Real-Time Data Analytics for Large Scale Sensor Data

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, development of software methods, techniques and tools, applications and 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. Most of the envisioned IoT applications involve complex intelligent systems that have to cater to situations that are geo-distributed in nature. Examples of such IoT based use cases include smart healthcare, management and decision making in smart grids, and disaster management, among others. In order that the aforementioned applications can meet real-time constraints, a number of research issues need to be addressed. Though it has been a well-accepted fact that bringing processing from a central data center to much closer premises at the edge of networks through extensive distributed processing is a potential solution, this area has not been explored much. Such a computing paradigm should form the basis of large-scale deployments with real-time alarms and triggers for their control aspects. Real-Time Data Analytics for Large-Scale Sensor Data captures the essence of real-time IoT based solutions that require a multi-disciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, as well as performance issues owing to geo-distributed data sources, optimization, distributed machine learning and many others. Examines IoT applications, design of real-time intelligent systems, as well as how to manage the rapid growth of the large volume of sensor data on a daily basis in an efficient wayDiscusses intelligent management systems for applications such as healthcare, robotics, and environment modelingProvides a focused approach towards design and implementation of real-time intelligent systems for the management of sensor data in large scale environments such as biomedical and clinical applications INDICE: 1. Internet of Things (IoT) in Healthcare: Smart Devices, Sensors, and Systems Related to Diseases and Health Conditions2. Real-Time data Analytics in Internet of Things for HealthCare3. Lightweight Code Self-Verification using Return Oriented Programming in Resilient IoT4. Monte-Carlo Simulation Models for Reliability Analysis of Low-Cost IoT Communication Networks in Smart Grid5. Lightweight Ciphertext Policy-Attribute based Encryption (LCP-ABE) scheme for Data Privacy and Security in Cloud assisted IoT6. Soft Sensor with Shape Descriptors for Flame Quality Prediction based on LSTM Regression7. Communication-aware Edge-centric Knowledge Dissemination in Edge Computing Environments8. An Effective Blockchain-based Decentralized Application for Smart Building System Management9. Privacy and Security of Internet of Things Devices10. Software Defined Networking for Internet of Things: Securing Home networks (IoT) using SDN

• ISBN: 978-0-12-818014-3
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 420
• Fecha Publicación: 01/09/2019
• Nº Volúmenes: 1
• Idioma: Inglés