AISim
  • 1. Market Background
    • 1.1 Development Prospects
    • 1.2 Potential Challenges
  • 2. AISim: The World’s First Web3 IoE Network
    • 2.1 AIA Protocol (AISim Intelligent Access Protocol)
    • 2.2 Decentralized Identity Authentication (DID) System
    • 2.3 Distributed AI Acceleration Engine
    • 2.4 Intelligent Privacy Computing Module
    • 2.5 DeAI Client (Decentralized AI Client)
    • 2.6 IoE Data Management and Intelligent Caching System
  • 3. Technical Architecture
    • 3.1 AIA Protocol
      • 3.1.1 Protocol Adaptation Layer
      • 3.1.2 Distributed Task Scheduling Engine
      • 3.1.3 Decentralized Communication Network
      • 3.1.4 Cross-layer Data Encryption and Privacy Protection
      • 3.1.5 Dynamic Resource Scheduling and Optimization
    • 3.2 Distributed AI Acceleration
      • 3.2.1 Edge Node Computing Optimization
      • 3.2.2 Multi-Node Distributed Execution
      • 3.2.3 Privacy-Preserving Collaborative Training
    • 3.3 Decentralized Identity and Access Management
      • 3.3.1 Identity Verification and Access Level Grading
      • 3.3.2 Multi-level Data Protection
  • 4. Application Scenario
    • 4.1 Smart Healthcare and Health Management
    • 4.2 Autonomous Driving and Intelligent Transportation
    • 4.3 Agricultural Internet of Things and Precision Agriculture
    • 4.4 Industrial Automation and Intelligent Manufacturing
    • 4.5 Smart City and Public Services
    • 4.6 Edge Computing and Distributed AI
  • 5. IoE Web3 Ecosystem Construction
    • 5.1 DID Physical Nodes
      • 5.1.1 Types of Smart SIM Cards
      • 5.1.2 Rights of DID Physical Nodes
    • 5.2 MVNO Integration
    • 5.3 Ecosystem Incentives
  • 6. Tokenomics
    • 6.1 Token Distribution
    • 6.2 AST Token Use Cases
  • 7.Roadmap
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  1. 2. AISim: The World’s First Web3 IoE Network

2.3 Distributed AI Acceleration Engine

AISim's Distributed AI Acceleration Engine provides robust computing power and supports AI Agents for inference and model updates through edge computing nodes. This engine optimizes task response time and ensures data privacy during processing using Fully Homomorphic Encryption (FHE) and Multi-Party Computation (MPC) technologies.

  • Edge Computing Nodes

Edge computing nodes are distributed globally across multiple regions, enabling data processing close to the source, reducing the latency associated with traditional cloud computing. AI Agents can perform inference and computation on these edge nodes, thereby enhancing the system's response speed and efficiency. Through a distributed computing architecture, the AISim network can process data simultaneously at multiple locations, reducing bottlenecks in data transmission and processing.

  • Real-time Inference and Model Updates

Leveraging efficient edge computing resources and distributed storage, AI Agents can not only perform real-time inference but also dynamically update their models. This enables them to quickly adapt to changing environments and task requirements, improving the overall decision-making efficiency and accuracy of the system.

  • Privacy-preserving Collaborative Training

To ensure data privacy, AISim integrates Multi-Party Computation (MPC) technology, allowing multiple nodes to share computational results without exposing their respective data. Additionally, by utilizing Fully Homomorphic Encryption (FHE) technology, AISim ensures that all data and models remain encrypted during cross-node collaborative training, further strengthening data privacy protection.

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Last updated 1 month ago