Big Data Analytics for Internet of Things

por Chishti, Mohammad Ahsan; Saleem. Tausifa Jan
Big Data Analytics for Internet of Things
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ISBN: 978-1-119-74075-9
Editorial: Wiley & Sons Ltd.
Fecha de la edición: 2021
idioma: Ingles
Encuadernación:
Nº Pág.: 400

pvp.139.95 €

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Resumen del libro

Reseña: BIG DATA ANALYTICS FOR INTERNET OF THINGSDiscover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applicationsAn exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc.A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analyticsA treatment of machine learning techniques for IoT data analyticsPerfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.
indice: 1. Big Data Analytics for the Internet of Things: An Overview Tausifa Jan Saleem and Mohammad Ahsan Chishti 2. Data, Analytics and Interoperability between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3) Shoumen Palit Austin Datta, Tausifa Jan Saleem, Molood Barati, María Victoria López López, Marie-Laure Furgala, Diana C. Vanegas, Gérald Santucci and Eric S. McLamore 2.1. Context 2.2. Models In The Background 2.3. Problem Space: Are We Asking The Correct Questions? 2.4. Solutions Approach: Elusive Quest To Build Bridges Between Data & Decisions 2.5. Avoid This Space - The Deception Space 2.6. Solution Space - Necessary To Ask Questions That May Not Have Answers, Yet 2.7. Solution Economy - Will We Ever Get There? 2.8. Is This Faux Naïveté In Its Purest Distillate? 2.9. Reality Check - Data Fusion 2.10. 'Double A' Perspective Of Data And Tools Vs Porous Pareto (80/20) Partition 2.11. Conundrums 2.12. Stigma Of Partition Versus Astigmatism Of Vision 2.13. Illusion Of Data, Delusion Of Big Data And The Absence Of Intelligence In AI 2.14. In Service Of Society 2.15. Data Science In Service Of Society - Knowledge And Performance From Peas 2.16. Temporary Conclusion References Acknowledgements 3. Machine Learning Techniques for IoT Data Analytics Nailah Afshan and Ranjeet Kumar Rout 3.1. Introduction 3.2. Taxonomy of Machine Learning Algorithms 3.2.1. Supervised ML Algorithms 3.2.1.1. Classification 3.2.1.1.1. K Nearest Neighbours (K-NN) Algorithm 3.2.1.1.2. Naïve Bayes Classifier 3.2.1.2. Regression Analysis 3.2.1.2.1. Linear Regression 3.2.1.3. Classification and Regression Tasks 3.2.1.3.1. Support Vector Machine 3.2.1.3.2. Support Vector Regression 3.2.1.3.3. Classification and regression trees 3.2.1.3.4. Random forest 3.2.1.3.5. Bootstrap Aggregating 3.2.2. Unsupervised Machine Learning Algorithms 3.2.2.1. Clustering 3.2.2.1.1. K-Means Clustering 3.2.2.1.2. Density-based spatial clustering of applications with noise (DBSCAN) 3.2.2.1.3. Neural Networks 3.2.2.2. Feature Extraction 3.2.2.2.1. Principal Component Analysis (PCA) 3.2.2.2.2. Canonical Correlation Analysis (CCA) 3.3. Conclusion References 4. IoT Data Analytics using Cloud Computing Anjum Sheikh, Sunil Kumar and Asha Ambhaikar 4.1. Introduction 4.2. IoT Data Analytics 4.2.1. Process of IoT Analytics 4.2.2. Types of Analytics 4.3. Cloud Computing for IoT 4.3.1. Deployment Models for Cloud 4.3.2. Service Models for Cloud Computing 4.3.3. Data Analytics on Cloud 4.4. Cloud-Based IoT Data Analytics Platform 4.4.1. Atos Codex 4.4.2. AWS IoT 4.4.3. IBM Watson IoT 4.4.4. Hitachi Vantara Pentaho, Lumada 4.4.5. Microsoft Azure IoT 4.4.6. Oracle IoT Cloud Services 4.5. Machine Learning for IoT analytics in Cloud 4.5.1. ML algorithms for Data Analytics 4.5.2. Types of Predictions supported by ML and Cloud 4.6. Challenges for Analytics using Cloud 4.7. Conclusion References 5. Deep Learning Architectures for IoT Data Analytics Snowber Mushtaq and Omkar Singh 5.1. Introduction 5.1.1. Types of Learning Algorithms 5.1.2. Steps involved in solving a problem 5.1.2.1. Basic Terminology 5.1.2.2. Training Process 5.1.3. Modeling in Data Science 5.1.4. Why Deep Learning and IoT? 5.2. Deep Learning Architectures 5.2.1. Restricted Boltzmann Machine 5.2.1.1. Training Boltzmann Machine 5.2.1.2. Applications of Restricted Boltzmann Machine 5.2.2. Deep Belief Networks 5.2.2.1. Training Deep Belief Networks 5.2.2.2. Applications of Deep Belief Networks 5.2.3. Auto Encoders 5.2.3.1. Training of Auto Encoders 5.2.3.2. Applications of Auto Encoders 5.2.4. Convolutional Neural Networks 5.2.4.1. Layers of Convolution Neural Network 5.2.4.2. Activation functions used in Convolution Neural Networks 5.2.4.3. Applications of Convolution Neural Networks 5.2.5. Generative Adversarial Network 5.2.5.1. Training of Generative Adversarial Network 5.2.5.2. Applications of Generative Adversarial Network 5.2.6. Recurrent Neural Networks 5.2.6.1. Training of Recurrent Neural Networks 5.2.6.2. Applications of Recurrent Neural Networks 5.2.7. Long Short Term Memory 5.2.7.1. Training of Long Short Term Memory 5.2.7.2. Applications of Long Short Term Memory 5.3. Conclusion References 6. Adding Personal Touches to IoT: A User-Centric IoT Architecture Sarabjeet Kaur Kochhar 6.1. Introduction 6.2. Enabling Technologies for Big Data Analytics of IoT Systems 6.3. Personalizing the IoT 6.3.1. Personalization for Business 6.3.2. Personalization for Marketing 6.3.3. Personalization for product improvement and service optimization 6.3.4. Personalization for automated recommendations 6.3.5. Personalization for improved user experience. 6.4. Related Work 6.5. User sensitized IoT architecture 6.6. Concerns and Future Directions 6.7. Conclusion References 7. Smart Cities and the Internet of Things Hemant Garg, Sushil Gupta and Basant Garg 7.1. Introduction 7.2. Development of Smart Cities and the Internet of Things 7.3. The combination of the internet of things with cities to form smart cities 7.3.1. Unification of the Internet of Things 7.3.2. Security of Smart Cities 7.3.3. Management of water and related amenities 7.3.4. Power Distribution and Management 7.3.5. Revenue collection and administration 7.3.6. Management of City assets and Human Resources 7.3.7. Environmental pollution management 7.4. How future smart cities can improve use of internet of things 7.5. Conclusion References 8. A Roadmap for Application of IoT Generated Big Data in Environmental Sustainability Ankur Kashyap 8.1. Background and motivation 8.2. Execution of study 8.2.1. Role of Big Data in sustainability 8.2.2. Present status and future possibilities of IoT in environmental sustainability 8.3. Proposed roadmap 8.4. Identification & prioritizing the barriers in the process 8.4.1. Internet infrastructure 8.4.2. High hardware & software cost 8.4.3. Less qualified workforce 8.5. Conclusion and discussion 9. Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids CM Thasnimol and R. Rajathy 9.1. Introduction 9.2. Types of Application of Synchrophasor Data 9.2.1. Voltage Stability Analysis 9.2.2. Transient Stability 9.2.3. Out of Step Splitting Protection 9.2.4. Multiple Event Detection 9.2.5. State Estimation 9.2.6. Fault Detection 9.2.7. Loss of Main (LOM) Detection 9.2.8. Topology Update Detection 9.2.9. Oscillation Detection 9.3. Utility Big Data Issues Related to PMU Driven Applications 9.3.1. Heterogeneous Measurement Integration 9.3.2. Variety and Interoperability 9.3.3. Volume and Velocity 9.3.4. Data Quality and Security 9.3.5. Utilization and Analytics 9.3.6. Visualization of Data 9.4. Big Data Analytics Platforms for PMU Data Processing 9.4.1. Hadoop 9.4.2. Apache Spark 9.4.3. Apache Hbase 9.4.4. Apache Storm 9.4.5. Cloud-Based Platforms 9.5. Conclusion References 10. Intelligent enterprise-level big data analytics for modelling and management in smart internet of roads Amin Fadaeddini, Babak Majidi and Mohammad Eshghi 10.1. Introduction 10.2. Fully convolutional deep neural network for autonomous vehicle identification 10.3. Experimental setup and results 10.4. Practical implementation of enterprise level big data analytics for smart city 10.5. Conclusion References 11. Predictive analysis of intelligent sensing and cloud based integrated water management system Tanuja Patgar and Ripal Patel 11.1. Introduction 11.2. Literature Survey 11.3. Proposed Six tier Data Framework 11.3.1. Methodology 11.3.2. Proposed Algorithn 11.4. Implementation and Result Analysis 11.4.1. Water Report for Home1 and Home2 Module 11.5. Conclusion References 12. Data Security in the Internet-of-Things: Challenges and Opportunities Shashwati Banerjea, Shashank Srivastava and Sachin Kumar 12.1. Introduction 12.2. Internet-of-Things (IoT): Brief introduction 12.2.1. Challenges in a Secure IoT 12.2.2. Security Requirements in IoT Architecture 12.2.2.1. Sensing layer 12.2.2.2. Network Layer 12.2.2.3. Interface Layer 12.2.3. Common Attacks in IoT 12.3. IoT Security Classification 12.3.1. Application Domain 12.3.1.1. Authentication 12.3.1.2. Authorization 12.3.1.3. Depletion of Resources 12.3.1.4. Establishment of Trust 12.3.2. Architectural Domain 12.3.2.1. Authentication in IoT architecture 12.3.2.2. Authorization in IoT architecture 12.3.3. Communication channel 12.4. Security in IoT Data 12.4.1. IoT Data Security: Requirements 12.4.1.1. Data: Confidentiality Integrity Authentication 12.4.1.2. Data Privacy 12.4.2. IoT Data Security: Research Directions 12.5. Conclusion References 13. DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment Rup Kumar Deka, Dhruba Kumar Bhattacharyya and Jugal Kumar Kalita 13.1. Introduction 13.1.1. State of the Art 13.1.2. Contribution 13.1.3. Organization 13.2. Cloud and DDoS Attack 13.2.1. Cloud Deployment Models 13.2.1.1. Differences between Private Cloud and Public Cloud 13.2.2. DDoS Attacks 13.2.2.1. Attacks on Infrastructure level 13.2.2.2. Attacks on Application level 13.2.3. DoS/DDoS Attack on Cloud: Probable Impact 13.3. Mitigation Approaches 13.3.1. Discussion 13.4. Challenges and Issues with Recommendations 13.5. A Generic Framework 13.6. Conclusion and Future Work References 14. Securing the Defense Data for Making Better Decisions using Data Fusion Syed Rameem Zahra 14.1. Introduction 14.2. Analysis of Big Data 14.2.1. Existing IoT Big Data Analytics System 14.2.2. Big Data Analytical Methods 14.2.3. Challenges in IoT Big Data Analytics 14.3. Data Fusion 14.3.1. Opportunities provided by Data Fusion 14.3.2. Data Fusion Challenges 14.3.3. Stages at which Data Fusion can happen 14.3.4. Mathematical model for Data Fusion 14.4. Data Fusion for IoT Security 14.4.1. Defense Use Case 14.5. Conclusion References 15. New age Journalism and Big data (Understanding big data & its influence on Journalism) Asif Khan and Heeba Din 15.1. Introduction 15.1.1. Big Data Journalism: The Next Big Thing 15.1.2. All About Data 15.1.3. Accessing Data for Journalism 15.1.4. Data Analytics: Tool for Journalist 15.1.5. Case Studies-Big Data 15.1.5.1. BBC Big Data 15.1.5.2. The Guardian Data Blog 15.1.5.3. Wiki-leaks 15.1.5.4. World Economic Forum 15.1.6. Big Data-Indian Scenario 15.1.7. Internet of Things and Journalism 15.1.8. Impact on Media/Journalism References 16. Two decades of big data in finance: Systematic literature review and future research agenda Nufazil Ahangar 16.1. Introduction 16.2. Methodology 16.3. Article Identification and Selection 16.4. Description and Classification of Literature 16.4.1. Research Method Employed 16.4.2. Articles Published Year Wise 16.4.3. Journal of Publication 16.5. Content and Citation Analysis of articles 16.5.1. Citation Analysis 16.5.2. Content Analysis 16.5.2.1. Big Data in Financial Markets 16.5.2.2. Big Data in Internet Finance 16.5.2.3. Big Data in Financial Services 16.5.2.4. Big Data and other Financial Issues 16.6. Reporting of Findings and Research Gaps 16.6.1. Findings from literature review 16.6.1.1. Lack of Symmetry 16.6.1.2. Dominance of Research on financial Markets, Internet Finance and Financial Services 16.6.1.3. Dominance of Empirical Research 16.6.2. Directions for Future Research References

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