Mobile Edge Artificial Intelligence

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

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  • Author : Yuanming Shi
  • Publisher : Academic Press
  • Pages : 206 pages
  • ISBN : 0128238356
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMobile Edge Artificial Intelligence

Mobile Edge Artificial Intelligence

Mobile Edge Artificial Intelligence
  • Author : Yuanming Shi,Kai Yang,Zhanpeng Yang,Yong Zhou
  • Publisher : Academic Press
  • Release : 07 August 2021
GET THIS BOOKMobile Edge Artificial Intelligence

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

Edge AI

Edge AI
  • Author : Xiaofei Wang,Yiwen Han,Victor C. M. Leung,Dusit Niyato,Xueqiang Yan,Xu Chen
  • Publisher : Springer Nature
  • Release : 31 August 2020
GET THIS BOOKEdge AI

As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and

Practical Deep Learning for Cloud, Mobile, and Edge

Practical Deep Learning for Cloud, Mobile, and Edge
  • Author : Anirudh Koul,Siddha Ganju,Meher Kasam
  • Publisher : "O'Reilly Media, Inc."
  • Release : 14 October 2019
GET THIS BOOKPractical Deep Learning for Cloud, Mobile, and Edge

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju,

Edge Intelligence in the Making

Edge Intelligence in the Making
  • Author : Sen Lin,Zhi Zhou,Zhaofeng Zhang,Xu Chen,Junshan Zhang
  • Publisher : Morgan & Claypool Publishers
  • Release : 21 October 2020
GET THIS BOOKEdge Intelligence in the Making

With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of

Mobile Edge Computing

Mobile Edge Computing
  • Author : Yan Zhang
  • Publisher : Springer Nature
  • Release : 01 October 2021
GET THIS BOOKMobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks.The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and

TinyML

TinyML
  • Author : Pete Warden,Daniel Situnayake
  • Publisher : O'Reilly Media
  • Release : 16 December 2019
GET THIS BOOKTinyML

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who

Machine Learning Approach for Cloud Data Analytics in IoT

Machine Learning Approach for Cloud Data Analytics in IoT
  • Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri
  • Publisher : John Wiley & Sons
  • Release : 27 July 2021
GET THIS BOOKMachine Learning Approach for Cloud Data Analytics in IoT

In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise some efficient mechanism to handle this huge data, thus necessitating efficient data handling techniques. Sustainable computing paradigms like cloud and fog are expedient to capably handle

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
  • Author : Christos Volos,Viet-Thanh Pham
  • Publisher : Academic Press
  • Release : 01 July 2021
GET THIS BOOKMem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types

Mobile Edge Computing

Mobile Edge Computing
  • Author : Yan Zhang
  • Publisher : Springer
  • Release : 02 October 2021
GET THIS BOOKMobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks.The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and

Computer Information Systems and Industrial Management

Computer Information Systems and Industrial Management
  • Author : Khalid Saeed,Rituparna Chaki,Valentina Janev
  • Publisher : Springer Nature
  • Release : 12 September 2019
GET THIS BOOKComputer Information Systems and Industrial Management

This book constitutes the proceedings of the 18th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2019, held in Belgrade, Serbia, in September 2019. The 43 full papers presented together with 3 abstracts of keynotes were carefully reviewed and selected from 70 submissions. The main topics covered by the chapters in this book are biometrics, security systems, multimedia, classification and clustering, industrial management. Besides these, the reader will find interesting papers on computer information systems as applied to wireless networks, computer graphics,

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning
  • Author : Nur Zincir-Heywood,Marco Mellia,Yixin Diao
  • Publisher : John Wiley & Sons
  • Release : 12 October 2021
GET THIS BOOKCommunication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss

Federated Learning Systems

Federated Learning Systems
  • Author : Muhammad Habib ur Rehman,Mohamed Medhat Gaber
  • Publisher : Springer Nature
  • Release : 25 January 2022
GET THIS BOOKFederated Learning Systems

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables

Artificial Intelligence for Communications and Networks

Artificial Intelligence for Communications and Networks
  • Author : Shuai Han,Liang Ye,Weixiao Meng
  • Publisher : Springer
  • Release : 04 July 2019
GET THIS BOOKArtificial Intelligence for Communications and Networks

This two-volume set LNICST 286-287 constitutes the post-conference proceedings of the First EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019, held in Harbin, China, in May 2019. The 93 full papers were carefully reviewed and selected from 152 submissions. The papers are organized in topical sections on artificial intelligence, mobile network, deep learning, machine learning, wireless communication, cognitive radio, internet of things, big data, communication system, pattern recognition, channel model, beamforming, signal processing, 5G, mobile management, resource management, wireless position.