Probabilistic Data Structures for Blockchain-Based Internet of Things Applications (Hardcover, 1)

I Background 1 Overview of Internet of Things 1.1 Understanding the concept of Internet of Things 1.2 Big data revolution 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 2.8 Smart farming 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 4.8 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 4.16 write() function 5 Cryptography primitives 5.1 Introduction 5.2 Encryption/Decryption process 5.3 Cryptographic hash functions 5.4 Digital signatures 5.5 Zero-knowledge proof 5.6 Hash Tables 5.7 RSA 5.8 Elliptic curve cryptography 6 Blockchain technology and technical foundations 6.1 Fundamentals of blockchain 6.2 Types of blockchain 6.3 Blockchain applications 6.4 Smart contracts 6.5 Issues with blockchain 6.6 Python implementation of blockchain 7 Verification and validation methods used by Blockchain 7.1 Consensus mechanism 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 10.3 Quotient Filter 10.4 Skip List 11 Cardinality Estimation Probabilistic Data Structures 11.1 Introduction 11.2 Linear Counting 11.3 LogLog 11.4 HyperLogLog 12 Frequency Count Query Probabilistic Data Structures 12.1 Introduction 12.2 Count-Min Sketch (CMS) 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 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 Bloom filter with probabilistic data structures 14.3 Integration of Quotient filter 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 HyperLogLog with blockchain 16 Applicability of frequency estimation PDS with Blockchain 16.1 RFID tag clonning 16.2 Understanding heavy hitters 16.3 Integration of count-min-sketch with blockchain 17 Applicability of approximate similarity search PDS with Blockchain 17.1 Approximate nearest neighbor binary search algorithm for wire- less multimedia sensor network 17.2 A Blockchain of image copyrights using locality sensitive hash- ing Bibliography