SBIR Phase I: Hybrid Blockchain to enable safe and transparent distribution of perishable goods

点击翻译按钮获取中文摘要

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to provide open and secure management of transactions between multiple parties in a supply chain. The lack of transparent information throughout the supply chain has eroded the trust and value of many perishable products and the brands they represent. The proposed project addresses the current issue of tracking and traceability of food products in a trusted manner. Through the development of a hybrid blockchain that is sensor aware, all the parties involved in the distribution of the goods will ensure that the food quality is maintained by entering transactions into a secure blockchain. The result is two-fold: consumers will be assured that they are getting the right goods and suppliers can trust that their products are being delivered with quality. While initially focused on the $2 billion food transaction market, the commercial potential will expand to a broader range of products that require secure traceability including consumer goods, home delivery and medical products.This SBIR Phase I project proposes to develop the core technology for the hybrid blockchain and test the functionality in a live commercial setting. The technology represents the first-in-class solution that leverages blockchain and DAGs (directed acyclic graphs) with integrated sensor and hybrid datasets to address the perishable nature of food products. The blockchain model will assume that a transaction is an event of data transfer and that there can be many disjointed events even on a small-scale supply chain network. For traceability, novel algorithms will be designed, making it feasible to connect disjointed events using geometry and a pseudo-Boolean function on top of a system that assigns a unique code. This implementation uses multivariate time series and node features for the meaningful aggregation and disaggregation of datasets, such as data collected by sensors and participating members. Putting such multivariate time series on the graph where participants are nodes with their pairwise relations as edges, the technology will address the novel optimization problem for efficient real-time monitoring and assist in providing further assurance of transaction integrity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.