Probabilistic Data Structures for Blockchain-Based Internet of Things Applications
1st Edition
Published on 27. January 2021
322 pages
978-1-000-32769-4 (ISBN)
Description
This book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the applicability of PDS in BC to technology practitioners and explains each PDS through code snippets and illustrative examples. Further, it provides references for the applications of PDS to BC along with implementation codes in python language for various PDS so that the readers can gain confidence using hands on experience. Organized into five sections, the book covers IoT technology, fundamental concepts of BC, PDS and algorithms used to estimate membership query, cardinality, similarity and frequency, usage of PDS in BC based IoT and so forth.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
17 Tables, black and white; 103 Illustrations, black and white
ISBN-13
978-1-000-32769-4 (9781000327694)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Neeraj Kumar | Arzoo Miglani
Probabilistic Data Structures for Blockchain-Based Internet of Things Applications
Book
10/2024
1st Edition
CRC Press
€98.30
Shipment within 15-20 days
Neeraj Kumar | Arzoo Miglani
Probabilistic Data Structures for Blockchain-Based Internet of Things Applications
Book
12/2020
1st Edition
CRC Press
€179.51
Shipment within 15-20 days
Persons
Prof. Neeraj Kumar received his Ph.D. in CSE from Shri Mata Vaishno Devi University, Katra (Jammu and Kashmir), India in 2009, and was a postdoctoral research fellow in Coventry University, Coventry, UK. He is working as a Professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala (Pb.), India. His research areas are Network management, IoT, Big Data Analytics, Deep learning and cyber-security.
Arzoo Miglani is currently pursuing Ph.D. from Thapar Institute of Engineering & Technology (TIET), Patiala. She had worked with DIT University, Dehradun for 2 years as an assistant professor and with TIET for 1 year. She has done her ME in Information Security from TIET in 2015. She has completed her B.tech from GJU, Hisar in 2009. She is GATE qualified. Her research area includes Wireless Sensor networks and network security, blockchain and content centric networking.
Arzoo Miglani is currently pursuing Ph.D. from Thapar Institute of Engineering & Technology (TIET), Patiala. She had worked with DIT University, Dehradun for 2 years as an assistant professor and with TIET for 1 year. She has done her ME in Information Security from TIET in 2015. She has completed her B.tech from GJU, Hisar in 2009. She is GATE qualified. Her research area includes Wireless Sensor networks and network security, blockchain and content centric networking.
Content
Part I-Background: 1. Overview of Internet of Things. 2 Smart applications. 3. IoT challenges. Part II- Blockchain overview. 4 Python Basics. 5. Cryptography primitives. 6. Blockchain technology and technical foundations. 7. Verification and validation methods used by Blockchain. 8. Data structures for Blockchain. Part III-Probabilistic data structures: An overview. 9. Introduction to probabilistic data structures. 10. Membership Query Probabilistic Data Structures. 11. Cardinality Estimation Probabilistic Data Structures. 12. Frequency Count Query Probabilistic Data Structures. 13. Approximate Similarity Search Query Probabilistic Data Structures. Part IV-Integration of Probabilistic Data Structures with Blockchain. 14. Applicability of membership query PDS with Blockchain. 15. Applicability of cardinality estimation PDS with Blockchain. 16. Applicability of frequency estimation PDS with Blockchain. 17. Applicability of approximate similarity search PDS with Blockchain.
