Data Layer

The Teneo Data Layer is a platform that connects real-world machine and device data to Web3, enabling data utilization, monetization, and tokenization. It integrates advanced technologies for data collection, AI analytics, and secure blockchain storage, creating a comprehensive ecosystem for various industries.

Data Processing and Enrichment

The data is transmitted from the real-world machine via the machine SDK and distributed to the nodes using a round-robin method. Each node receives a fraction of the encrypted data, secured with the node's public key. Teneo's machine learning algorithms and AI then label the data to determine its category. The data is stored in short-term memory and wiped after labeling. Thanks to the sealed and confidential hardware, the data remains isolated from any external access.

Teneo nodes can label incoming machine data through a combination of automated processes, AI algorithms, and decentralized validation mechanisms. Here's a detailed approach:

Data Collection and Initial Processing:

The raw data is pre-processed to remove noise and ensure consistency. This may involve normalizing values, handling missing data, and converting data formats.

Automated Labeling with AI Algorithms:

AI-Based Labeling: AI algorithms trained on historical data can automatically label the incoming data. These algorithms can recognize patterns and categorize data based on predefined criteria. For example, a model trained to recognize vehicle telemetry data can label incoming data as speed, location, fuel consumption, etc.

Decentralized Validation and Consensus:

• tNodes Role: Teneo’s decentralized nodes (tNodes) play a crucial role in validating and labeling data. Multiple nodes process the data independently to ensure accuracy and reliability.

• Zero-Knowledge Proofs: tNodes use zero-knowledge proofs to validate the integrity of the data without revealing the data itself. This ensures data privacy while verifying its correctness.

• Consensus Mechanism: A consensus mechanism, Proof of Stake (PoS), is used to agree on the final labels.

Feedback and Continuous Improvement:

• Feedback Loop: Data consumers (e.g., AI models, companies) provide feedback on the labeled data, which is used to improve the labeling algorithms and processes.

• Iterative Training: The AI models are continuously retrained with new labeled data and feedback, enhancing their accuracy over time.

Data Flow

Direct Data Acquisition:

The data is sourced directly from machines, ensuring no interference from other machines or humans. This approach guarantees the data's accuracy and reliability as it is validated straight from the source.

API Integration:

For machines that already have validated data sources, Teneo can use product APIs from companies like Tesla or Starlink satellites. Additionally, IoT devices can be integrated to verify data from two sources of truth.

Connection:

Smart Machines/IoT Devices: Devices connected to the internet can directly transmit data to Teneo. For devices not yet connected, Teneo provides connectivity solutions.

Examples of Data Flow:

• Ride Hail Vehicles: Teneo can access vehicle data through APIs or SIM cards connected to the vehicle's CAN Bus. This data is then transmitted over the mobile network to Teneo.

• Satellites: Tokenized satellites transmit data packages directly to Teneo, with no additional hardware required.

• Other Smart Devices: Devices like harvester robots, solar panels, and delivery drones send data to their operating companies, which then connect to Teneo.

Processing and Integration:

Once data is received, Teneo processes and links it to the blockchain. This process ensures real-time tracking and management of machine data, enhancing transparency and accuracy.