Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté : électronique
Auteur(s) : Prabhu, C. S. R
Titre(s) : Fog computing, deep learning and big data analytics-research directions [Texte électronique] / C.S.R. Prabhu
Publication : Singapore : Springer, copyright 2019
Description matérielle : 1 ressource dématérailisée
Note(s) : Bibliogr. p. 59-71
Sujet(s) : Apprentissage profond
Données massives
Informatique omniprésente
Indice(s) Dewey :
004.678 2 (23e éd.) = Infonuagique
Identifiants, prix et caractéristiques : ISBN 9789811332098. - ISBN 9811332096. - ISBN 9789811332081 (erroné). - ISBN 9811332088
(erroné)
Identifiant de la notice : ark:/12148/cb45779161g
Notice n° :
FRBNF45779161
(notice reprise d'un réservoir extérieur)
Table des matières : Intro; Preface; Contents; About the Author; Abstract; 1 Introduction; 1.1 A New Economy
Based on IoT Emerging from 2015; 1.1.1 Emergence of IoT; 1.1.2 Smart Cities and IoT;
1.1.3 Stages of IoT and Stakeholders; 1.1.4 Analytics; 1.1.5 Analytics from the Edge
to Cloud [179]; 1.1.6 Security and Privacy Issues and Challenges in the Internet of
Things (IoT); 1.1.7 Access; 1.1.8 Cost Reduction; 1.1.9 Opportunities and Business
Model; 1.1.10 Content and Semantics; 1.1.11 Data-Based Business Models Coming Out
of IoT; 1.1.12 Future of IoT; 1.1.13 Big Data Analytics and IoT
1.2 The Technological Challenges of an IoT-Driven Economy1.3 Fog Computing Paradigm
as a Solution; 1.4 Definitions of Fog Computing; 1.5 Characteristics of Fog Computing;
1.6 Architectures of Fog Computing; 1.6.1 Cloudlet Architecture [11]; 1.6.2 IoX Architecture;
1.6.3 Local Grid's Fog Computing Platform; 1.6.4 ParStream; 1.6.5 ParaDrop; 1.6.6
Prismatic Vortex; 1.7 Designing a Robust Fog Computing Platform; 1.8 Present Challenges
in Designing Fog Computing Platform; 1.9 Platform and Applications; 1.9.1 Components
of Fog Computing Platform; 1.9.2 Applications and Case Studies
2 Fog Application Management2.1 Introduction; 2.2 Application Management Approaches;
2.3 Performance; 2.4 Latency-Aware Application Management; 2.5 Distributed Application
Development in Fog; 2.6 Distributed Data Flow Approach; 2.6.1 Latency-Aware Fog Application
Management; 2.7 Resource Coordination Approaches; 3 Fog Analytics; 3.1 Introduction;
3.2 Fog Computing; 3.3 Stream Data Processing; 3.4 Stream Data Analytics, Big Data
Analytics and Fog Computing; 3.4.1 Machine Learning for Big Data, Stream Data and
Fog Ecosystem; 3.4.2 Deep Learning Techniques; 3.4.3 Deep Learning and Big Data
3.5 Different Approaches to Fog Analytics3.6 Comparison; 3.7 Cloud Solutions for the
Edge Analytics; 4 Fog Security and Privacy; 4.1 Introduction; 4.2 Authentication;
4.3 Privacy Issues; 4.4 User Behaviour Profiling; 4.5 Data Theft by Insider; 4.6 Man-in-the-Middle
Attack; 4.7 Failure Recovery and Backup Mechanisms; 5 Research Directions; 5.1 Harnessing
Temporal Dimension of IoT Data for Customer Relationship Management (CRM); 5.2 Adding
Semantics to IoT Data; 5.3 Towards a Semantic Web of IoT; 5.4 Diversity, Interoperability
and Standardization in IoT; 5.5 Data Management Issues in IoT
