Intelligent and Secure Wireless Sensor System for Monitoring Cattle on Farms

Researchers will develop a robust, antifragile AI-driven wireless sensor system that can adapt, evolve, and grow stronger under high uncertainty, dynamics, threats, and failures to ensure early detection of diseases and prevent or mitigate pathogen spread.

Funded by CCI Southwest Virginia Node

Project Investigators

• Principal Investigator (PI): Sook Shin Ha, Virginia Tech Department of Electrical and Computer Engineering
• Co-PI: Jin-Hee Cho, Virginia Tech Department of Computer Science
• Co-PI: Dong Ha, Virginia Tech Department of Electrical and Computer Engineering
• Co-PI: Neil E. Brown, Virginia State University Department of Agriculture

Rationale

The rapid integration of AI-based monitoring systems in smart farms offers tremendous potential for improving livestock management, particularly in disease detection and animal welfare. 

However, these systems face significant biosecurity challenges, such as AI vulnerabilities, cross-farm contamination, and targeted bioterrorism. 

Building on these goals, integrating AI-driven monitoring systems is about leveraging technology for disease detection and addressing the pressing biosecurity risks such as AI vulnerabilities, contamination across farms, and bioterrorism threats. 

Projected Outcomes

Researchers propose a framework called FragileGuard, an antifragile AI-driven wireless sensor system designed to detect disease. 

They’ll fortify smart farm environments, especially for cattle, by creating secure AI models that withstand cyber and adversarial threats, safeguarding both animal health and the broader food supply chain. 

By embracing the anti-fragility principle, this research is geared toward developing adaptive AI systems that thrive under dynamic and uncertain conditions, ultimately reinforcing the resilience of smart farms.