R 1,832.00
Distributed Computing for Emerging Smart Networks
This book constitutes the refereed proceedings of the Second International Workshop on Distributed Computing for Emerging Smart Networks, DiCES-N 2020, held in Bizerte, Tunisia, in December 2020. Due to the COVID-19 pandemic the conference was held online. The 8 full papers included in this volume were carefully reviewed and selected from numerous submissions. The papers are organized in the following topical sections: intelligent transportation systems; emerging networking technologies; artificial intelligence and internet of things.
R 2,151.69
Artificial Intelligence and Quantum Computing for Advanced Wireless Networks
ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machinesAn exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and moreDiscussions of explainable neural networks and XAIExaminations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.
R 5,437.00
Intelligent Distributed Computing XII
This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.
R 9,484.29
Intelligent Distributed Computing XII
This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.
R 8,059.00
Distributed Computing for Emerging Smart Networks
This book contains extended versions of the best papers presented at the First International Workshop on Distributed Computing for Emerging Smart Networks, DiCES-N 2019, held in Hammamet, Tunisia, in October 2019.The 9 revised full papers included in this volume were carefully reviewed and selected from 24 initial submissions. The papers are organized in the following topical sections: intelligent transportation systems; distributed computing for networking and communication; articial intelligence applied to cyber physical systems.
R 1,937.69
Distributed Artificial Intelligence
Distributed Artificial Intelligence (DAI) came to existence as an approach for solving complex learning, planning, and decision-making problems. When we talk about decision making, there may be some meta-heuristic methods where the problem solving may resemble like operation research. But exactly, it is not related completely to management research. The text examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. This publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource allocations, and plans for multiple agents.Topics included plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning.The book will attract researchers and practitioners who are working in management and computer science, and industry persons in need of a beginner to advanced understanding of the basic and advanced concepts.
R 3,360.34
Distributed Machine Learning and Gradient Optimization
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
R 6,551.19
Coordination of Distributed Problem Solvers
As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu nication. Limited internode communication stems from either inherent band width limitations of the communication medium or from the high computa tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.
R 4,650.90
Network Optimization
Network optimization is important in the modeling of problems and processes from such fields as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Recent advances in data structures, computer technology, and algorithm development have made it possible to solve classes of network optimization problems that until recently were intractable. The refereed papers in this volume reflect the interdisciplinary efforts of a large group of scientists from academia and industry to model and solve complicated large-scale network optimization problems.
R 4,030.00
Federated Learning for Future Intelligent Wireless Networks
Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers delivers a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. In the book, readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues that apply to wireless communications. Readers will also find: A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FLComprehensive explorations of wireless communication network design and optimization for federated learningPractical discussions of novel federated learning algorithms and frameworks for future wireless networksExpansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distributionPerfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.
R 4,722.00
Intelligent Networks
The book presents the latest developments in intelligent communication networks based on applicability from various domains of artificial intelligence and machine learning including channel modeling, model-based structure, channel prediction, and signal detection. It further explains important topics such as vehicular mobility modeling, human-centric network applications, security and privacy in social networks, and trust-based intelligent transportation systems. This book:Presents a model-based approach to constructing an effective network by using state-of-the-art artificial intelligent techniques. Discusses the theoretical and practical applications of channel prediction and signal detection. Introduces the fundamental concepts and application of vehicular networks in conjunction with artificial intelligence. Explores wireless communication network techniques enabled by human-centric applications, designed, and developed with artificial intelligence characteristics. Highlights the challenges in designing and developing an effective and intelligent communication network that can be applied in different domains of human activities for finding sustainable solutions.It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and information technology.
R 9,328.19
Intelligent Networks
This book explains how intelligence can be introduced into digital telephone networks. Beginning by outlining the basic principles of Intelligent Networks (IN), it tracks the application of the IN principle of separation of the service logic for advanced telephony services from the underlying call control infrastructures. The book explains how large public networks are updated to incorporate IN principles for advanced telephony services. This is illustrated using many practical examples which are set against a background of following the ETSI standards within the overall ITU-T framework of recommendations. This book is targeted at postgraduate telecommunications students, or engineers requiring a concise and practical introduction to IN.
R 4,417.00
Distributed Machine Learning and Gradient Optimization
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
R 6,761.11
Multi-agent Optimization
This book contains three well-written research tutorials that inform the graduate reader about the forefront of current research in multi-agent optimization. These tutorials cover topics that have not yet found their way in standard books and offer the reader the unique opportunity to be guided by major researchers in the respective fields. Multi-agent optimization, lying at the intersection of classical optimization, game theory, and variational inequality theory, is at the forefront of modern optimization and has recently undergone a dramatic development. It seems timely to provide an overview that describes in detail ongoing research and important trends. This book concentrates on Distributed Optimization over Networks; Differential Variational Inequalities; and Advanced Decomposition Algorithms for Multi-agent Systems. This book will appeal to both mathematicians and mathematically oriented engineers and will be the source of inspiration for PhD students and researchers.
R 2,564.00
Distributed Computing and Artificial Intelligence, 14th International Conference
The 14th International Symposium on Distributed Computing and Artificial Intelligence 2017 (DCAI 2017) provided a forum for presenting the application of innovative techniques to study and solve complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to advancing the development of systems that can meet the ever-growing demands of today’s society. The book brings together past experience, current work and promising future trends in distributed computing, artificial intelligence and their applications to efficiently solve real-world problems. It combines contributions in well-established and evolving areas of research, including the content of the DCAI 17 Special Sessions, which focused on multi-disciplinary and transversal aspects, such as AI-driven methods for multimodal networks and processes modeling, and secure management towards smart buildings and smart grids. The symposium was jointly organized by the Polytechnic of Porto, the Osaka Institute of Technology and the University of Salamanca. The latest event was held in Porto, Portugal, from 21st to 23rd June 2017.
R 6,228.00
Intelligent Distributed Computing IX
This book represents the combined peer-reviewed proceedings of the ninth International Symposium on Intelligent Distributed Computing – IDC’2015, of the Workshop on Cyber Security and Resilience of Large-Scale Systems – WSRL’2015, and of the International Workshop on Future Internet and Smart Networks – FI&SN’2015. All the events were held in Guimarães, Portugal during October 7th-9th, 2015. The 46 contributions published in this book address many topics related to theory and applications of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.
R 9,653.07
Intelligent Distributed Computing X
This book presents the combined peer-reviewed proceedings of the tenth International Symposium on Intelligent Distributed Computing (IDC’2016), which was held in Paris, France from October 10th to 12th, 2016. The 23 contributions address a range of topics related to theory and application of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.
R 6,556.80
Intelligent Distributed Computing X
This book presents the combined peer-reviewed proceedings of the tenth International Symposium on Intelligent Distributed Computing (IDC’2016), which was held in Paris, France from October 10th to 12th, 2016. The 23 contributions address a range of topics related to theory and application of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.
R 6,555.91
Telecommunications Optimization
Written in an accessible and easy-to-read style, this cutting-edge book presents advanced solutions to current and future telecommunications optimization problems. The field of telecommunications is growing and changing ever more rapidly, presenting new real-world problems for optimization researchers to address. Telecommunications engineers tend to know all about the problems involved but are often not aware of developments in computer science and artificial intelligence that might solve those problems. This unique book takes a colloborative approach describing the essence of the problems and then the heuristic and adaptive techniques which are now recognised as adept at solving these problems. In addition the emerging technologies in telecommunications and increasing use of the Internet expand the role that advanced heuristic and adaptive methods can play. Topics covered include: Heuristic techniques covering local search methods and population-based search techniquesAdaptive computation techniques covering neural computation, fuzzy logic and game theoryPractical and successful ways to address problems in network design and planning, routing, protocol design and network management This state-of-the-art book will be an essential resource for optimization researchers needing a wider appreciation of the problems in telecommunications, and indispensable for telecommunications engineers using heuristic and adaptive techniques.
R 10,549.00
Particle Swarm Optimization and Intelligence
Particle Swarm Optimization and Intelligence: Advances and Applications examines modern intelligent optimization algorithms proven as very efficient in applications from various scientific and technological fields. Providing distinguished and unique research, this innovative publication offers a compendium of leading field experiences as well as theoretical analyses and complementary techniques useful to academicians and practitioners.
R 6,987.32
R 428.00
Distributed Artificial Intelligence
Distributed Artificial Intelligence (DAI) came to existence as an approach for solving complex learning, planning, and decision-making problems. When we talk about decision making, there may be some meta-heuristic methods where the problem solving may resemble like operation research. But exactly, it is not related completely to management research. The text examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. This publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource allocations, and plans for multiple agents.Topics included plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning.The book will attract researchers and practitioners who are working in management and computer science, and industry persons in need of a beginner to advanced understanding of the basic and advanced concepts.
R 9,957.90
Cooperative and Distributed Intelligent Computation in Fog Computing
This informative text/reference presents a detailed review of the state of the art in fog computing paradigm. In particular, the book examines a broad range of important cooperative and distributed computation algorithms, along with their design objectives and technical challenges.The coverage includes the conceptual fundamental of fog computing, its practical applications, cooperative and distributed computation algorithms using optimization, swarm intelligence, matching theory, and reinforcement learning methods. Discussions are also provided on remaining challenges and open research issues for designing and developing the efficient distributed computation solutions in the next-generation of fog-enabled IoT systems.
R 5,380.67
Optimization and Decision Science: Methodologies and Applications
This proceedings volume highlights the state-of-the-art knowledge related to optimization, decisions science and problem solving methods, as well as their application in industrial and territorial systems. It includes contributions tackling these themes using models and methods based on continuous and discrete optimization, network optimization, simulation and system dynamics, heuristics, metaheuristics, artificial intelligence, analytics, and also multiple-criteria decision making. The number and the increasing size of the problems arising in real life require mathematical models and solution methods adequate to their complexity. There has also been increasing research interest in Big Data and related challenges. These challenges can be recognized in many fields and systems which have a significant impact on our way of living: design, management and control of industrial production of goods and services; transportation planning and traffic management in urban and regional areas; energy production and exploitation; natural resources and environment protection; homeland security and critical infrastructure protection; development of advanced information and communication technologies. The chapters in this book examine how to deal with new and emerging practical problems arising in these different fields through the presented methodologies and their applications. The chapter topics are applicable for researchers and practitioners working in these areas, but also for the operations research community. The contributions were presented during the international conference “Optimization and Decision Science” (ODS2017), held at Hilton Sorrento Palace Conference Center, Sorrento, Italy, September 4 – 7, 2017. ODS 2017, was organized by AIRO, Italian Operations Research Society, in cooperation with DIETI (Department of Electrical Engineering and Information Technology) of University “Federico II” of Naples.
R 6,110.31
Distributed Computer and Communication Networks
Thisbook constitutes the refereed proceedings of the 18th International Conferenceon Distributed and Computer and Communication Networks, DCCN 2015, held inMoscow, Russia, in October 2015.The 38 revised full papers presented were carefully reviewed andselected from 94 submissions. The papers cover the following topics: computerand communication networks architecture optimization; control in computer andcommunication networks; performance and QoS evaluation in wireless networks;modeling and simulation of network protocols; queuing and reliability theory;wireless IEEE 802.11, IEEE 802.15, IEEE 802.16, and UMTS (LTE) networks; FRIDtechnology and its application in intellectual transportation networks;protocols design (MAC, Routing) for centimeter and millimeter wave meshnetworks; internet and web applications and services; application integrationin distributed information systems; big data in communication networks.
R 2,188.88
Foundations of Distributed Artificial Intelligence
Distributed Artificial Intelligence (DAI) is a dynamic area of research and this book is the first comprehensive, truly integrated exposition of the discipline presenting influential contributions from leaders in the field. Commences with a solid introduction to the theoretical and practical issues of DAI, followed by a discussion of the core research topics--communication, coordination, planning--and how they are related to each other. The third section describes a number of DAI testbeds, illustrating particular strategies commissioned to provide software environments for building and experimenting with DAI systems. The final segment contains contributions which consider DAI from different perspectives.
R 8,595.00
Computational Intelligence in Wired and Wireless Networks
The text examines how computational intelligence can address the complex challenges and opportunities that arise in both wired and wireless networks. It discusses topics including fault tolerance and self-healing mechanisms, edge and fog computing, machine learning in wireless networks, quality of experience, and integration of emerging technologies.This book:Introduces theoretical concepts, and demonstrates how computational intelligence techniques can be applied to solve real-world problems in network optimization, security, quality of experience, and resource allocation. Discusses emerging technologies such as 5G and beyond, edge computing, and machine learning in the context of wired and wireless networks. Presents the integration of machine learning concepts, including deep learning and reinforcement learning, in the context of wireless networks. Highlights the use of computational intelligence techniques in enhancing network optimization by providing adaptive, self-learning, and efficient solutions. Covers design, management, and optimization of networks using machine learning, deep learning, and reinforcement learning. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer science and engineering, telecommunications, and information technology.
R 6,118.00
Distributed Artificial Intelligence for 5G/6G Communications
The aim of this book is to delineate the challenges faced by new generation mobile networks, such as 5G and forthcoming 6G, and introduce the concept of addressing these challenges through the development of a novel Distributed AI (DAI) framework. The book proposes a distributed AI approach to handle these complexities effectively.Distributed Artificial Intelligence for 5G/6G Communications: Frameworks with Machine Learning aims to provide a comprehensive understanding of the proposed DAI framework, its architecture, implementation, and application in the context of D2D communication in 5G and beyond networks. It showcases how the framework can enhance decision-making, control, and communication performance while considering both static and dynamic network environments. The book delves into the advantages of D2D communication, highlighting how it transcends licensed frequency bands and bypasses the cellular network, leading to improved network metrics such as spectral efficiency, energy efficiency, data rates, and interference management. It concludes by emphasizing that the DAI framework can offer enhanced network control, reduced signaling overhead, and efficient decision-making while capitalizing on existing implementations to tackle various challenges across the 5G and 6G landscape.This book is intended for professionals that specialize in designing, implementing, and maintaining communication networks such as telecommunications/ communication engineers, wireless engineers. The book's emphasis on incorporating AI and ML techniques in the proposed framework also makes it of interest to computer and software engineers working in artificial intelligence and machine learning engineers.
R 6,665.77
Advances in Intelligent Networking and Collaborative Systems
The aim of this book is to provide the latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to intelligent social networks and collaborative systems, intelligent networking systems, mobile collaborative systems, secure intelligent cloud systems, etc., and to reveal synergies among various paradigms in the multi-disciplinary field of intelligent collaborative systems. It presents the Proceedings of the 9th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2017), held on August 24–26, 2017 in Toronto, Canada.With the rapid evolution of the Internet, we are currently experiencing a shift from the traditional sharing of information and applications as the main purpose of the Web to an emergent paradigm that puts people at the very centre of networks and exploits the value of people’s connections, relations and collaborations. Social networks are also playing a major role in the dynamics and structure of intelligent Web-based networking and collaborative systems.Virtual campuses, virtual communities and organizations effectively leverage intelligent networking and collaborative systems by tapping into a broad range of formal and informal electronic relations, such as business-to-business, peer-to-peer and many types of online collaborative learning interactions, including the emerging e-learning systems. This has resulted in entangled systems that need to be managed efficiently and autonomously. In addition, the latest and powerful technologies based on Grid and wireless infrastructure as well as Cloud computing are now greatly enhancing collaborative and networking applications, but are also facing new issues and challenges. The principal objective of the research and development community is to stimulate research that leads to the creation of responsive environments for networking and, in the longer-term, the development of adaptive, secure, mobile, and intuitive intelligent systems for collaborative work and learning.
R 8,059.00
Intelligent Optimal Control for Distributed Industrial Systems
This book focuses on the distributed control and estimation of large-scale networked distributed systems and the approach of distributed model predictive and moving horizon estimation. Both principles and engineering practice have been addressed, with more weight placed on engineering practice. This is achieved by providing an in-depth study on several major topics such as the state estimation and control design for the networked system with considering time-delay, data-drop, etc., Distributed MPC design for improving the performance of the overall networked system, which includes several classic strategies for different scenarios, details of the application of the distributed model predictive control to smart grid system and distributed water network. The comprehensive and systematic treatment of theoretical and practical issues in distributed MPC for networked systems is one of the major features of the book, which is particularly suited for readers who are interested to learn practical solutions in distributed estimation and optimization of distributed networked systems. The book benefits researchers, engineers, and graduate students in the fields of chemical engineering, control theory and engineering, electrical and electronic engineering, chemical engineering, and computer engineering, etc.
R 7,326.00
Distributed Intelligent Circuits And Systems
The main objective of this book is to provide insights into recent advances in distributed intelligent circuits, systems and their applications. Distributed intelligence is the key enabler for innovations in machine-to-machine communications. The innovations are directed towards keeping existing algorithms as the base and developing new intelligent systems by employing smart technologies. Artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention in assisting doctors in the detection of disease patterns without much human intervention. In agriculture, robots automate slow, repetitive and dull tasks, allowing farmers to focus more on improving overall production yields.The evolving trends point to the interface of artificial intelligence with machines being a factor in enhancing the decision-making capabilities of smart machines. This book provides relevant theoretical frameworks that include basic models, algorithms, circuit designs and the latest developments in experimental aspects in the field of distributed intelligence systems for industrial applications. The challenges encountered in the development of models for distributed intelligence systems for environmental monitoring are mitigated with artificial intelligence, machine learning and deep learning. This book identifies challenges and helps in applying solutions in the development of advanced intelligent systems for environmental monitoring.
R 6,727.32
Distributed Artificial Intelligence: Theory and Praxis
Distributed AI is the branch of AI concerned with how to coordinate behavior among a collection of semi-autonomous problem-solving agents: how they can coordinate their knowledge, goals and plans to act together, to solve joint problems, or to make individually or globally rational decisions in the face of uncertainty and multiple, conflicting perspectives. Distributed, coordinated systems of problem solvers are rapidly becoming practical partners in critical human problem-solving environments, and DAI is a rapidly developing field of both application and research, experiencing explosive growth around the world. This book presents a collection of articles surveying several major recent developments in DAI. The book focuses on issues that arise in building practical DAI systems in real-world settings, and covers work undertaken in a number of major research and development projects in the U.S. and in Europe. It provides a synthesis of recent thinking, both theoretical and applied, on major problems of DAI in the 1990s.
R 4,645.17
Intelligent Networks and Intelligence in Networks
International Conference Intelligent Network and Intelligence in Networks (2IN97) French Ministry of Telecommunication, 20 Avenue de Segur, Paris -France September 2-5, 1997 Organizer: IFIP WG 6.7 -Intelligent Networks Sponsorship: IEEE, Alcatel, Ericsson, France Telecom, Nokia, Nordic Teleoperators, Siemens, Telecom Finland, Lab. PRiSM Aim of the conference To identify and study current issues related to the development of intelligent capabilities in networks. These issues include the development and distribution of services in broadband and mobile networks. This conference belongs to a series of IFIP conference on Intelligent Network. The first one took place in Lappeeranta August 94, the second one in Copenhagen, August 95. The proceedings of both events have been published by Chapman&Hall. IFIP Working Group 6.7 on IN has concentrated with the research and development of Intelligent Networks architectures. First the activities have concentrated in service creation, service management, database issues, feature interaction, IN performance and advanced signalling for broadband services. Later on the research activities have turned towards the distribution of intelligence in networks and IN applications to multimedia and mobility. The market issues of new services have also been studied. From the system development point of view, topics from OMG and TINA-C have been considered.
R 6,032.00
Contemporary Advancement in Evolutionary Multi-objective Optimization
This endeavour presents a rich collection of contemporary advances in evolutionary multi-objective optimisation algorithms, blending foundational concepts with state-of-the-art research and real-world applications. Featuring contributions from leading experts, it offers clear insights into modern EMO methodologies, emerging AI-EC integrations, and diverse problem-solving domains. An essential reference for researchers, practitioners, and students exploring intelligent optimisation Algorithms.
R 7,451.34
Distributed Economic Operation in Smart Grid: Model-Based and Model-Free Perspectives
This book aims to work out the distributed economic operation in smart grids in a systematic way, which ranges from model-based to model-free perspectives. The main contributions of this book can be summarized into three folds. First, we investigate the fundamental economic operation problems in smart grids from model-based perspective. Specifically, these problems can be modeled as deterministic optimization models, and we propose some distributed optimization algorithms by integrating the multi-agent consensus theory and optimization techniques to achieve the distributed coordination of various generation units and loads. Second, due to the randomness of the large-scale renewable energies and the flexibility of the loads, we further address these economic operation problems from a model-free perspective, and we propose learning-based approaches to address the uncertainty and randomness. At last, we extend the idea of model-based and model-free algorithms to plug-in electric vehicles (PEVs) charging/discharging scheduling problem, the key challenge of which involves multiple objectives simultaneously while the behavior of PEVs and the electricity price are intrinsically random. This book presents several recent theoretical findings on distributed economic operation in smart grids from model-based and model-free perspectives. By systematically integrating novel ideas, fresh insights, and rigorous results, this book provides a base for further theoretical research on distributed economic operation in smart grids. It can be a reference for graduates and researchers to study the operation and management in smart grids. Some prerequisites for reading this book include optimization theory, matrix theory, game theory, reinforcement learning, etc.
R 6,228.00
Network Games, Artificial Intelligence, Control and Optimization
This book LNCS 16173 constitutes the proceedings of the 12th International Conference on Network Games, Artificial Intelligence, Control and Optimization, NETGCOOP 2025, held in Bilbao, Spain, during October 8-10, 2025. The 16 full papers were carefully reviewed and selected from 23 submissions. These papers cover different areas of modeling, game theory, control and optimization, with a focus on scheduling and resource allocation in 5G/6G networks, matching in graphs as well as propagation, pricing and economic models, energy and generative artificial intelligence.
R 2,539.96
Optimization, Learning, and Control for Interdependent Complex Networks
This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.
R 2,015.00
Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference
This book offers the exchange of ideas between scientists and technicians from both the academic and industrial sector which is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The 18th International Symposium on Distributed Computing and Artificial Intelligence 2021 (DCAI 2021) is a forum to present the applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. The present edition brings together past experience, current work, and promising future trends associated with distributed computing, artificial intelligence, and their application in order to provide efficient solutions to real problems. This year’s technical program presents both high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 55 papers were submitted to main track and special sessions, by authors from 24 different countries, representing a truly “wide area network” of research activity. The DCAI’21 technical program has selected 21 papers, and, as in past editions, it will be special issues in ranked journals such as Electronics, Sensors, Systems, Robotics, Mathematical Biosciences and ADCAIJ. These special issues cover extended versions of the most highly regarded works. Moreover, DCAI'21 special sessions have been a very useful tool to complement the regular program with new or emerging topics of particular interest to the participating community.
R 7,340.93
