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Digital MRV (dMRV) provides cryptographically verifiable proof of real-world activities and impacts through automated data collection, validation, and certification. Unlike traditional MRV, it enables real-time monitoring, cryptographic trust, and automated verification.

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Key Advantages

Real-Time Data

Continuous IoT monitoring vs periodic surveys

Cryptographic Trust

Tamper-proof records with blockchain anchoring

Protocol-Driven

Standardized templates for rapid deployment

Automated Verification

AI-powered validation with Oracle services

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Protocol Configuration

  • Select or create protocol template
  • Define measurement parameters
  • Configure validation rules
  • Set reporting intervals
  • Map to recognized standards
  • Set baseline calculations
  • Define emission factors
  • Configure verification rules

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Example Protocol

{
  "type": "CleanCookingProtocol",
  "version": "1.0.0",
  "methodology": "GS_MMECD_1.0",
  "parameters": {
    "measurementInterval": 300,
    "requiredMetrics": [
      "burnTime",
      "fuelConsumed",
      "temperature"
    ],
    "baselineEmissionFactor": 7.2,
    "minimumDataPoints": 100
  },
  "validation": {
    "rules": [{
      "metric": "temperature",
      "min": 50,
      "max": 300
    }],
    "requiredEvidence": [
      "deviceTelemetry",
      "fuelDelivery",
      "baselineData"
    ]
  }
}

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Data Collection

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Device Integration

from emerging import Device, Protocol

# Load protocol
protocol = Protocol.get("clean-cooking-v1")

# Configure device with protocol
device = Device.configure(
    device_id="did:ixo:device/123",
    protocol=protocol,
    settings={
        "measurement_interval": 300,
        "offline_buffer_size": 1000
    }
)

# Start measurements
device.start_monitoring()

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Data Pipeline

  • Device authentication
  • Secure data transmission
  • Edge validation
  • Offline buffering
  • Protocol validation
  • Data aggregation
  • Anomaly detection
  • Baseline comparison

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Verification Process

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Oracle Network

from emerging import Oracle, Protocol

# Initialize oracle with protocol
oracle = Oracle(
    protocol_id="clean-cooking-v1",
    min_confidence=0.95,
    required_validators=3
)

# Verify measurements
verification = oracle.verify_measurements(
    measurements=device_data,
    baseline=baseline_data,
    evidence={
        "telemetry": sensor_logs,
        "delivery": fuel_records
    }
)

# Issue credential if valid
if verification.is_valid:
    credential = verification.issue_credential()

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Validation Rules

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protocolCompliance
boolean
required
Measurements follow protocol specification
​
dataIntegrity
boolean
required
Cryptographic proofs are valid
​
evidenceQuality
number
required
Confidence score for supporting evidence

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Credential Issuance

{
  "@context": [
    "https://www.w3.org/2018/credentials/v1",
    "https://w3id.org/dmrv/v1"
  ],
  "type": ["VerifiableCredential", "MeasurementClaim"],
  "issuer": "did:ixo:oracle/456",
  "issuanceDate": "2024-03-15T12:00:00Z",
  "credentialSubject": {
    "id": "did:ixo:device/123",
    "protocol": "clean-cooking-v1",
    "measurements": {
      "burnTime": 300,
      "fuelConsumed": 15
    },
    "evidence": [{
      "type": "TelemetryData",
      "hash": "0x123...",
      "uri": "ipfs://Qm..."
    }]
  }
}

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Best Practices

Follow protocol specifications carefully and implement comprehensive data validation at every stage.

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Security

  • Use secure communication channels
  • Implement device authentication
  • Validate data integrity
  • Monitor for anomalies

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Scalability

  • Configure offline buffering
  • Implement batch processing
  • Use load balancing
  • Monitor system performance

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Next Steps

Protocol Guide

Create custom protocols

Device Integration

Connect IoT devices

Oracle Setup

Configure validation networks