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Tackling Grand Challenges in Food Safety: A Big Data and IoT Enabled Approach

Overview

In this project, we apply our research achievements to food safety area and tackle the critical issues including biological risks, chemical risks, and food fraud in food safety. These issues are not adequately addressed by current solutions, e.g., sampling inspection. First, although biological and chemical risks can be well detected, food status cannot be traced in a secure and authentic way to avoid food fraud. Second, the sampling inspection is conducted by human resources, which is labor-intensive and time-consuming. Third, the sampling inspection can only detect food status while it leaves unknown which part goes wrong and what the future food status will be. To tackle the above food safety issues, we develop a big data and IoT enabled system, which provides three food safety services, i.e., traceability, status detection, and risk prediction.

Demo

Achievements

  • Xiulong Liu, Xin Xie, Shangguang Wang, Jia Liu, Didi Yao, Jiannong Cao, and Keqiu Li. "Efficient range queries for large-scale sensor-augmented RFID systems." IEEE/ACM Transactions on Networking 27, no. 5 (2019): 1873-1886
  • Yanni Yang, Yanwen Wang, Jiannong Cao, Jinlin Chen, "HearLiquid: Non-intrusive Liquid Fraud Detection Using Commodity Acoustic Devices", under submission
  • Yanni Yang, Weiwei Fu, Jiannong Cao, Jinlin Chen, Irwin King, "Repurposing Online Restaurant Reviews for Foodborne Disease Detection and Risk Analysis", under submission

Members

Tarun Kulshrestha, Shan Jiang, Yanni Yang, Chenyang Zhao.

Previous members include Wengen Li, Yanwen Wang, Xiulong Liu, Weiwei Fu. Thanks for their contributions.