Technology Category
Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustConstraint Satisfaction and Optimization: Distributed CSP/OptimizationApplication Domains: Internet of Things, Sensor Networks & Smart Cities
ChainGuards: Verification of Sensed Data using Permissioned Blockchain Technology
🤖 AI Summary
This work proposes a permissioned blockchain-based decentralized verification mechanism to address the issue of untrustworthy sensor data in supply chains, which often leads to anomalous data being recorded on the blockchain. The mechanism uniquely integrates product-level validation rules with blockchain smart contracts, leveraging a rule engine to validate sensor data in real time and automatically trigger alerts and audit procedures upon detecting inconsistencies or anomalies. Experimental evaluation in a real-world cherry supply chain demonstrates that the system effectively ensures the reliability of on-chain data with low computational overhead, accurately identifies abnormal entries, and establishes a trustworthy data foundation for traceability of high-value agricultural products.
Technology Category
Application Category
📝 Abstract
Sensor technologies have evolved to a point where it is now practical to monitor products along the supply chain. The collected data can be stored in a decentralized way using blockchain technology. However, ensuring the reliability of the sensed data is a critical challenge. In other words, we need to trust the data that we write to the blockchain. In this work, we propose ChainGuards, a decentralized system that uses product-specific rules to verify data collected across the supply chain, with particular focus on sensor-derived information, issuing warnings and triggering audits when anomalies are detected. We evaluated ChainGuards using data from a real cherry supply chain deployment. The result shows that the implemented solution provides reliable verification of supply chain data with low performance overhead, able to correctly detect data discrepancies and inconsistencies.
Problem
Research questions and friction points this paper is trying to address.
sensed data reliability
supply chain
blockchain
data verification
trust
Innovation
Methods, ideas, or system contributions that make the work stand out.
permissioned blockchain
supply chain verification
sensor data validation
anomaly detection
decentralized auditing
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