TRACE addresses the critical challenge of verifiable Product Carbon Footprint (PCF) tracking in modern logistics operations and supply chains. By leveraging zero-knowledge proofs and cryptographic verification techniques, the system ensures data integrity while maintaining privacy and enabling efficient aggregation of emission data across complex transport networks.
The system implements established industry standards including iLEAP (Integrating Logistics Emissions and PCFs) and GLEC (Global Logistics Emissions Council) frameworks to ensure standardized and reliable carbon footprint calculations for logistics operations.
- 📊 Process-Centric Orchestration: Camunda-based workflow management for logistics processes
- 🔐 Zero-Knowledge Proofs: Uses RISC0 zkVM for cryptographically secure emission calculations
- 🔗 Proof Composition: Enables chaining and aggregation of proofs across supply chain stages
- 📡 Simulated Sensor Integration: Simulates and verifies transport data from IoT sensors for demonstration
- 🛡️ Digital Signatures: RSA-based authentication ensures data source integrity
- 🌐 Distributed Architecture: Microservices-based system with Kubernetes deployment
TRACE consists of several interconnected microservices:
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Business Process Service (Python)
- Orchestrates workflow processes for transport carbon tracking
- Manages BPMN workflows and task coordination
- Integrates with all system components
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Proving Service (Rust/RISC0)
- Generates zero-knowledge proofs for carbon footprint calculations
- Verifies previous proofs and aggregates emission data
- Implements Transport Chain Element (TCE) emission calculations
-
Verifier Service (Rust)
- Independent verification of ZK proofs and digital signatures
- Validates cryptographic receipts and emission claims
- Provides gRPC API for external verification
-
Sensor Data Service (Python/FastAPI)
- Simulates real-world vehicle sensors for demonstration
- Generates signed sensor data with distance measurements
-
Sensor Key Registry (Python/FastAPI)
- Manages and validates RSA public keys for sensor authentication
- Ensures only trusted sensors can contribute data
- Provides REST API for key verification services
-
PCF Registry (Python/Flask)
- Stores and manages Product Carbon Footprint (PCF) proofs
- Provides both REST and gRPC APIs for proof storage/retrieval
- Integrates with MinIO for distributed file storage
The system implements a comprehensive emission calculation model based on:
- Transport Operations Centers (TOC): Road, rail, air, and sea transport modes
- Hub Operations Centers (HOC): Warehousing and logistics hubs
- Transport Chain Elements (TCE): Individual transport segments with mass and distance data
Ensure you have the following tools installed:
- Docker
- Kubernetes (Minikube or Kind)
- kubectl
- Helm
- Git
- Bash shell
- Camunda Modeler (Camunda 8)
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Clone the repository
git clone https://github.com/ACP-PCVCF/TRACE.git cd TRACE -
Deploy the system
Choose one of the following deployment options:
Option A: Minikube with Helm Charts
./scripts/minikube/setup-helm.sh
Option B: Kind with Helm Charts
./scripts/kind/setup-helm.sh
-
Access the services
# Camunda Gateway (for BPMN deployment) kubectl port-forward svc/camunda-zeebe-gateway 26500:26500 -n proving-system # Camunda Operate (process monitoring) kubectl port-forward svc/camunda-operate 8081:80 -n proving-system
For detailed setup instructions, see Setup Documentation.
Or for a visual guide, please watch this short video covering the setup, deployment, and a brief demonstration.
- Setup Instructions - Complete system setup guide
- Update Instructions - Service update and maintenance procedures
This project was developed as part of an academic research initiative. The codebase demonstrates advanced concepts in:
- Zero-knowledge proof systems
- Distributed system architecture
- Cryptographic verification
- Supply chain traceability
- Privacy-preserving computation
Supervision: @curiousjaki, @JonathanHeiss
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
This project was developed as part of an academic research initiative and is open source software that can be freely used, modified, and distributed.