Real-Time Data Analytics for Large Scale Sensor Data
Advances in ubiquitous sensing applications for healthcare Series, Vol. 6

Coordinators: Das Himansu, Dey Nilanjan, Emilia Balas Valentina

Language: Anglais
Cover of the book Real-Time Data Analytics for Large Scale Sensor Data

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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
1. Internet of Things (IoT) in Healthcare: Smart Devices, Sensors, and Systems Related to Diseases and Health Conditions
2. Real-Time data Analytics in Internet of Things for HealthCare
3. Lightweight Code Self-Verification using Return Oriented Programming in Resilient IoT
4. Monte-Carlo Simulation Models for Reliability Analysis of Low-Cost IoT Communication Networks in Smart Grid
5. Lightweight Ciphertext Policy-Attribute based Encryption (LCP-ABE) scheme for Data Privacy and Security in Cloud assisted IoT
6. Soft Sensor with Shape Descriptors for Flame Quality Prediction based on LSTM Regression
7. Communication-aware Edge-centric Knowledge Dissemination in Edge Computing Environments
8. An Effective Blockchain-based Decentralized Application for Smart Building System Management
9. Privacy and Security of Internet of Things Devices
10. Software Defined Networking for Internet of Things: Securing Home networks (IoT) using SDN
Himansu Das is working as an as Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He has received his B. Tech and M. Tech degree from Biju Pattnaik University of Technology (BPUT), Odisha, India. He has published several research papers in various international journals and conferences. He has also edited several books of international repute. He is associated with different international bodies as Editorial/Reviewer board member of various journals and conferences. He is a proficient in the field of Computer Science Engineering and served as an organizing chair, publicity chair and act as member of program committees of many national and international conferences. He is also associated with various educational and research societies like IACSIT, ISTE, UACEE, CSI, IET, IAENG, ISCA etc., His research interest includes Grid Computing, Cloud Computing, and Machine Learning. He has also 10 years of teaching and research experience in different engineering colleges.
Nilanjan Dey received his Ph. D. Degree from Jadavpur University, India, in 2015. He is an Assistant Professor in the Department of Information Technology, Techno India College of Technology, Kolkata, W.B., India. He holds an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA and Research Scientist of Laboratory of Applied Mathematical Modeling in Human Physiology, Territorial Organization of- Scientific and Engineering Unions, Bulgaria. Associate Researcher of Laboratoire RIADI, University of Manouba, Tunisia. His research topic is Medical Imaging, Data mining, Machine learning, Computer Aided Diagnosis, Atherosclerosis etc. He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence (IGI Global), US, International Journal of Rough Sets and Data Analysis (IGI Global), US, the International Journal of Synthetic Emotions (IGI Global), US, (Co-EinC) and International Journal of Natural Com