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Proceedings of the International Symposium on Intelligent Computing and Networking 2024
International Symposium on Intelligent Computing and Networking 2024 (ISICN 2024), March 18-20, 2024; San Juan, Puerto Rico. Intelligent computing and networking is a rapidly growing area that has attracted significant attention due to its potential impact on the quality of service of applications for B5G/6G networks and the future Internet. To enable intelligent computing and networking, it is necessary to integrate technologies from the fields of communications, networking, computing, artificial intelligence, and numerous other fields. The scope of the symposium covers all enabling technologies for intelligent computing and networking. This book includes a huge variety of topics ranging from communications, computing, and networking to services and applications of artificial intelligence. Intended readership includes engineers and researchers in artificial intelligence, intelligent computing and networking areas. Targeted primary market will be professors, graduate students, and senior undergraduate students as well as research engineers. Targeted secondary market will be industry managers, consultants, and government research agencies in the related fields.
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Hybrid Optimization
Hybrid Optimization focuses on the application of artificial intelligence and operations research techniques to constraint programming for solving combinatorial optimization problems. This book covers the most relevant topics investigated in the last ten years by leading experts in the field, and speculates about future directions for research.This book includes contributions by experts from different but related areas of research including constraint programming, decision theory, operations research, SAT, artificial intelligence, as well as others. These diverse perspectives are actively combined and contrasted in order to evaluate their relative advantages.This volume presents techniques for hybrid modeling, integrated solving strategies including global constraints, decomposition techniques, use of relaxations, and search strategies including tree search local search and metaheuristics. Various applications of the techniques presented as well as supplementary computational tools are also discussed.
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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011
The purpose of the 12th Conference Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2011) held on July 6-8, 2011 in Sydney, Australia was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas and research results about all aspects (theory, applications and tools) of computer and information sciences, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected 14 outstanding papers from SNPD 2011, all of which you will find in this volume of Springer’s Studies in Computational Intelligence.
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Scalable Optimization via Probabilistic Modeling
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
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Learning and Intelligent Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Learning and Intelligent Optimization, LION 5, held in Rome, Italy, in January 2011.The 32 revised regular and 3 revised short papers were carefully reviewed and selected from a total of 99 submissions. In addition to the contributions to the general track there are 11 full papers and 3 short papers presented at the following four special sessions; IMON: Intelligent Multiobjective OptimizatioN, LION-PP: Performance Prediction Self* EAs: Self-tuning, self-configuring and self-generating evolutionary algorithms LION-SWAP: Software and Applications.
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Intelligent Distributed Computing IV
The emergent ?eld of Intelligent Distributed Computing brings together two wellestablishedareasofComputerScience:DistributedComputingandC- putational Intelligence. Theoretical foundations and practical applications of Intelligent Distributed Computing set the premises for the new generation of intelligent distributed information systems. Intelligent Distributed Computing - IDC Symposium Series was started as an initiative of research groups from: (i) Systems Research Institute, P- ish Academy of Sciences in Warsaw, Poland and (ii) Software Engineering Department of the University of Craiova, Craiova, Romania. IDC aims at bringing together researchers and practitioners involved in all aspects of - telligent Distributed Computing. IDC is interested in works that are relevant for both Distributed Computing and Computational Intelligence, with sci- ti?c contribution in at least one of these two areas. IDC'2010 was the fourth event in the series and was hosted by IEEE Morocco Section in Tangier, - rocco duringSeptember16-18,2010.IDC'2010wascollocatedwith:(i)NATO summer schoolon"SoftwareAgents, Agent Systems andtheir Applications"; nd (ii) 2 International Workshop on Multi-Agent Systems Technology and - mantics, MASTS'2010. The material published in this book is divided into three main parts: (i) 1 invited contribution; (ii) 28 contributions of IDC'2010 participants; and (iii) 4 contributions of MASTS'2010 participants. The response to IDC'2010 call for paper was generous. We received 53 submissions from 20 countries (we counted the country of each coauthor for each submitted paper). Each submission was carefully reviewed by at least 3 members of the IDC'2010 Program Committee.
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Advances in Intelligent Networking and Collaborative Systems
With the fast development of the Internet, we are experiencing a shift from the traditional sharing of information and applications as the main purpose of the Web to an emergent paradigm, which locates people at the very center of networks and exploits the value of people's connections, relations, and collaboration. 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 strongly leverage intelligent networking and collaborative systems by a great variety 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 in an autonomous way. In addition, latest and powerful technologies based on grid and wireless infrastructure as well as cloud computing are currently enhancing collaborative and networking applications a great deal but also facing new issues and challenges. The principal purpose of the research and development community is to stimulate research that will lead to the creation of responsive environments for networking and, at longer term, the development of adaptive, secure, mobile, and intuitive intelligent systems for collaborative work and learning.The aim of the book is to provide 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., as well as to reveal synergies among various paradigms in such a multi-disciplinary field intelligent collaborative systems.
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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2012
The purpose of the 13th International Conference on Computer and Information Science (SNPD 2012) held on August 8-10, 2012 in Kyoto, Japan was to bring together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. Our conference officers selected the best 17 papers from those papers accepted for presentation at the conference in order to publish them in this volume. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rounds of rigorous review. The conference organizers selected 17 outstanding papers from SNPD 2012, all of which you will find in this volume of Springer’s Studies in Computational Intelligence.
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Optimization and Multiobjective Control of Time-Discrete Systems
Richard Bellmann developed a theory of dynamic programming which is for many reasons still in the center of great interest. The authors present a new approach in the ?eld of the optimization and multi-objective control of time-discrete systems which is closely related to the work of Richard Bellmann. They develop their own concept and their extension to the optimization and multi-objective control of time-discrete systems as well as to dynamic networks and multilayered structures are very stimulating for further research. Di?erent perspectives of discrete control and optimal dynamic ?ow problems on networks are treated and characterized. Together with the algorithmic solutions a framework of multi-objective control problems is - rived. The conclusion with a real world example underlines the necessity and - portance of their theoretic framework. As they come back to the classical Bellmann concept of dynamic programming they stress and honor his basic concept without debase their own work. Multilayereddecisionprocessesaspartofthedesignandanalysisofcomplexsystems and networks will be essential in many ways and ?elds in the future.
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Planning and Operation of Active Distribution Networks
This book offers a broad and detailed view about how traditional distribution systems are evolving smart/active systems. The reader will be able to share the view of a number of researchers directly involved in this field. For this sake, philosophical discussions are enriched by the presentation of theoretical and computational tools. A senior reader may incorporate some concepts not available during his/her graduation process, whereas new Engineers may have contact with some material that may be essential to his/her practice as professionals.
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Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference
This book of LNNS contains accepted papers presented at DCAI conference in the Special Session on Intelligent Systems Applications (ISA), Guimarães (Portugal), July 2023. ISA is a major forum for presentation of development and applications of innovative techniques in the fields of building management, assistive technologies, the applied engineering, and industry. The scientific committee of the ISA has selected 8 full papers for its publication. This year ISA Special Session has teamed up with “Logic Journal of the IGPL” (Oxford University Press) for one special issue including selected papers from ISA Special Session 2023. We would like to thank all the contributing authors, the members of the Program Committee, the Organizing Committee of the Universities of Minho, and Salamanca for their hard and highly valuable work, which has been essential for the success of ISA 2023.
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Distributed Cooperative Control and Communication for Multi-agent Systems
This book investigates distributed cooperative control and communication of MASs including linear systems, nonlinear systems and multiple rigid body systems. The model-based and data-driven control method are employed to design the (optimal) cooperative control protocol.The approaches of this book consist of model-based and data-driven control such as predictive control, event-triggered control, optimal control, adaptive dynamic programming, etc.From this book, readers can learn about distributed cooperative control methods, data-driven control, finite-time stability analysis, cooperative attitude control of multiple rigid bodies. Some fundamental knowledge prepared to read this book is finite-time stability theory, event-triggered sampling mechanism, adaptive dynamic programming and optimal control.
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Data-Driven Systems and Intelligent Applications
This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.This book:Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.
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Intelligent Distributed Computing VI
This book represents the combined peer-reviewed proceedings of the Sixth International Symposium on Intelligent Distributed Computing -- IDC~2012, of the International Workshop on Agents for Cloud -- A4C~2012 and of the Fourth International Workshop on Multi-Agent Systems Technology and Semantics -- MASTS~2012. All the events were held in Calabria, Italy during September 24-26, 2012.The 37 contributions published in this book address many topics related to theory and applications of intelligent distributed computing and multi-agent systems, including: adaptive and autonomousdistributed systems, agent programming, ambient assistedliving systems, business process modeling and verification, cloudcomputing, coalition formation, decision support systems, distributed optimization and constraint satisfaction, gesture recognition, intelligent energy management in WSNs, intelligent logistics, machine learning, mobile agents, parallel and distributed computational intelligence, parallel evolutionary computing, trust metrics and security, scheduling in distributed heterogenous computing environments, semantic Web service composition, social simulation, and software agents for WSNs.
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Neural Networks and Artificial Intelligence
This book constitutes the refereed proceedings of the 8th International Conference on Neural Networks and Artificial Intelligence, ICNNAI 2014, held in Brest, Belarus, in June 2014. The 19 revised full papers presented were carefully reviewed and selected from 27 submissions. The papers are organized in topical sections on forest resource management; artificial intelligence by neural networks; optimization; classification; fuzzy approach; machine intelligence; analytical approach; mobile robot; real world application.
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Modern Computing
Modern Computing: Principles and Paradigms provides a comprehensive text and reference on modern computing, covering the most recent paradigms, systems and architectures that have revolutionised computational intelligence through the use of cutting-edge Artificial Intelligence of Things (AIoT) applications.The book presents state-of-the-art research on emerging areas of communications, networking and emerging technologies such as AIoT applications, Digital Twins, Edge AI, Industrial Internet of Things (IIoT). Advanced research findings (theoretical innovations and modelling applications) provide high-quality concepts, hybrid applications, innovative methodologies and development trend studies for a wide audience. It will enable readers to learn the fundamental science as well as the practical development of AI-driven next-generation modern computing.With particular emphasis the areas of AI, next generation computing and cloud computing, this book will be of great use to postgraduates and researchers across computer science and engineering, information technology, electronics and communication and data science.
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Distributed Linear Programming Models in a Smart Grid
This book showcases the strengths of Linear Programming models for Cyber Physical Systems (CPS), such as the Smart Grids. Cyber-Physical Systems (CPS) consist of computational components interconnected by computer networks that monitor and control switched physical entities interconnected by physical infrastructures. A fundamental challenge in the design and analysis of CPS is the lack of understanding in formulating constraints for complex networks. We address this challenge by employing collection of Linear programming solvers that models the constraints of sub-systems and micro grids in a distributed fashion.The book can be treated as a useful resource to adaptively schedule resource transfers between nodes in a smart power grid. In addition, the feasibility conditions and constraints outlined in the book will enable in reaching optimal values that can help maintain the stability of both the computer network and the physical systems. It details the collection of optimization methods that are reliable for electric-utilities to use for resource scheduling, and optimizing their existing systems or sub-systems. The authors answer to key questions on ways to optimally allocate resources during outages, and contingency cases (e.g., line failures, and/or circuit breaker failures), how to design de-centralized methods for carrying out tasks using decomposition models; and how to quantify un-certainty and make decisions in the event of grid failures.
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Bio-Inspired Collaborative Intelligent Control and Optimization
This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.
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Data-Driven Evolutionary Optimization
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
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Learning and Intelligent Optimization
This book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advancedresearch developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning.
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Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments
The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.
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Intelligent Computational Optimization in Engineering
We often come across computational optimization virtually in all branches of engineering and industry. Many engineering problems involve heuristic search and optimization, and, once discretized, may become combinatorial in nature, which gives rise to certain difficulties in terms of solution procedure. Some of these problems have enormous search spaces, are NP-hard and hence require heuristic solution techniques. Another difficulty is the lack of ability of classical solution techniques to determine appropriate optima of non-convex problems. Under these conditions, recent advances in computational optimization techniques have been shown to be advantageous and successful compared to classical approaches. This Volume presents some of the latest developments with a focus on the design of algorithms for computational optimization and their applications in practice. Through the chapters of this book, researchers and practitioners share their experience and newest methodologies with regard to intelligent optimization and provide various case studies of the application of intelligent optimization techniques in real-world applications.This book can serve as an excellent reference for researchers and graduate students in computer science, various engineering disciplines and the industry.
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Intelligent Systems and Networks
This book presents Proceedings of the International Conference on Intelligent Systems and Networks (ICISN 2021), held at Hanoi in Vietnam. It includes peer-reviewed high-quality articles on intelligent system and networks. It brings together professionals and researchers in the area and presents a platform for exchange of ideas and to foster future collaboration. The topics covered in this book include—foundations of computer science; computational intelligence language and speech processing; software engineering software development methods; wireless communications signal processing for communications; electronics track IoT and sensor systems embedded systems; etc.
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Evolutionary Computation in Combinatorial Optimization
This book constitutes the refereed proceedings of the 14th European Conference on Evolutionary Computation in Combinatorial Optimization, Evo COP 2014, held in Granada, Spain, in April 2014, co-located with the Evo*2014 events Euro GP, Evo BIO, Evo MUSART and Evo Applications.The 20 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers cover the following topics: swarm intelligence algorithms, fitness landscapes and adaptive algorithms, real world and routing problems and cooperative and metaheuristic search.
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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
This edited book presents the scientific outcomes of the 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2018), which was held in Busan, Korea on June 27–29, 2018. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The book includes research findings on all aspects (theory, applications and tools) of computer and information science and discusses the practical challenges encountered along the way and the solutions adopted to respond to them.The book includes 13 of the conference’s most promising papers.
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Soft Computing Applications in Optimization, Control, and Recognition
Soft computing includes several intelligent computing paradigms, like fuzzy logic, neural networks, and bio-inspired optimization algorithms. This book describes the application of soft computing techniques to intelligent control, pattern recognition, and optimization problems. The book is organized in four main parts. The first part deals with nature-inspired optimization methods and their applications. Papers included in this part propose new models for achieving intelligent optimization in different application areas. The second part discusses hybrid intelligent systems for achieving control. Papers included in this part make use of nature-inspired techniques, like evolutionary algorithms, fuzzy logic and neural networks, for the optimal design of intelligent controllers for different kind of applications. Papers in the third part focus on intelligent techniques for pattern recognition and propose new methods to solve complex pattern recognition problems. The fourth part discusses new theoretical concepts and methods for the application of soft computing to many different areas, such as natural language processing, clustering and optimization.
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Intelligent Computing and Optimization
This book of Springer Nature is another proof of Springer’s outstanding greatness on the lively interface of Holistic Computational Optimization, Green IoTs, Smart Modeling, and Deep Learning! It is a masterpiece of what our community of academics and experts can provide when an interconnected approach of joint, mutual, and meta-learning is supported by advanced operational research and experience of the World-Leader Springer Nature!The 6th edition of International Conference on Intelligent Computing and Optimization took place at G Hua Hin Resort & Mall on April 27–28, 2023, with tremendous support from the global research scholars across the planet. Objective is to celebrate “Research Novelty with Compassion and Wisdom” with researchers, scholars, experts, and investigators in Intelligent Computing and Optimization across the globe, to share knowledge, experience, and innovation—a marvelous opportunity for discourse and mutuality by novel research, invention, and creativity.This proceedings book of the 6th ICO’2023 is published by Springer Nature—Quality Label of Enlightenment.
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Distributed Machine Learning with Python
Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloudKey FeaturesAccelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesBook DescriptionReducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.What you will learnDeploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is forThis book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.
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Intelligent Computing Theories and Application
This two-volume set of LNCS 12836 and LNCS 12837 constitutes - in conjunction with the volume LNAI 12838 - the refereed proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021, held in Shenzhen, China in August 2021. The 192 full papers of the three proceedings volumes were carefully reviewed and selected from 458 submissions.The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” The papers are organized in the following subsections: Evolutionary Computation and Learning, Image and signal Processing, Information Security, Neural Networks, Pattern Recognition Swarm Intelligence and Optimization, and Virtual Reality and Human-Computer Interaction.
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Services Management in Intelligent Networks
This volume of the Lecture Notes in Computer Science series contains all the papersaccepted forpresentation atthe 11thIFIP/IEEEInternationalWorkshop on Distributed Systems: Operations & Management (DSOM 2000), which was held at the Thompson Conference Center, The University of Texas, Austin, Texas, USA, from 4 to 6 December, 2000. DSOM 2000 is the eleventh workshop in a series of annual workshops and it followsin the footsteps ofhighlysuccessful previous meetings, themost recent of whichwere held inZurich, Switzerland (DSOM'99), Delaware, USA (DSOM'98) andSydney, Australia(DSOM'97).The goalof the DSOMworkshops is tobring together researchers in the areas of network, systems and services management, from both industry and academia, to discuss recent advances and foster future growth in this e ld. In contrast to the larger management symposia, such as IM (Integrated Management) and NOMS (Network Operations and Management Symposium), the DSOM workshops are organized as single-track programs in order to stimulate interaction among participants. The speci c focus of DSOM 2000 is \Services Management in Intelligent Networks", re?ecting the current interest and development in the el d of d- tributed, networked application services, their de nition, operation and m- agement.Most of the papers presented at the workshop address some important aspect of this broad problem area. This year we have departed slightly from the traditional DSOM format. We have introduced two sessions devoted to the presentation and discussion of papers that describe work-in-progress.
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Artificial Intelligence and Internet of Things based Augmented Trends for Data Driven Systems
This book comprehensively discusses the role of cloud computing in artificial intelligence‑based data‑driven systems and hybrid cloud computing for large data‑driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‑based frameworks and advanced computing techniques to deal with online/virtual systems.This book:• Covers the aspects of security, authentication, and prediction for data‑driven systems in heterogeneous environments.• Provides data‑driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‑making for real‑time problems.• Showcases deep learning‑based computer vision algorithms for enhanced pattern detection in different domains based on data‑centric approaches.• Examines the role of the Internet of Things and machine learning algorithms for data‑driven systems.• Highlights the applications of data‑driven systems and cloud computing in enhancing network performance.This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering.
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Next Generation Intelligent Optical Networks
Optical networks have been in commercial deployment since the early 1980s as a result of advances in optical, photonic, and material technologies. Although the initial deployment was based on silica ?ber with a single wavelength modulated at low data rates, it was quickly demonstrated that ?ber can deliver much more bandwidth than any other transmission medium, twisted pair wire, coaxial cable, or wireless. Since then, the optical network evolved to include more exciting technologies, gratings, optical ?lters, optical multiplexers, and optical ampli?ers so that today a single ?ber can transport an unprecedented aggregate data rate that exceeds Tbps, and this is not the upper limit yet. Thus, the ?ber optic network has been the network of choice, and it is expected to remain so for many generationsto come, for both synchronousand asynchronouspayloads; voice, data, video, interactive video, games, music, text, and more. In the last few years, we have also witnessed an increase in network attacks as a result of store andforwardcomputer-basednodes. These attackshave manymaliciousobjectives:harvestsomeone else’s data, impersonate another user, cause denial of service, destroy ?les, and more. As a result, a new ?eld in communicationis becomingimportant,communicationnetworksand informationse- rity. In fact, the network architect and system designer is currently challenged to include enhanced features such as intruder detection, service restoration and countermeasures, intruder avoidance, and so on. In all, the next generation optical network is intelligent and able to detect and outsmart malicious intruders.
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Design, Implementation, and Analysis of Next Generation Optical Networks
By the end of the decade, approximately 50 billion devices will be connected over the internet using multiple services such as online gaming, ultra-high definition videos, and 5G mobile services. The associated data traffic demand in both fixed and mobile networks is increasing dramatically, causing network operators to have to migrate the existing optical networks towards next-generation solutions. The main challenge within this development stems from network operators having difficulties finding cost-effective next-generation optical network solutions that can match future high capacity demand in terms of data, reach, and the number of subscribers to support multiple network services on a common network infrastructure. Design, Implementation, and Analysis of Next Generation Optical Networks: Emerging Research and Opportunities is an essential reference source that discusses the next generation of high capacity passive optical access networks (PON) in terms of design, implementation, and analysis and offers a complete reference of technology solutions for next-generation optical networks. Featuring research on topics such as artificial intelligence, electromagnetic interface, and wireless communication, this book is ideally designed for researchers, engineers, scientists, and students interested in understanding, designing, and analyzing the next generation of optical networks.
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Distributed Computing and Networking
This book constitutes the fully refereed proceedings of the 9th International Conference on Distributed Computing and Networking, ICDCN 2008 - formerly known as IWDC (International Workshop on Distributed Computing), held in Kolkata, India, in January 2008. The 30 revised full papers and 27 revised short papers presented together with 3 keynote talks and 1 invited lecture were carefully reviewed and selected from 185 submissions. The papers are organized in topical sections.
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Network Models and Optimization
Network models are critical tools in business, management, science and industry. “Network Models and Optimization” presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. The book extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, traveling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. The book can be used both as a student textbook and as a professional reference for practitioners who use network optimization methods to model and solve problems.
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Introduction to Distributed Self-Stabilizing Algorithms
This book aims at being a comprehensive and pedagogical introduction to the concept of self-stabilization, introduced by Edsger Wybe Dijkstra in 1973. Self-stabilization characterizes the ability of a distributed algorithm to converge within finite time to a configuration from which its behavior is correct (i.e., satisfies a given specification), regardless the arbitrary initial configuration of the system. This arbitrary initial configuration may be the result of the occurrence of a finite number of transient faults. Hence, self-stabilization is actually considered as a versatile non-masking fault tolerance approach, since it recovers from the effect of any finite number of such faults in an unified manner. Another major interest of such an automatic recovery method comes from the difficulty of resetting malfunctioning devices in a large-scale (and so, geographically spread) distributed system (the Internet, Pair-to-Pair networks, and Delay Tolerant Networks are examples of such distributed systems). Furthermore, self-stabilization is usually recognized as a lightweight property to achieve fault tolerance as compared to other classical fault tolerance approaches. Indeed, the overhead, both in terms of time and space, of state-of-the-art self-stabilizing algorithms is commonly small. This makes self-stabilization very attractive for distributed systems equipped of processes with low computational and memory capabilities, such as wireless sensor networks.After more than 40 years of existence, self-stabilization is now sufficiently established as an important field of research in theoretical distributed computing to justify its teaching in advanced research-oriented graduate courses. This book is an initiation course, which consists of the formal definition of self-stabilization and its related concepts, followed by a deep review and study of classical (simple) algorithms, commonly used proof schemes and design patterns, as well as premium results issued from the self-stabilizing community. As often happens in the self-stabilizing area, in this book we focus on the proof of correctness and the analytical complexity of the studied distributed self-stabilizing algorithms.Finally, we underline that most of the algorithms studied in this book are actually dedicated to the high-level atomic-state model, which is the most commonly used computational model in the self-stabilizing area. However, in the last chapter, we present general techniques to achieve self-stabilization in the low-level message passing model, as well as example algorithms.
R 2,177.06
Distributed Cooperative Control and Communication for Multi-agent Systems
This book investigates distributed cooperative control and communication of MASs including linear systems, nonlinear systems and multiple rigid body systems. The model-based and data-driven control method are employed to design the (optimal) cooperative control protocol.The approaches of this book consist of model-based and data-driven control such as predictive control, event-triggered control, optimal control, adaptive dynamic programming, etc.From this book, readers can learn about distributed cooperative control methods, data-driven control, finite-time stability analysis, cooperative attitude control of multiple rigid bodies. Some fundamental knowledge prepared to read this book is finite-time stability theory, event-triggered sampling mechanism, adaptive dynamic programming and optimal control.
R 6,849.93
Recent Advances on Hybrid Approaches for Designing Intelligent Systems
This book describes recent advances on hybrid intelligent systems using soft computing techniques for diverse areas of application, such as intelligent control and robotics, pattern recognition, time series prediction and optimization complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of type-2 fuzzy logic, which basically consists of papers that propose new models and applications for type-2 fuzzy systems. The second part contains papers with the main theme of bio-inspired optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application. The third part contains papers that deal with new models and applications of neural networks in real world problems. The fourth part contains papers with the theme of intelligent optimization methods, which basically consider the proposal of new methods of optimization to solve complex real world optimization problems. The fifth part contains papers with the theme of evolutionary methods and intelligent computing, which are papers considering soft computing methods for applications related to diverse areas, such as natural language processing, recommending systems and optimization.
R 6,070.77
Nature Inspired Cooperative Strategies for Optimization (NICSO 2008)
The inspiration from Biology and the Natural Evolution process has become a research area within computer science. For instance, the description of the arti?cial neuron given by McCulloch and Pitts was inspired from biological observations of neural mechanisms; the power of evolution in nature in the diverse species that make up our world has been related to a particular form of problem solving based on the idea of survival of the ?ttest; similarly, - ti?cial immune systems, ant colony optimisation, automated self-assembling programming, membrane computing, etc. also have their roots in natural phenomena. The ?rst and second editions of the International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), were held in Granada, Spain, 2006, and in Acireale, Italy, 2007, respectively. As in these two previous editions, the aim of NICSO 2008, held in Tenerife, Spain, was to provide a forum were the latest ideas and state of the art research related to nature inspired cooperative strategies for problem solving were discussed. The contributions collected in this book were strictly peer reviewed by at least three members of the international programme committee, to whom we are indebted for their support and assistance. The topics covered by the contributionsincludenature-inspiredtechniqueslikeGeneticAlgorithms,Ant Colonies, Amorphous Computing, Arti?cial Immune Systems, Evolutionary Robotics, Evolvable Systems, Membrane Computing, Quantum Computing, Software Self Assembly, Swarm Intelligence, etc.
R 6,814.35
Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.
R 6,228.00
Network Optimization
This book constitutes the refereed proceedings of the 5th International Conference on Network Optimization, INOC 2011, held in Hamburg, Germany, in June 2011. The 65 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers highlight recent developments in network optimization and are organized in the following topical sections: theoretical problems, uncertainty, graph theory and network design; network flows; routing and transportation; and further optimization problems and applications (energy oriented network design, telecom applications, location, maritime shipping, and graph theory).
R 3,100.81
Distributed Cooperative Model Predictive Control of Networked Systems
This book is inspired by the development of distributed model predictive control of networked systems to save computation and communication sources. The significant new contribution is to show how to design efficient DMPCs that can be coordinated asynchronously with the increasing effectiveness of the event-triggering mechanism and how to improve the event-triggered DMPC for different requirements improvement of control performance, extension to interconnected networked systems, etc. The book is likely to be of interest to the persons who are engaged in researching control theory in academic institutes, the persons who go in for developing control systems in R&D institutes or companies, the control engineers who are engaged in the implementation of control algorithms, and people who are interested in the distributed MPC.
R 4,030.00
Advances in Bio-inspired Computing for Combinatorial Optimization Problems
"Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems.
R 4,679.44
Distributed Computing and Networking
people volunteer their time and energy and work in a dedicated fashion to pull everything together each year, including our very supportive Steering Comm- tee members led by Sukumar Ghosh. However, the success of ICDCN is mainly due to the hard work of all those people who submit papers and/or attend the conference. We thank you all. January 2009 Prasad Jayanti Andrew T. Campbell Message from the Technical Program Chairs Welcome to the proceedings of the 10thInternationalConferenceon Distributed Computing and Networking (ICDCN) 2009. As ICDCN celebrates its 10th - niversary,ithasbecomeanimportantforumfordisseminatingthelatestresearch results in distributed computing and networking. We received 179 submissions from all over the world, including Algeria, A- tralia, Canada, China, Egypt, France, Germany, Hong Kong, Iran, Italy, Japan, Malaysia, The Netherlands, Poland, Singapore, South Korea, Taiwan, and the USA, besides India, the host country. The submissions were read and evaluated by the Program Committee, which consisted of 25 members for the Distributed Computing Track and 28 members for the Networking Track, with the ad- tional help of external reviewers. The Program Committee selected 20 regular papers and 32 short papers for inclusion in the proceedings and presentation at the conference. We were fortunate to have several distinguished scientists as keynote speakers. Andrew Campbell (Dartmouth College, USA), Maurice Herlihy (Brown University, USA), and P. R. Kumar (University of of Illinois, Urbana-Champaign) delivered the keynote address. Krithi Ramamritham from IIT Bombay, India, delivered the A. K. Choudhury Memorial talk.
R 1,587.83
