Real-Time Data Analytics for Large Scale Sensor Data

Das, Himansu, Dey, Nilanjan, Emilia Balas, Valentina

  • 出版商: Academic Press
  • 出版日期: 2019-09-03
  • 售價: $5,440
  • 貴賓價: 9.5$5,168
  • 語言: 英文
  • 頁數: 420
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0128180145
  • ISBN-13: 9780128180143
  • 相關分類: 感測器 SensorData Science
  • 海外代購書籍(需單獨結帳)


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 way
  • Discusses intelligent management systems for applications such as healthcare, robotics, and environment modeling
  • Provides 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