A DLT-based Trust Framework for IoT Ecosystems
Dec 2018 - Jan 2023
Architecture and trust-reputation framework for IoT edge-cloud ecosystems using DLT and off-chain ML trust computation.
Problem Statement
- IoT ecosystems lacked transparent trust management across edge, cloud, and stakeholder boundaries.
- Centralized trust approaches introduced single points of failure.
- Security, decentralization, and scalability trade-offs needed measurable evidence.
What I Led
- Designed DLT-based architecture for decentralized IoT ecosystems with off-chain ML trust computation.
- Built and evaluated trust reputation mechanisms across different consensus and deployment choices.
- Ran simulation experiments to assess security, decentralization, and scalability impacts.
Deliverables
- DLT architecture and off-chain trust workflow
- ML-driven trust reputation model
- Simulation and evaluation outputs
Architecture Placeholder
Diagram slot for system architecture and data flow.
Hyperledger FabricEthereumPyTorchScikit-learnTensorFlowGoRaftPBFT
Industry Relevance
Provides actionable design guidance for secure IoT trust management where multi-party coordination and auditability are required.
Research-to-practice bridge
This work translated thesis-level contributions into architectural patterns that are useful for real IoT deployments and trust workflow design.