A Pork Traceability Framework Based on Internet of Things

  • Baocai Xu
  • Jingjun LiEmail author
  • Yun Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 387)


It is essential to trace the source of food for security reasons. In this paper, a traceability framework is proposed and implemented for pork industry. We apply the technologies from Internet of Things such as remote monitoring, sensor network, and data mining to create a chain covering the whole process of pork production. The traceability information can also be accessed through the Internet and any costumer could find out where the pork he/she is buying comes from and how it comes. This paper is mainly focus on the implementation side of the traceability and how to preserve related information along the production process. Our experiments show that the proposed framework can implement the pork traceability efficiently.


Traceability Internet of things 2D barcode Data mining 



This research is supported by jiangsu science and technology support item under Grants No.BE2011398.


  1. 1.
    Xie, J.F., Lu, C.H., Li, B.M., Wang, L.F., Shi, Y., Xue, Q.K., Li, B.S.: Development of monitoring and traceability system for pork production. World Engineers Convention, Shanghai, China, pp. 2−6 (2004) Google Scholar
  2. 2.
    Hobbs, J.E., Von Bailey, D., Dickinson, D.L., Haghiri, M.: Traceability in the Canadian red meat sector: do consumers care? Can. J. Agric. Econ. 53, 47–65 (2005)CrossRefGoogle Scholar
  3. 3.
    Cao, J., Wu, Z., Mao, B., Zhang, Y.: Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web J. Internet Web Inf. Syst. (2012). doi: 10.1007/s11280-012-0164-6 Google Scholar
  4. 4.
    Arana, A., Sovet, B., Lasa, J.: Meat traceability using DNA markers: application to the beef industry. Meat Sci. 61, 367–368 (2002)CrossRefGoogle Scholar
  5. 5.
    Wu, Z., Mao, B., Cao, J.: MRGIR: open geographical information retrieval using MapReduce. In: The 19th International Conference on GeoInformatics (GeoInformatics 2011), Shanghai, China, June 2011Google Scholar
  6. 6.
    Buckley, J.: From RFID to the Internet of things—pervasive networked systems. In: European Commission, DG Information Society and Media, Networks and Communication Technologies Directorate, Brussels (2006)Google Scholar
  7. 7.
    Xiong, B.H.: A Comprehensive Technical Platform for Precision Feeding of Dairy Cattle, pp. 51–57. Chinese Agricultural Science &Technology Press, Beijing (2005). (in Chinese)Google Scholar
  8. 8.
    Cao, J., Wu, Z., Wang, Y., Yi, Z.: Hybrid collaborative filtering algorithm for bidirectional web service recommendation. Knowl. Inf. Syst. (2012). doi: 10.1007/s10115-012-0562-1 Google Scholar
  9. 9.
    Schmidt, O., Quilter, J.M., et al.: Inferring the origin and dietary history of beef from C, N and S stable ration analysis. Food Chem. 91, 545–549 (2005)CrossRefGoogle Scholar
  10. 10.
    Cunningham, E.P., Meghen, C.M.: Biological identification systems: genetic markers. Sci. Tech. Rev. 20(2), 491–499 (2001)Google Scholar
  11. 11.
    Buitkamp, J., Ammer, H., Geldermenn, H.: DNA fingerprinting in domestic animals. Electrophoresis 12, 169–174 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Jiangsu Yurun Food Industry Group Co., LtdNanjingChina

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