DGT DOCS
  • 1. INTRODUCTION
    • 1.1 Executive Summary
    • 1.2 Why DGT
    • 1.3 Distributed Ledgers Technology
      • 1.3.1 Decentralization approach
      • 1.3.2 Consensus Mechanism
      • 1.3.3 Transactions
      • 1.3.4 Layered Blockchain Architecture
      • 1.3.5 Tokenomics
      • 1.3.6 Web 3 Paradigm
      • 1.3.7 Common Myths about Blockchain
    • 1.4 The DGT Overview
      • 1.4.1 Platform Approach
      • 1.4.2 DGT Functional Architecture
      • 1.4.3 Technology Roadmap
    • 1.5 How to create a Solution with DGT Networks
    • 1.6 Acknowledgments
  • 2. REAL WORLD APPLICATIONS
    • 2.1 Case-Based Approach
      • 2.1.1 DGT Mission
      • 2.1.2 The Methodology
      • 2.1.3 Case Selection
    • 2.2 Supply Chain and Vertical Integration
      • 2.2.1 Logistics Solution for Spare Parts Delivery
      • 2.2.2 DGT Based Solution for Coffee Chain Products
    • 2.3 Innovative Financial Services
      • 2.3.1 Crowdfunding Platform
      • 2.3.2 Real World Assets Tokenization
      • 2.3.3 Virtual Neobank over DGT Network
      • 2.3.4 DGT based NFT Marketplace
    • 2.4 Decentralized Green Energy Market
      • 2.4.1 Peer To Peer Energy Trading
      • 2.4.2 DGT based Carbon Offset Trading
    • 2.5 B2B2C Ecosystems and Horizontal Integration
      • 2.5.1 KYC and User Scoring
      • 2.5.2 Decentralized Marketing Attribution
      • 2.5.3 Case Decentralized Publishing Platform
      • 2.5.4 Value Ecosystem
    • 2.6 More Cases
  • 3. DGT ARCHITECTURE
    • 3.1 Scalable Architecture Design
      • 3.1.1 High Level Architecture
      • 3.1.2 DGT Approach
      • 3.1.3 Unique contribution
      • 3.1.4 Component Based Architecture
    • 3.2 Performance Metrics
    • 3.3 Network Architecture
      • 3.3.1 Nework Architecture in General
      • 3.3.2 Network Identification
      • 3.3.3 H-Net Architecture
      • 3.3.4 Transport Level
      • 3.3.5 Segments
      • 3.3.6 Static and Dynamic Topologies
      • 3.3.7 Cluster Formation
      • 3.3.8 Node Networking
      • 3.3.9 Permalinks Control Protocol
    • 3.4 Fault-Tolerant Architecture
      • 3.4.1 Introduction to Fault Tolerance
      • 3.4.2 F-BFT: The Hierarchical Consensus Mechanism
      • 3.4.3 Cluster Based Algorithms
      • 3.4.4 Arbitrator Security Scheme
      • 3.4.5 Heartbeat Protocol
      • 3.4.6 Oracles and Notaries
      • 3.4.7 DID & KYC
    • 3.5 Transactions and Performance
      • 3.5.1 Transaction Basics
      • 3.5.2 Transaction Processing
      • 3.5.3 Transaction and block signing
      • 3.5.4 Transaction Families
      • 3.5.5 Transaction Receipts
      • 3.5.6 Smart Transactions
      • 3.5.7 Private Transactions
      • 3.5.8 Multi signature
    • 3.6 Data-Centric Model
      • 3.6.1 Data layer overview
      • 3.6.2 Global State
      • 3.6.3 Genesis Record
      • 3.6.4 Sharding
      • 3.6.5 DAG Synchronization
    • 3.7 Cryptography and Security
      • 3.7.1 Security Architecture Approach
      • 3.7.2 Base Cryptography
      • 3.7.3 Permission Design
      • 3.7.4 Key Management
      • 3.7.5 Encryption and Decryption
      • 3.7.6 Secure Multi Party Computation
      • 3.7.7 Cryptographic Agility
      • DGTTECH_3.8.4 Gateway Nodes
    • 3.8 Interoperability
      • 3.8.1 Interoperability Approach
      • 3.8.2 Relay Chain Pattern
      • 3.8.3 Virtual Machine Compatibility
      • 3.8.4 Gateway Nodes
      • 3.8.5 Token Bridge
    • 3.9 DGT API and Consumer Apps
      • 3.9.1 Presentation Layer
      • 3.9.2 Application Architecture
    • 3.10 Technology Stack
    • REFERENCES
  • 4. TOKENIZATION AND PROCESSING
    • 4.1 Introduction to Tokenization
      • 4.1.1 DGT Universe
      • 4.1.2 Driving Digital Transformation with Tokens
      • 4.1.3 Real-World Tokenization
      • 4.1.4 Key Concepts and Definitions
    • 4.2 Foundations of Tokenization
      • 4.2.1 Definition and Evolution of Tokenization
      • 4.2.2 Tokenization in the Blockchain/DLT Space
      • 4.2.3 The Tokenization Process
      • 4.2.4 Tokenization on the DGT Platform
      • 4.2.5 Regulatory and Legal Aspects of Tokenization
      • 4.2.6 Typical Blockchain-Based Business Models
    • 4.3 The DEC Transaction Family
      • 4.3.1 DEC Transaction Family Overview
      • 4.3.2 DEC Token Features
      • 4.3.3 DEC Token Protocol
      • 4.3.4 DEC Account Design
      • 4.3.5 DEC Transaction Family Flow
      • 4.3.6 DEC Commands
      • 4.3.7 DEC Processing
      • 4.3.8 Payment Gateways
    • 4.4 Understanding Secondary Tokens
      • 4.4.1 The different types of tokens supported by DGT
      • 4.4.2 How secondary tokens are produced
  • 5. EXPLORING TOKENOMICS
    • 5.1 Introduction
      • 5.1.1 What does tokenomics mean?
      • 5.1.2 Goals of Building the Model for DGT Network
      • 5.1.3 Tokens vs Digital Money
      • 5.1.4 The Phenomenon of Cryptocurrency
      • 5.1.5 Basic Principles of Tokenomics
      • 5.1.6 AB2023 Model
    • 5.2 Node & User Growth
      • 5.2.1 Node Ecosystem
      • 5.2.2 User Growth and Retention Modeling
    • 5.3 Transactions
      • 5.3.1 Transaction Amount Components
      • 5.3.2 Shaping the Transaction Profile: A Three-pronged Approach
      • 5.3.3 Calculation of Transaction Number
    • 5.4 Network Performance Simulation
      • 5.4.1 Endogenous Model
      • 5.4.2 Network Entropy
      • 5.4.3 Network Utility
    • 5.5 Token Supply Model
      • 5.5.1 Introduction to Supply and Demand Dynamics
      • 5.5.2 Token distribution
      • 5.5.3 Supply Protocol
      • 5.5.4 Token Balance and Cumulative Supply
    • 5.6 Token Demand Model
      • 5.6.1 Node-Base Demand
      • 5.6.2 Transaction-Based Token Demand
      • 5.6.3 Staking Part Modeling
      • 5.6.4 Total Demand
    • 5.7 Token Price Simulation
      • 5.7.1 Nelson-Siegel-Svensson model
      • 5.7.2 The Price Model
    • 5.8 Decentralization Measurement
      • 5.8.1 Active Node Index
      • 5.8.2 Node Diversity in Hybrid Networks
      • 5.8.3 Token distribution
      • 5.8.4 Integral Calculation of Decentralization Metric
    • 5.9 Aggregated Metrics
      • 5.9.1 Transaction Throughput: Evaluating Network Performance and Scalability
      • 5.9.2 Market Capitalization: A Dimension of Valuation in Cryptocurrency
      • 5.9.3 Total Value Locked (TVL): A Spotlight on Network Engagement and Trust
  • 6. ADMINISTRATOR GUIDE
    • 6.1 Introduction
      • 6.1.1 Administrator Role
      • 6.1.2 Platform sourcing
      • 6.1.3 DGT Virtualization
      • 6.1.4 Using Pre-Built Virtual Machine Images
      • 6.1.5 Server Preparation
      • 6.1.6 OS Setup and initialization
    • 6.2 DGT CORE: Single Node Setup
      • 6.2.1 Launch the First DGT Node
      • 6.2.2 Dashboard setup
      • 6.2.3 Nodes Port Configuration
      • 6.2.4 Single Node Check
    • 6.3 DGT CORE: Setup Private/Public Network
      • 6.3.1 Network launch preparation
      • 6.3.2 A Virtual Cluster
      • 6.3.3 A Physical Network
      • 6.3.4 Attach node to Existing Network
    • 6.4 DGT Dashboard
    • 6.5 DGT CLI and base transaction families
    • 6.6 GARANASKA: Financial Processing
      • 6.6.1 Overview of DGT’s financial subsystem
      • 6.6.2 DEC emission
      • 6.6.3 Consortium account
      • 6.6.4 User accounts
      • 6.6.5 Payments
    • 6.7 Adjust DGT settings
      • 6.7.1 DGT Topology
      • 6.7.2 Manage local settings
    • 6.8 DGT Maintenance
      • 6.8.1 Stopping and Restarting the Platform
      • 6.8.2 Backing up Databases
      • 6.8.3 Network Performance
      • 6.8.4 Log & Monitoring
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  1. 5. EXPLORING TOKENOMICS

5.8 Decentralization Measurement

Navigating through the intricate layers of DGT networks presents a fascinating exploration into the world of blockchain and cryptocurrency. The dichotomy between decentralized (Boko 2002) and centralized models is prominently showcased in DGT’s consortium-based solution, meticulously crafted to harmonize the efficiency and scalability of centralized models with the trust and transparency of decentralized networks. In this chapter, we plunge into the depth of modeling DGT characteristics such as Node Distribution, Transaction Distribution, Token Distribution, and Consortium Impact, thereby unveiling the critical metrics that illuminate the network’s equilibrium in the context of decentralization.

DGT networks astutely reject the peer-to-peer model of node interaction, opting instead for the creation of clusters and embracing a hybrid network nature, intertwining private and public segments. This thoughtful design enhances communication efficiency and amplifies overall scalability, yet brings to light an essential query: How can decentralization be preserved amidst this efficiency? This question paves the way for the introduction of redundancy within verification mechanisms, particularly observable when achieving consensus. As we traverse this path, we encounter the necessity of meticulous calculations to justify such redundancy, extending beyond the conventional Nakamoto metric to embrace a multitude of considerations. These span across various network "forces", such as the distribution of computing power and tokens, creating a nuanced landscape where economic characteristics and the advantages of decentralized solutions precariously balance on the tightrope of strategic network planning.

Embarking on this journey, we acknowledge that not all nodes or user accounts can stand as independent economic agents within the network. Thus, our exploration is underscored by a pivotal quest: To discern the degree of dependency that not only preserves the network’s decentralized integrity (Zhang, Ma, and Liu 2023) but also nurtures its evolution and development in the burgeoning world of blockchain technology.

Below are the main calculated results according to the AB2023 model (Khvatov and Bogdanov 2023)

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Last updated 1 year ago