A Case Study of CPNS Intelligence: Provenance Reasoning over Tracing Cross Contamination in Food Supply Chain

A Cyber-Physical System (CPS) is a system featuring a tight combination and coordination of the system's computational and physical elements. CPS integrates the executive ability of the physical world and the intelligence of the cyber world to add new capabilities to real-world physical systems. Recent years has witnessed the thriving of various applications in Cyber-Physical Networked System (CPNS), one of which is food distribution industry. Food supply chain is a typical case of model of networked systems. As food safety is becoming an increasing concern over the world, assurance in the quality and trace ability in food supply chain is essential. While data collection of food is available with CPNS, intelligent sensing and process in CPNS is insufficient, e.g., though it is easy to trace the origin of food, finding the source of cross contamination is an unsolved critical issue. In this paper, a case study of CPNS intelligence is presented to provide solutions for ceasing outbreaks of food borne disease. As the case of provenance reasoning, a heuristic approach to tracing cross contamination is studied, which is comprised of dynamic partition sampling strategy and heuristic tracing algorithm. With satisfactory performance and accuracy results for our approach in our simulation, we further suggest strategies regarding provenance reasoning to address the challenges of provenance as an open issue in cloud computing and domain specific intelligence (D.S.I) in Internet of Things (IoT) and CPS.