Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xiii 6. Preface - p. xvii 7. Chapter 1: Internet of Things in healthcare: Smart devices, sensors, and systems related to diseases and health conditions - p. xviii 8. Chapter 2: Real-time data analytics in healthcare using the Internet of Things - p. 35 9. Chapter 3: Lightweight code self-verification using return-oriented programming in resilient IoT - p. 50 10. Chapter 4: Monte-Carlo Simulation models for reliability analysis of low-cost IoT communication networks in smart grid - p. 71 11. Chapter 5: Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT - p. 96 12. Chapter 6: Soft sensor with shape descriptors for flame quality prediction based on LSTM regression - p. 114 13. Chapter 7: Communication-aware edge-centric knowledge dissemination in edge computing environments - p. 138 14. Chapter 8: An effective blockchain-based, decentralized application for smart building system management - p. 156 15. Chapter 9: Privacy and security of Internet of Things devices - p. 181 16. Chapter 10: Software-Defined Networking for the Internet of Things: Securing home networks using SDN - p. 214 17. Index - p. 271
Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xiii 6. Preface - p. xvii 7. Chapter 1: Internet of Things in healthcare: Smart devices, sensors, and systems related to diseases and health conditions - p. xviii 8. Chapter 2: Real-time data analytics in healthcare using the Internet of Things - p. 35 9. Chapter 3: Lightweight code self-verification using return-oriented programming in resilient IoT - p. 50 10. Chapter 4: Monte-Carlo Simulation models for reliability analysis of low-cost IoT communication networks in smart grid - p. 71 11. Chapter 5: Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT - p. 96 12. Chapter 6: Soft sensor with shape descriptors for flame quality prediction based on LSTM regression - p. 114 13. Chapter 7: Communication-aware edge-centric knowledge dissemination in edge computing environments - p. 138 14. Chapter 8: An effective blockchain-based, decentralized application for smart building system management - p. 156 15. Chapter 9: Privacy and security of Internet of Things devices - p. 181 16. Chapter 10: Software-Defined Networking for the Internet of Things: Securing home networks using SDN - p. 214 17. Index - p. 271
