Empowering IoT with Big Data Analytics.
| Other author | Serhani, Mohamed Adel. |
| Other author | Xu, Yang. |
| Other author | Maamar, Zakaria. |
| Format | Electronic |
| Publication | London, United Kingdom ; New York, NY : Elsevier Science & Technology, 2024. |
| Description | 1 online resource (392 pages). |
| Supplemental Content | Click here to view book |
| Subjects |
| Series | Intelligent Data-Centric Systems Intelligent data centric systems. ^A1334749 |
| Contents | Intro -- Empowering IoT with Big Data Analytics -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Introduction to empowering IoT with big data analytics -- 1. Introduction -- 2. Understanding IoT and big data analytics -- 2.1. Fundamentals of big data analytics and its intersection with IoT -- 2.2. Conceptual framework of IoT-enabled big data analytics -- 3. Challenges in empowering IoT with big data analytics -- 3.1. Data management and storage challenges -- 3.2. Security and privacy challenges -- 3.3. Scalability challenges -- 3.4. Integration complexities challenges |
| Contents | 3.5. Regulatory and compliance challenges -- 4. Opportunities for empowering IoT with big data analytics -- 5. The way ahead -- References -- Chapter 2: Big data analytics for IoT: Technologies, importance, and algorithms -- 1. Introduction to IoT big data analytics -- 2. Technologies enabling IoT big data analytics -- 2.1. Data storage suitable for IoT big data analytics -- 2.1.1. Distributed file systems -- 2.1.2. Database management systems (DBMSs) -- 2.2. Computing architectures and paradigms suitable for IoT big data analytics -- 2.2.1. Cloud-based processing platforms |
| Contents | 2.2.2. Edge-based processing platforms -- 2.2.3. Hybrid Cloud/Edge processing architectures -- 3. Importance of IoT big data analytics -- 3.1. Data exploration and visualization -- 3.2. Real-time and stream processing -- 3.3. Contextualization and enrichment -- 4. IoT big data analytics algorithms and applications -- 4.1. IoT data stream mining approaches -- 4.1.1. Real-time data analysis -- 4.1.2. Data stream classification -- 4.1.3. Data stream clustering -- 4.1.4. Concept drift detection algorithms -- 4.1.5. Anomaly detection -- 4.2. IoT data batch processing -- 4.2.1. MapReduce |
| Contents | 4.2.2. Apache Hadoop and Spark -- 4.2.3. Apache Pig and Hive -- 4.2.4. TensorFlow -- 5. Summary and future trends/challenges -- References -- Chapter 3: Machine learning for sensory data analytics**This work was supported by the Key Research and Development Proje ... -- 1. Deep learning models for sensory data analytics -- 1.1. Convolutional neural networks -- 1.1.1. CNN architecture -- 1.1.2. Applications of CNNs in IoT sensory data analysis -- 1.2. Recurrent neural networks -- 2. Deep reinforcement learning for IoT data -- 2.1. DRL fundamentals -- 2.2. Value-based methods |
| Contents | 2.3. Policy-based methods -- 2.4. Actor-critic methods -- 3. Distributed machine learning for big data -- 3.1. Introduction -- 3.2. Overview and challenges of DML -- 3.2.1. Overview -- 3.2.2. Challenges -- 3.3. Communication models -- 3.4. Communication methods -- 3.5. Optimization algorithms -- 3.6. Network protocols -- 4. Conclusion -- References -- Chapter 4: Applications of big data analytics in IoT -- 1. Introduction -- 1.1. Purpose and scope -- 2. Sustainable solutions for smart cities-based IoT -- 2.1. The power of big data in smart cities |
| General note | 2.2. Big data analytics: Unveiling hidden insights |
| Issued in other form | Print version: Serhani, Mohamed Adel Empowering IoT with Big Data Analytics Chantilly : Elsevier Science & Technology,c2024 9780443216404 |
| ISBN | 9780443216411 electronic book |
| ISBN | 044321641X electronic book |