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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 intellige… Read more
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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.
Computer/data scientists, biomedical engineers, researchers and software engineers in the areas of data aggregation, sensor cloud architecture, intelligent sensing, and its applications
1. Introduction to sensors and system
1.1 Fundamentals of sensors/transducers
1.2 Principles and properties
1.3 Classification of sensors
1.4 Networking methodology
1.5 Types of sensors
1.6 Smart sensors and transducers
1.7 Summary
References
2. Real-life application of sensors and systems
2.1 Overview of Internet of things
2.2 Design perspective
2.3 Related platform
2.4 Real-life examples and implementation
2.5 WSN simulation environments
2.6 Summary
References
3. Wireless sensor network: principle and application
3.1 Wireless communication and sensor networks
3.2 Sensor components and technology
3.3 Sensor network protocols
3.4 Sensor networks application scenario
3.5 Summary
References
4. Overview of sensor cloud
4.1 Basics of cloud computing
4.2 Types of clouds
4.3 Cloud computing models
4.4 Sensor cloud platform
4.5 Sensor cloud architecture
4.6 Sensor cloud workflow
4.7 Application scenario
4.8 Summary
References
5. Sensor data accumulation methodologies
5.1 Sensor data classification
5.2 Data transmission methodology
5.3 Convergecast: inverse of broadcasting
5.4 Data aggregation
5.5 Choice of MAC layer
5.6 Energy analysis
5.7 Data collection methodologies
5.8 Types of aggregation
5.9 Summary
References
Further reading
6. Intelligent sensor network
6.1 Introduction
6.2 Intelligence hierarchy
6.3 Preliminary concepts of AI and Machine Learning
6.4 Intelligent approaches in WSN node deployment
6.5 Intelligent routing overview
6.6 Sensor data mining
6.7 Intelligent sensor network applications
6.8 Summary
References
Further reading
7. Conclusion
7.1 Chapters 1 and 2
7.2 Chapters 3 and 4
7.3 Chapters 5 and 6
7.4 Scope for future enhancement
Index
AM
AK
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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.