Probabilistic data structures for blockchain-based internet of things applications / Neeraj Kumar, Arzoo Miglani.

Format:

Book

Author/Creator:

Kumar, Neeraj, author.

Miglani, Arzoo, author.

Language:

English

Subjects (All):

Blockchains (Databases).

Probabilistic databases.

Internet of things.

Physical Description:

1 online resource (323 pages)

Edition:

1st ed.

Place of Publication:

Boca Raton, Florida ; London, England ; New York : CRC Press, [2021]

Summary:

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.

Contents:

Cover

Half Title

Title Page

Copyright Page

Contents

Foreword

Preface

Biography

I: Background

1. Overview of Internet of Things

1.1. Understanding the Concept of Internet of Things

1.1.1. Components of IoT

1.2. Big Data Revolution

1.2.1. Big data and IoT

1.3. Understanding edge computing

2. Smart Applications

2.1. Internet-of-Energy

2.2. Autonomous Vehicles

2.3. Healthcare

2.4. Smart Farming

2.5. Smart Education

2.6. Smart Industry

2.7. Smart City

3. IoT challenges

3.1. Security

3.2. Data Management

3.3. Privacy

3.4. Heterogeneity

3.5. Latency

3.6. Communication Challenge in IoT

II: Blockchain Overview

4. Python Basics

4.1. Introduction

4.2. Comments

4.3. Multi-line Statement

4.4. Blocks and Indentation

4.5. Creating Variables and Assigning Values

4.6. Data Types

4.7. Operators

4.7.1. Arithmetic operator

4.7.2. Comparison operator

4.7.3. Logical operator

4.7.4. Python Bitwise operator

4.7.5. Assignment operator

4.7.6. Membership operator

4.7.7. Identity Operators

4.8. Input and Output in Python

4.9. List

4.10. Dictionary

4.11. Python Conditions and if-else

4.12. Loops

4.13. Functions in Python

4.14. Classes and Objects in Python

4.15. File Handling in Python

4.15.1. open() function

4.15.2. close() function

4.15.3. read() function

4.16. write() function

5. Cryptography Primitives

5.1. Introduction

5.2. Encryption/Decryption Process

5.2.1. Encryption

5.2.2. Decryption

5.2.3. Symmetric key encryption

5.2.4. Asymmetric key encryption

5.2.5. Public key

5.2.6. Private key

5.3. Cryptographic Hash Functions

5.3.1. Typical properties of a hash function

5.3.2. Requirements of hash function:

5.3.3. Applications of cryptographic hash functions.

5.3.4. MD5 message-digest algorithm

5.3.5. SHA-256

5.4. Digital Signatures

5.4.1. Model of digital signature

5.5. Zero-Knowledge Proof

5.6. Hash Tables

5.6.1. Separate chaining

5.6.2. Linear probing

5.6.3. Quadratic probing

5.6.4. Double hashing

5.7. RSA

5.7.1. Steps for key generation:

5.7.2. Encryption

5.7.3. Decryption

5.7.4. RSA example

5.8. Elliptic Curve Cryptography

6. Blockchain Technology and Technical Foundations

6.1. Fundamentals of Blockchain

6.1.1. Characteristics of blockchain technology

6.1.2. What constitutes a block of blockchain?

6.1.3. Bitcoin basics

6.2. Types of Blockchain

6.3. Blockchain Applications

6.4. Smart Contracts

6.5. Issues with Blockchain

6.5.1. IPFS: A solution to decentralized data stoge problem

6.6. Python Implementation of Blockchain

7. Verification and Validation Methods Used by Blockchain

7.1. Consensus Mechanism

7.1.1. Proof-of-Work

7.1.2. Proof-of-Stake

7.1.3. Delegated Proof-of-Stake

7.1.4. Practical Byzantine Fault Tolerance

7.1.5. Proof-of-Authority

7.1.6. Proof-of-Capacity

7.1.7. Proof-of-Burn

7.1.8. Proof-of-Luck

7.2. Simplified Payment Verification

7.3. Block Validation

7.4. Transaction Validation

8. Data Structures for Blockchain

8.1. Data Structures for Blockchain

III: Probabilistic Data Structures: An Overview

9. Introduction to Probabilistic Data Structures

9.1. Need of Probabilistic Data Structures

9.2. Deterministic Data Structures vs. Probabilistic Data Structures

9.3. Probabilistic Data Structures Applications

9.4. Probabilistic Data Structure Challenges

10. Membership Query Probabilistic Data Structures

10.1. Membership Query Probabilistic Data Structures

10.2. Bloom Filter and its Variants

10.2.1. Structure of Bloom filter.

10.2.2. Implementation of BF in Python

10.2.3. Variants of BF

10.2.3.1. Counting BF

10.2.3.2. Compressed BF

10.2.3.3. Spectral BF

10.2.3.4. Deletable Bloom Filter (DBF)

10.2.3.5. Stable BF

10.2.3.6. Retouched Bloom Filter (RBF)

10.2.3.7. Dynamic Bloom Filter

10.2.3.8. Cuckoo Filter

10.2.3.9. Hierarchical BF

10.2.3.10. Stochastic Fair Blue

10.3. Quotient Filter

10.4. Skip List

10.4.1. Skiplist implementation in Python

11. Cardinality Estimation Probabilistic Data Structures

11.1. Introduction

11.2. Linear Counting

11.2.1. Implemtation code of linear counting

11.3. LogLog

11.3.1. Implementation of LogLog in Python

11.4. HyperLogLog

11.4.1. Implementation of HLL in Python

12. Frequency Count Query Probabilistic Data Structures

12.1. Introduction

12.2. Count-Min Sketch

12.2.1. Implementation of CMS with Python

12.2.2. Count-mean-min-sketch

12.3. Count-sketch

12.4. Count-Min with Conservative Update Sketch

13. Approximate Similarity Search Query Probabilistic Data Structures

13.1. Introduction

13.2. Minhashing

13.3. Locality Sensitive Hashing

13.3.1. Simhash

IV: Integration of Probabilistic Data Structures with Blockchain

14. Applicability of Membership Query PDS with Blockchain

14.1. Full Blockchain Client vs Lightweight Blockchain Client

14.2. BF with probabilistic data structures

14.2.1. Bitcoin using BF

14.2.1.1. PoW using BF

14.2.2. Ethereum using BF

14.2.3. Integration of blockchain and BF for certi cate revocation

14.2.4. Integration of blockchain and BF in smart grid sector

14.2.5. Integration of blockchain and Bitcoin for bodyworn sensing devices

14.2.6. Graphene: Block propagation protocol

14.2.7. Anti-malware software using BF and blockchain.

14.2.8. Transaction execution in disaster prone area using BF and blockchain

14.2.9. Non-equivocation with BF and blockchain

14.2.10. Outdoor health monitoring using blockchain and BF

14.2.11. Multi-domain collaboration for MEC in 5G and beyond based on blockchain and BF

14.3. Integration of QF with Blockchain

14.4. Integration of Skiplist with Blockchain

15. Applicability of Cardinality Estimation PDS with Blockchain

15.1. DDoS Attack in Blockchain

15.2. Mempool Transaction Count

15.3. Interation of Linear Counting, LogLog and Hyper-LogLog with Blockchain

15.3.1. Counting transaction in mempool

15.3.2. HLL to prevent DoS attack

15.3.3. IoT chain

16. Applicability of Frequency Estimation PDS with Blockchain

16.1. RFID Tag Cloning

16.2. Understanding Heavy Hitters

16.3. Integration of CMS with Blockchain

16.3.1. Detecting DoS attack

16.3.2. Detecting RFID tag cloning

17. Applicability of Approximate Similarity Search PDS with Blockchain

17.1. Approximate Nearest Neighbor Binary Search Algorithm for Wireless Multimedia Sensor Network

17.2. A Blockchain of Image Copyrights Using Locality Sensitive Hashing

Bibliography

Index.

Notes:

Description based on print version record.

ISBN:

9781003080046

1003080049

9781000327632

1000327639

9781000327694

1000327698

OCLC:

1230568004