AI-Based Advanced Optimization Techniques for Edge Computing PDF Download

Author: Mohit Kumar
Publisher: Wiley
ISBN: 9781394287031
Category : Computers
Languages : en
Pages : 0

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Book Description

Author: Yuanming Shi
Publisher: Academic Press
ISBN: 0128238356
Category : Computers
Languages : en
Pages : 208

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Book Description
Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources. Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning

Author: Souvik Pal
Publisher: Springer Nature
ISBN: 3031583884
Category :
Languages : en
Pages : 392

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Author: Rajest, S. Suman
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 618

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Book Description
The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smart homes, smart grids, and the IoT, among many other intelligent applications. Intelligent networks are founded on integrating caching and multi-agent systems that optimize data storage and the entire device’s learning process. However, a central node through which all agents transmit status messages and reward information is a major drawback of this design pattern. This central node condition instigates more communication overhead, potential data leakage, and the birth of data islands. To reverse this trend, using distributed optimization techniques and methodologies in cache-enabled multi-agent learning environments is increasingly beneficial. Advancing Intelligent Networks Through Distributed Optimization explains the current race for sophisticated and accurate distributed optimization in cache-enabled intelligent IoT networks given the need to make multi-agent learning converge faster and reduce communication overhead. These techniques will require innovative resource allocation strategies stretching from system training to caching, communication, and processing amongst millions of agents. This book combines the key recent research in these races into a single binder that can serve all the interested theoretical and practical scholars. The book focuses broadly on intelligent systems’ optimization trends. It identifies the various applications of advanced distributed optimization from manufacturing to medicine, agriculture and smart cities.

Author: H S Madhusudhan
Publisher: CRC Press
ISBN: 1040112641
Category : Computers
Languages : en
Pages : 263

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Book Description
This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques. Focuses on virtual machine placement and migration techniques for cloud data centers Presents the role of machine learning and metaheuristic approaches for optimisation in cloud computing services Includes application of placement techniques for quality of service, performance, and reliability improvement Explores data center resource management, load balancing and orchestration using machine learning techniques Analyses dynamic and scalable resource scheduling with a focus on resource management The text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology.

Author: Rob Botwright
Publisher: Rob Botwright
ISBN: 1839386738
Category : Computers
Languages : en
Pages : 176

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Book Description
Introducing "Edge Computing 101: Novice to Pro" - your ultimate guide to mastering edge computing from beginner to expert level. This comprehensive book bundle includes four essential volumes packed with expert techniques and practical applications to propel your understanding and proficiency in edge computing. Book 1, "Edge Computing Fundamentals: A Beginner's Guide to Distributed Systems," provides a solid foundation for understanding the core principles of distributed systems. Whether you're new to the field or seeking to reinforce your knowledge, this book equips you with the fundamental concepts necessary to embark on your journey into the world of edge computing. In Book 2, "Edge Computing Architectures: Design Principles and Best Practices," you'll delve deeper into the intricacies of designing effective edge computing architectures. From deployment models to optimization techniques, this volume offers invaluable insights and best practices to help you build robust and scalable edge systems. For those looking to advance their skills, Book 3, "Advanced Edge Computing: Scalability, Security, and Optimization Strategies," explores advanced techniques and strategies for overcoming scalability challenges, enhancing security measures, and optimizing performance in edge environments. With real-world examples and case studies, you'll gain practical expertise to tackle complex issues and achieve optimal outcomes. Finally, Book 4, "Edge Computing in Industry 4.0: Practical Applications and Future Trends," focuses on the practical applications of edge computing across various industries, with a special emphasis on Industry 4.0. Discover how edge computing is transforming manufacturing, healthcare, smart cities, and more, and gain insights into future trends shaping the industry. Whether you're a novice looking to build a strong foundation or a seasoned professional aiming to stay ahead of the curve, "Edge Computing 101: Novice to Pro" has you covered. With its comprehensive coverage, expert guidance, and practical insights, this book bundle is your essential companion for mastering the dynamic world of edge computing. Get your copy today and take your edge computing skills to the next level!

Author: Archana Mire
Publisher: John Wiley & Sons
ISBN: 1119791758
Category : Computers
Languages : en
Pages : 436

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Book Description
Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

Author: Krishan Arora
Publisher: CRC Press
ISBN: 1000904873
Category : Computers
Languages : en
Pages : 228

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Book Description
Modern Computational Techniques for Engineering Applications presents recent computational techniques used in the advancement of modern grids with the integration of non-conventional energy sources like wind and solar energy. It covers data analytics tools for smart cities, smart towns, and smart computing for sustainable development. This book- Discusses the importance of renewable energy source applications wind turbines and solar panels for electrical grids. Presents optimization-based computing techniques like fuzzy logic, neural networks, and genetic algorithms that enhance the computational speed. Showcases cloud computing tools and methodologies such as cybersecurity testbeds and data security for better accuracy of data. Covers novel concepts on artificial neural networks, fuzzy systems, machine learning, and artificial intelligence techniques. Highlights application-based case studies including cloud computing, optimization methods, and the Industrial Internet of Things. The book comprehensively introduces modern computational techniques, starting from basic tools to highly advanced procedures, and their applications. It further highlights artificial neural networks, fuzzy systems, machine learning, and artificial intelligence techniques and how they form the basis for algorithms. It presents application-based case studies on cloud computing, optimization methods, blockchain technology, fog and edge computing, and the Industrial Internet of Things. It will be a valuable resource for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical engineering, electronics and communications engineering, and computer engineering.

Author: Ahmed A. Abd El-Latif
Publisher: Springer Nature
ISBN: 3031510976
Category :
Languages : en
Pages : 259

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Book Description

Author: Rohit Anand
Publisher: John Wiley & Sons
ISBN: 1394188080
Category : Computers
Languages : en
Pages : 469

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Book Description
META-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systems—generally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed systems will find this book indispensable. Meta-Heuristic Algorithms for Advanced Distributed Systems readers will also find: Analysis of security issues, distributed system design, stochastic optimization techniques, and more Detailed discussion of meta-heuristic techniques such as the genetic algorithm, particle swam optimization, and many others Applications of optimized distribution systems in healthcare and other key??industries Meta-Heuristic Algorithms for Advanced Distributed Systems is ideal for academics and researchers studying distributed systems, their design, and their applications.