Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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High-Performance Networks for Multimedia Applications
High-Performance Networks for Multimedia Applications presents the latest research on the services and protocols for networks providing the communication support for distributed multimedia applications. The need for end-to-end QoS for these multimedia applications is raising the stakes for a powerful shaping and scheduling in the network adapter. It is also creating a need for new services at the ATM layer, CBR and VBR being augmented by UBR, ABR and GFR which have to be evaluated in the TCP/IP environment of today and tomorrow. With the pressure of all the new technologies available today, the backbone architecture needs to be revisited and the success of the TCP/IP must not eliminate the possibility of adding a native ATM access to it. Most of the research in communication services such as IntServ, DiffServ and Native ATM is driven by the requirements of multimedia systems and this book illustrates the new emphasis by bringing telecommunication and computer communication experts together with application designers. This is particularly true for the security issues also addressed here. Last but not least, modeling techniques and mathematical models are essential to assess the performance of the networks to be built and to evaluate next century scenarios unachievable by a simple scaling of today's solutions. High-Performance Networks for Multimedia Applications is a collection of high quality research papers and the in-depth treatment of the subjects provides interesting and innovative solutions. It is an essential reference for telecommunication and computer experts and QoS-based application designers. It is also a comprehensive text for graduate students in high-perforrnance networks and multimedia applications.
R 3,021.58
Networked Filtering and Fusion in Wireless Sensor Networks
By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. Networked Filtering and Fusion in Wireless Sensor Networks introduces the subject of multi-sensor fusion as the method of choice for implementing distributed systems.The book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case studies.Complete with research supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for WSNs.The book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help you broaden your understanding of prospective applications and how to address such problems in practice.After reading the book, you will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.
R 3,805.25
Dynamic Control and Optimization
This book contains the revised selected papers of the International Conference on Dynamic Monitoring and Optimization, DCO 2021, held in Aveiro, Portugal, February 3-5, 2021. The papers present achievements in the most challenging areas of dynamic control, optimization and related topics, including recent results in nonlinear dynamic control systems, calculus of variations, sub-Riemannian geometry, conventional differential equations, control of PDE evolution, stochastic differential equations, the spread of acoustic waves in elastic media, dynamics in space-time, Nondegenerate abnormality, controllability, and the infimum gap phenomena in optimization and optimal control with state constraints.
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System Modelling and Optimization
Proceedings volume contains carefully selected papers presented during the 17th IFIP Conference on System Modelling and Optimization. Optimization theory and practice, optimal control, system modelling, stochastic optimization, and technical and non-technical applications of the existing theory are among areas mostly addressed in the included papers. Main directions are treated in addition to several survey papers based on invited presentations of leading specialists in the respective fields. Publication provides state-of-the-art in the area of system theory and optimization and points out several new areas (e.g fuzzy set, neural nets), where classical optimization topics intersects with computer science methodology.
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Deep Reinforcement Learning for Wireless Networks
This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool.
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Constrained Global Optimization: Algorithms and Applications
Global optimization is concerned with the characterization and computation of global minima or maxima of nonlinear functions. Such problems are widespread in mathematical modeling of real world systems for a very broad range of applications. The applications include economies of scale, fixed charges, allocation and location problems, quadratic assignment and a number of other combinatorial optimization problems. More recently it has been shown that certain aspects of VLSI chip design and database problems can be formulated as constrained global optimization problems with a quadratic objective function. Although standard nonlinear programming algorithms will usually obtain a local minimum to the problem , such a local minimum will only be global when certain conditions are satisfied (such as f and K being convex).
R 1,532.85
Computational Intelligence in Wireless Sensor Networks
This book emphasizes the increasingly important role that Computational Intelligence (CI) methods are playing in solving a myriad of entangled Wireless Sensor Networks (WSN) related problems. The book serves as a guide for surveying several state-of-the-art WSN scenarios in which CI approaches have been employed. The reader finds in this book how CI has contributed to solve a wide range of challenging problems, ranging from balancing the cost and accuracy of heterogeneous sensor deployments to recovering from real-time sensor failures to detecting attacks launched by malicious sensor nodes and enacting CI-based security schemes. Network managers, industry experts, academicians and practitioners alike (mostly in computer engineering, computer science or applied mathematics) benefit from th e spectrum of successful applications reported in this book. Senior undergraduate or graduate students may discover in this book some problems well suited for their own research endeavors.
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Role of Population Games in the Design of Optimization-Based Controllers
This book reports on the implementation of evolutionary-game theory in the design of distributed optimization-based controllers. First, it discusses how the classical population-game approach can contribute to and complement the design of optimization-based controllers. It shows how the features of this approach can be exploited to extend their capabilities in the solution of distributed optimization problems, and examines density games in order to consider multiple coupled constraints and preserve the non-centralized information requirements. Furthermore, it establishes a close relationship between the possible interactions among agents in a population with constrained information sharing among different local controllers. It also discusses coalitional games, focusing on the Shapley power index and proposes an alternative method of computing the latter, which reduces computational time, as well as a different way of finding it using distributed communication structures. All the proposed strategies are then tested on various control problems, such as those related to the Barcelona water supply network, multiple continuous stirred tank reactors, various unmanned aerial vehicle systems, and a water distribution system. This thesis, examined at the Universitat Politècnica de Catalunya and Universidad de los Andes in 2017, received the award for best thesis in control from the control group of the Spanish Committee of Automatic Control (CEA) in the same year.
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Distributed Security Monitoring and Resilient Cooperative Control
This book systematically addresses the security and resilience of networked agent systems, from sensor networks to general multi-agent systems, against evolving cyber threats.It explores techniques for distributed security monitoring to detect sparse attacks on sensors (location-fixed/varying and sparse-fixed/varying), Byzantine node/link attacks, and vulnerability-related attacks in heterogeneous sensor networks. It provides advanced solutions for finite/fixed-time monitoring convergence under nonlinear dynamics. Regarding resilient cooperative control of multi-agent systems, the book tackles the challenge of isolation against various attacks, such as false data injection and covert, non-collusive, and collusive attacks. Practical applications range from resilient smart grids and attack-resistant drones to self-healing infrastructures. Rigorous mathematical frameworks, including graph theory and matrix analysis, establish a principled link between theoretical guarantees and the security operations of real-world networked agent systems. These frameworks offer tools to fortify critical networks against unpredictable, unstructured attacks.This book will appeal to scholars, engineers, and practitioners involved in systems engineering, control theory, cybersecurity, and artificial intelligence.
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Distributed Algorithms
This book constitutes the proceedings of the 9th International Workshop on Distributed Algorithms, WDAG '95, held in Le Mont-Saint-Michel, France in September 1995.Besides four invited contributions, 18 full revised research papers are presented, selected from a total of 48 submissions during a careful refereeing process. The papers document the progress achieved in the area since the predecessor workshop (LNCS 857); they are organized in sections on asynchronous systems, networks, shared memory, Byzantine failures, self-stabilization, and detection of properties.
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Artificial Intelligence Systems Based on Hybrid Neural Networks
This book is intended for specialists as well as students and graduate students in the field of artificial intelligence, robotics and information technology. It is will also appeal to a wide range of readers interested in expanding the functionality of artificial intelligence systems. One of the pressing problems of modern artificial intelligence systems is the development of integrated hybrid systems based on deep learning. Unfortunately, there is currently no universal methodology for developing topologies of hybrid neural networks (HNN) using deep learning. The development of such systems calls for the expansion of the use of neural networks (NS) for solving recognition, classification and optimization problems. As such, it is necessary to create a unified methodology for constructing HNN with a selection of models of artificial neurons that make up HNN, gradually increasing the complexity of their structure using hybrid learning algorithms.
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New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics
In this book, recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, are presented. In addition, the above-mentioned methods are applied to areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing techniques. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There also some papers that offer theoretical concepts and applications of meta-heuristics in different areas. Another group of papers describe diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical problems. There are also some papers that present theory and practice of neural networks in different areas of application. In addition, there are papers that present theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, there are some papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition and classification problems.
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Distributed Computer and Communication Networks
This book constitutes the refereed proceedings of the 22nd International Conference on Distributed and Computer and Communication Networks, DCCN 2019, held in Moscow, Russia, in September 2019.The 50 full papers and 2 short papers were carefully reviewed and selected from 174 submissions. The papers cover the following topics: Computer and Communication Networks and Technologies, Analytical Modeling of Distributed Systems, and Distributed Systems Applications.
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Distributed Computer and Communication Networks
This book constitutes the refereed proceedings of the 25th International Conference on Distributed Computer and Communication Networks, DCCN 2022, held in Moscow, Russia, in September 2022.The 27 full papers and 2 short papers included in this book were carefully reviewed and selected from 130 submissions. They were organized in topical sections as follows: Distributed Systems Applications, Computer and Communication Networks, Analytical Modeling of Distributed Systems.
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Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering
This book focuses on metaheuristic methods and its applications to real-world problems in Engineering. The first part describes some key metaheuristic methods, such as Bat Algorithms, Particle Swarm Optimization, Differential Evolution, and Particle Collision Algorithms. Improved versions of these methods and strategies for parameter tuning are also presented, both of which are essential for the practical use of these important computational tools. The second part then applies metaheuristics to problems, mainly in Civil, Mechanical, Chemical, Electrical, and Nuclear Engineering. Other methods, such as the Flower Pollination Algorithm, Symbiotic Organisms Search, Cross-Entropy Algorithm, Artificial Bee Colonies, Population-Based Incremental Learning, Cuckoo Search, and Genetic Algorithms, are also presented. The book is rounded out by recently developed strategies, or hybrid improved versions of existing methods, such as the Lightning Optimization Algorithm, Differential Evolution with Particle Collisions, and Ant Colony Optimization with Dispersion – state-of-the-art approaches for the application of computational intelligence to engineering problems.The wide variety of methods and applications, as well as the original results to problems of practical engineering interest, represent the primary differentiation and distinctive quality of this book. Furthermore, it gathers contributions by authors from four countries – some of which are the original proponents of the methods presented – and 18 research centers around the globe.
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Internet and Distributed Computing Systems
This book constitutes the refereed proceedings of the 6th International Conference on Internet and Distributed Computing Systems, IDCS 2013, held in Hangzhou, China, in October 2013.The 20 revised full papers and 13 invited papers presented were carefully reviewed and selected from numerous submissions. The papers cover the following topics: ad-hoc and sensor networks, internet and Web technologies, network operations and management, information infrastructure; resilience, as well as fault tolerance and availability.
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Accelerated Optimization for Machine Learning
This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
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Computational Optimization and Applications in Engineering and Industry
Contemporary design in engineering and industry relies heavily on computer simulation and efficient algorithms to reduce the cost and to maximize the performance and sustainability as well as profits and energy efficiency. Solving an optimization problem correctly and efficiently requires not only the right choice of optimization algorithms and simulation methods, but also the proper implementation and insight into the problem of interest. This book consists of ten self-contained, detailed case studies of real-world optimization problems, selected from a wide range of applications and contributed from worldwide experts who are working in these exciting areas. Optimization topics and applications include gas and water supply networks, oil field production optimization, microwave engineering, aerodynamic shape design, environmental emergence modelling, structural engineering, waveform design for radar and communication systems, parameter estimation in laser experiment and measurement, engineering materials and network scheduling. These case studies have been solved using a wide range of optimization techniques, including particle swarm optimization, genetic algorithms, artificial bee colony, harmony search, adaptive error control, derivative-free pattern search, surrogate-based optimization, variable-fidelity modelling, as well as various other methods and approaches. This book is a practical guide to help graduates and researchers to carry out optimization for real-world applications. More advanced readers will also find it a helpful reference and aide memoire.
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Distributed Computing
This book constitutes the refereed proceedings of the 4th International Workshop on Distributed Computing, IWDC 2002, held in Calcutta, India, in December 2002.The 31 revised full papers and 3 student papers presented together with 3 keynote papers were carefully reviewed and selected from more than 90 submissions. The papers are organized in topical sections on Web caching, distributed computing, wireless networks, wireless mobile systems, VLSI and parallel systems, optical networks, and distributed systems.
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Stochastic Global Optimization and Its Applications with Fuzzy Adaptive Simulated Annealing
Stochastic global optimization is a very important subject, that has applications in virtually all areas of science and technology. Therefore there is nothing more opportune than writing a book about a successful and mature algorithm that turned out to be a good tool in solving difficult problems. Here we present some techniques for solving several problems by means of Fuzzy Adaptive Simulated Annealing (Fuzzy ASA), a fuzzy-controlled version of ASA, and by ASA itself. ASA is a sophisticated global optimization algorithm that is based upon ideas of the simulated annealing paradigm, coded in the C programming language and developed to statistically find the best global fit of a nonlinear constrained, non-convex cost function over a multi-dimensional space. By presenting detailed examples of its application we want to stimulate the reader’s intuition and make the use of Fuzzy ASA (or regular ASA) easier for everyone wishing to use these tools to solve problems. We kept formal mathematical requirements to a minimum and focused on continuous problems, although ASA is able to handle discrete optimization tasks as well. This book can be used by researchers and practitioners in engineering and industry, in courses on optimization for advanced undergraduate and graduate levels, and also for self-study.
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Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks
SMART AND SUSTAINABLE APPROACHES FOR OPTIMIZING PERFORMANCE OF WIRELESS NETWORK Explores the intersection of sustainable growth, green computing and automation, and performance optimization of 5G wireless networks Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks explores how wireless sensing applications, green computing, and Big Data analytics can increase the energy efficiency and environmental sustainability of real-time applications across areas such as healthcare, agriculture, construction, and manufacturing. Bringing together an international team of expert contributors, this authoritative volume highlights the limitations of conventional technologies and provides methodologies and approaches for addressing Quality of Service (QOS) issues and optimizing network performance. In-depth chapters cover topics including blockchain-assisted secure data sharing, smart 5G Internet of Things (IoT) scenarios, intelligent management of ad hoc networks, and the use of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques in smart healthcare, smart manufacturing, and smart agriculture. Covers design, implementation, optimization, and sustainability of wireless and sensor-based networksDiscusses concepts of sustainability and green computing as well as their relevance to society and the environment Addresses green automation applications in various disciplines such as computer science, nanoscience, information technology (IT), and biochemistryExplores various smart and sustainable approaches for current wireless and sensor-based networksIncludes detailed case studies of current methodologies, applications, and implementations Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks: Real-time Applications is an essential resource for academic researchers and industry professionals working to integrate sustainable development and Information and Communications Technology (ICT).
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Robust and Online Large-Scale Optimization
Scheduled transportation networks give rise to very complex and large-scale networkoptimization problems requiring innovative solution techniques and ideas from mathematical optimization and theoretical computer science. Examples of scheduled transportation include bus, ferry, airline, and railway networks, with the latter being a prime application domain that provides a fair amount of the most complex and largest instances of such optimization problems. Scheduled transport optimization deals with planning and scheduling problems over several time horizons, and substantial progress has been made for strategic planning and scheduling problems in all transportation domains.This state-of-the-art survey presents the outcome of an open call for contributions asking for either research papers or state-of-the-art survey articles. We received 24 submissions that underwent two rounds of the standard peer-review process, out of which 18 were finally accepted for publication.The volume is organized in four parts: Robustness and Recoverability, Robust Timetabling and Route Planning, Robust Planning Under Scarce Resources, and Online Planning: Delay and Disruption Management.
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Real Time Convex Optimisation for 5G Networks and Beyond
There is no doubt that we are facing a wireless data explosion. Modern wireless networks need to satisfy increasing demand, but are faced with challenges such as limited spectrum, expensive resources, green communication requirements and security issues. In the age of internet of things (IoT) with massive data transfers and huge numbers of connected devices, including high-demand QoS (4G, 5G networks and beyond), signal processing is producing data sets at the gigabyte and terabyte scales. Modest-sized optimisation problems can be handled by online algorithms with fast speed processing and a huge amount of computer memory. With the rapid increase in powerful computers, more efficient algorithms and advanced parallel computing promise an enormous reduction in calculation time, solving modern optimisation problems on strict deadlines at microsecond or millisecond time scales. Finally, the interplay between machine learning and optimisation is an efficient and practical approach to optimisation in real-time applications. Real-time optimisation is becoming a reality in signal processing and wireless networks. This book considers advanced real-time optimisation methods for 5G and beyond networks. The authors discuss the fundamentals, technologies, practical questions and challenges around real-time optimisation of 5G and beyond communications, providing insights into relevant theories, models and techniques. The book should benefit a wide audience of researchers, practitioners, scientists, professors and advanced students in engineering, computer science, ubiquitous computing, information technology, and networking and communications engineering, as well as professionals in government agencies.
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Optimization Techniques for Solving Complex Problems
Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One—covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two—delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.
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Artificial Intelligence for Communications and Networks
This book constitutes the post-conference proceedings of the Second EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020, held in December 2020. Due to COVID-19 pandemic the conference was held virtually.The 52 full papers were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on Deep Learning/Machine Learning on Information and Signal Processing; AI in Ubiquitous Mobile Wireless Communications; AI in UAV-assisted wireless communications; Smart Education: Educational Change in the age of artificial Intelligence; AI in SAR/ISAR Target Detection; Recent advances in AI and their applications in future electronic and information field.
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New Solutions and Technologies in Electrical Distribution Networks
As the electrical industry continues to develop, one sector that still faces a range of concerns is the electrical distribution system. Excessive industrialization and inadequate billing are just a few issues that have plagued this electrical sector as it advances into the smart grid environment. Research is necessary to explore the possible solutions in fixing these problems and developing the distribution sector into an active and smart system.New Solutions and Technologies in Electrical Distribution Networks is a collection of innovative research on the methods and applications of solving major issues within the electrical distribution system. Some issues covered within the publication include distribution losses, improper monitoring of system, renewable energy integration with micro-grid and distributed energy sources, and smart home energy management system modelling. This book is ideally designed for power engineers, electrical engineers, energy professionals, developers, technologists, policymakers, researchers, academicians, industry professionals, and students seeking current research on improving this key sector of the electrical industry.
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Distributed Embedded Controller Development with Petri Nets
This book describes a model-based development approach for globally-asynchronous locally-synchronous distributed embedded controllers. This approach uses Petri nets as modeling formalism to create platform and network independent models supporting the use of design automation tools. To support this development approach, the Petri nets class in use is extended with time-domains and asynchronous-channels. The authors’ approach uses models not only providing a better understanding of the distributed controller and improving the communication among the stakeholders, but also to be ready to support the entire lifecycle, including the simulation, the verification (using model-checking tools), the implementation (relying on automatic code generators), and the deployment of the distributed controller into specific platforms.Uses a graphical and intuitive modeling formalism supported by design automation tools;Enables verification, ensuring that the distributed controller was correctly specified;Provides flexibility in the implementation and maintenance phases to achieve desired constraints (high performance, low power consumption, reduced costs), enabling porting to different platforms using different communication nodes, without changing the underlying behavioral model.
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Handbook Of Machine Learning - Volume 2: Optimization And Decision Making
Building on , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.
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Next Generation Smart Grids: Modeling, Control and Optimization
This book is a collection of chapters describing the advanced and future aspects of smart grid technology. The book emphasizes technical issues, theoretical background and practical applications that drive postgraduates, researchers and practicing engineers with the right advanced skills, vision and knowledge who will further be capable of leading in teams involved in the modelling, control, design, and optimization of the future smart grids. This feature strengthens the benefits of the book for the readers who will gain an insightful understanding of future smart grid challenges including: (i) the formulation of decision-making models, (ii) the familiarization with efficient solution algorithms for such models and (iii) insights into these problems through the detailed analysis of numerous illustrative examples. Further the chapters in this book provide comprehensive coverage of modelling, control and optimization of smart grid which are quite different from most technical publications.
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Aggregation in Large-Scale Optimization
When analyzing systems with a large number of parameters, the dimen sion of the original system may present insurmountable difficulties for the analysis. It may then be convenient to reformulate the original system in terms of substantially fewer aggregated variables, or macrovariables. In other words, an original system with an n-dimensional vector of states is reformulated as a system with a vector of dimension much less than n. The aggregated variables are either readily defined and processed, or the aggregated system may be considered as an approximate model for the orig inal system. In the latter case, the operation of the original system can be exhaustively analyzed within the framework of the aggregated model, and one faces the problems of defining the rules for introducing macrovariables, specifying loss of information and accuracy, recovering original variables from aggregates, etc. We consider also in detail the so-called iterative aggregation approach. It constructs an iterative process, at· every step of which a macroproblem is solved that is simpler than the original problem because of its lower dimension. Aggregation weights are then updated, and the procedure passes to the next step. Macrovariables are commonly used in coordinating problems of hierarchical optimization.
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Intelligent Computing, Smart Communication and Network Technologies
This book constitutes the refereed proceedings of the First International Conference on Intelligent Computing, Smart Communication and Network Technologies, ICICSCNT 2023, held in Chennai, India, during April 26–27, 2023.The 36 full papers included in this book were carefully reviewed and selected from 782 submissions. The main aim of ICICSCNT 2023 is to present the state-of-the-art research innovations and results in the area of intelligent systems and advanced control technologies and real-world applications in ICT research, particularly covering autonomous systems, artificial intelligence, sensing, hardware and software implementation, soft computing, optimization, intelligent control design, intelligent system integration and control applications, and other relevant research areas.
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Distributed Computing Through Combinatorial Topology
Distributed Computing Through Combinatorial Topology describes techniques for analyzing distributed algorithms based on award winning combinatorial topology research. The authors present a solid theoretical foundation relevant to many real systems reliant on parallelism with unpredictable delays, such as multicore microprocessors, wireless networks, distributed systems, and Internet protocols. Today, a new student or researcher must assemble a collection of scattered conference publications, which are typically terse and commonly use different notations and terminologies. This book provides a self-contained explanation of the mathematics to readers with computer science backgrounds, as well as explaining computer science concepts to readers with backgrounds in applied mathematics. The first section presents mathematical notions and models, including message passing and shared-memory systems, failures, and timing models. The next section presents core concepts in two chapters each: first, proving a simple result that lends itself to examples and pictures that will build up readers' intuition; then generalizing the concept to prove a more sophisticated result. The overall result weaves together and develops the basic concepts of the field, presenting them in a gradual and intuitively appealing way. The book's final section discusses advanced topics typically found in a graduate-level course for those who wish to explore further.
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Stochastic Adaptive Search for Global Optimization
The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algo rithms, are gaining in popularity among practitioners and engineers be they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well under stood. In this book, an attempt is made to describe the theoretical prop erties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and de velopment of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical anal ysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods.
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Distributed Decision Making
Distributed decision making (DDM) has become of increasing importance in quantitative decision analysis. In applications like supply chain management, service operations, or managerial accounting, DDM has led to a paradigm shift. The book provides a unified approach to such seemingly diverse fields as multi-level stochastic programming, hierarchical production planning, principal agent theory, negotiations or contract theory. Different settings like multi-level one-person decision problems, multi-person antagonistic planning, and leadership situations are covered. Numerous examples and real-life planning cases illustrate the concepts. The new edition has been considerably expanded by additional chapters on supply chain management, service operations and multi-agent systems.
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Intelligent Computing on IoT 2.0, Big Data Analytics, and Block Chain Technology
The book is designed as a reference text and explores the concepts and techniques of IoT, artificial intelligence (AI), and blockchain. It also discusses the possibility of applying blockchain for providing security in various domains. The specific highlight of this book is focused on the application of integrated technologies in enhancing data models, better insights and discovery, intelligent predictions, smarter finance, smart retail, global verification, transparent governance, and innovative audit systems. The book discusses the potential of blockchain to significantly increase data while boosting accuracy and integrity in IoT-generated data and AI-processed information.It elucidates definitions, concepts, theories, and assumptions involved in smart contracts and distributed ledgers related to IoT systems and AI approaches.The book offers real-world uses of blockchain technologies in different IoT systems and further studies its influence in supply chains and logistics, the automotive industry, smart homes, the pharmaceutical industry, agriculture, and other areas.It also presents readers with ways of employing blockchain in IoT and AI, helping them to understand what they can and cannot do with blockchain.The book is aimed primarily at advanced undergraduates and graduates studying computer science, computer engineering, electrical engineering, information systems, computational sciences, artificial intelligence, and information technology. Researchers and professionals will also find this book very useful.
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Distributed Autonomous Robotic Systems 3
Distributed autonomous robotic systems (DARS) are systems composed of multiple autonomous units such as modules, cells, processors, agents, and robots. Combination or cooperative operation of multiple autonomous units is expected to lead to desirable features such as flexibility, fault tolerance, and efficiency. The DARS is the leading established conference on distributed autonomous systems. All papers have the common goal to contribute solutions to the very demanding task of designing distributed systems to realize robust and intelligent robotic systems.
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Industrial Networks and Intelligent Systems
This book constitutes the refereed proceedings of the 9th EAI International Conference on Industrial Networks and Intelligent Systems, INISCOM 2023, held in Ho-Chi-Minh City, Vietnam, during August 2-3, 2023. The 23 full papers were selected from 55 submissions and are organized thematically in tracks on telecommunications systems and networks; information processing and data analysis; industrial networks and intelligent systems; security and privacy.
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Smart Communications, Intelligent Algorithms and Interactive Methods
This book gathers selected papers presented at the 4th International Conference on Wireless Communications and Applications (ICWCA 2020), held at Hainan University, China. The first volume of the proceedings will focus on the newest methods and algorithms in smart wireless communications in the areas of multimedia communications over wireless; smart antenna and space-time signal processing; antenna, wireless propagation, and channel modeling; OFDM and multi-carrier techniques; localization and navigation techniques; software-defined networking (SDN) and network function virtualization (NFV); knowledge-defined networking (KDN) and the applications of artificial intelligence (AI) in future networks; future data-center networks; resource allocation and orchestration in future networks and many others.
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Big Data Optimization: Recent Developments and Challenges
The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
R 7,326.00
Connecting Intelligence
ICCIDC 2025 is a premier global event focused on the latest advancements in computational intelligence and data communication technologies. ICCIDC 2025 will cover a wide range of topics including artificial intelligence, machine learning, data mining, signal processing, network protocols, wireless communication, and data security, among others. With keynote addresses, technical sessions, workshops, and networking opportunities, the conference provides an excellent platform for participants to exchange ideas and showcase their groundbreaking research. As an inaugural event, ICCIDC 2025 is poised to set the stage for future discussions and collaborations that will drive the evolution of computational intelligence and data communication, paving the way for next-generation innovations. Unlike general AI and data communication conferences, ICCIDC 2025 emphasizes the synergistic integration of computational intelligence and data communication to solve real-world problems. The conference aims to bridge gaps between AI-driven computation, networking, and emerging communication paradigms. Many conferences focus exclusively on AI or networking, but ICCIDC 2025 promotes a multi-disciplinary discussion covering: computational intelligence (machine learning, deep learning, soft computing); Future communication systems (5G/6G, IoT, edge computing, blockchain and Real-world applications (healthcare, cybersecurity, smart cities, fintech).
R 3,254.39
Evolutionary Multi-Criterion Optimization
This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 held in Guimarães, Portugal in March/April 2015. The 68 revised full papers presented together with 4 plenary talks were carefully reviewed and selected from 90 submissions. The EMO 2015 aims to continue these type of developments, being the papers presented focused in: theoretical aspects, algorithms development, many-objectives optimization, robustness and optimization under uncertainty, performance indicators, multiple criteria decision making and real-world applications.
R 2,201.21
Data-Driven Intelligence in Wireless Networks
This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence.This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
R 6,667.09
