Edge Intelligence

Deep learning-enabled edge computing

By: Shajulin Benedict

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Hardcover | 20 June 2024

At a Glance

Hardcover
280 Pages

Dimensions(cm)
25.4 x 17.78 x 1.75

Hardcover

$253.00

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The promise of cloud and AI was to automate and speed innovation by driving actionable insight from data. However the unprecedented scale and complexity of data that's created by connected devices is outpacing network and infrastructure capabilities. Edge computing offers a solution as a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers. The proposed book would review and build on the popular techniques relating to edge intelligence and look at future potential and research direction in the field. In particular this book explores the techniques involved in edge computing, such as model compression, transfer learning, federated learning, energy efficiency aspects of applications, and so forth. It covers the edge-related applications that improve the economics of such applications, and it encompasses a few, mostly open-source, platforms and techniques that could be easily and readily incorporated by developers or researchers.

Key Features:

Each chapter includes learning objectives and summaries.

Author offers challenges to existing systems, and it will introduce ideas for future developments.

Inclusion of case studies.

Includes business opportunities and economic improvement potential offered by edge intelligence.

Showcases architectures and platforms that could be applied using edge intelligence to develop applications in allied technologies, such as smart mobility, drone developments, metaverse creations etc.

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