A Beginner's Guide to Data Agglomeration and Intelligent Sensing

A Beginner's Guide to Data Agglomeration and Intelligent Sensing

ISBN-10:
0128203412
ISBN-13:
9780128203415
Pub. Date:
02/24/2020
Publisher:
Elsevier Science
ISBN-10:
0128203412
ISBN-13:
9780128203415
Pub. Date:
02/24/2020
Publisher:
Elsevier Science
A Beginner's Guide to Data Agglomeration and Intelligent Sensing

A Beginner's Guide to Data Agglomeration and Intelligent Sensing

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Overview

A Beginners Guide to Data Agglomeration and Intelligent Sensing provides an overview of the Sensor Cloud Platform, Converge-casting, and Data Aggregation in support of intelligent sensing and relaying of information. The book begins with a brief introduction on sensors and transducers, giving readers insight into the various types of sensors and how one can work with them. In addition, it gives several real-life examples to help readers properly understand concepts. An overview of concepts such as wireless sensor networks, cloud platforms, and device-to-cloud and sensor cloud architecture are explained briefly, as is data gathering in wireless sensor networks and aggregation procedures.

Final sections explore how to process gathered data and relay the data in an intelligent way, including concepts such as supervised and unsupervised learning, software defined networks, sensor data mining and smart systems.


Product Details

ISBN-13: 9780128203415
Publisher: Elsevier Science
Publication date: 02/24/2020
Pages: 198
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Amartya Mukherjee received his M.Tech from NIT Durgapur and is presently pursuing a PhD from Maulana Abul Kalam Azad University of Technology, Kalyani. He is currently the head of the Department of Computer Science & Engineering (AIML). He has over 14 years of teaching experience in the field of Computer Science. He has written numerous books and research articles in the field of IoT, embedded application development, wireless communication, and smart systems for various renowned publishing companies, like IEEE, Elsevier, Springer, Taylor & Francis, and World Scientific Press. His Embedded Systems & Robotics with Opensource Tools is one of the bestselling books in embedded application development. His research interests include flying ad-hoc networks, IoT, IoDT, intelligent systems, 5G and 6G communication, and machine learning.

Ayan Kumar Panja is currently Assistant Professor at the Institute of Engineering and Management, Kolkata, India. His main research is Machine Learning, Pattern Recognition, Audio Signal Processing, Wireless Communication and Sensor Networks.

Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He also holds a position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence , IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (SpringerNature), Data-Intensive Research(SpringerNature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He was an associate editor of IET Image Processing and editorial board member of Complex & Intelligent Systems, Springer Nature. He is an editorial board member of Applied Soft Computing, Elsevier. He is having 35 authored books and over 300 publications in the area of medical imaging, machine learning, computer aided diagnosis, data mining, etc. He is the Fellow of IETE and Senior member of IEEE.

Table of Contents

1. Introduction to Sensors and Systems2. Real life Application of Sensors and Systems3. Wireless Sensor Network: Principle and Application4. Overview of Sensor Cloud5. Sensor Data Accumulation Methodology6. Intelligent Sensor Network7. Conclusion

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Provides a foundational overview of data agglomeration and intelligent sensing

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