SIRG: COLLABORATIVE RESEARCH: DeerNet-Wireless Sensor Networking for Wildlife Behavior Analysis and Interaction Modeling

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AbstractOne of the greatest challenges in understanding the role of free-ranging wildlife in maintaining diversity, tracking invasive species, and the spread of emerging diseases is obtaining unobtrusive visual information required in studying the behaviors and interactions of wildlife species in their environment. The state-of-the-art wildlife monitoring technologies, including radio-tracking and wireless sensor networks, do not provide sampling of animal resource selection, behavior patterns, and the environmental context of the animal's behavior. The central objective of this interdisciplinary award is to bring the video monitoring capability to wireless sensor networks so as to collect important visual information for wildlife behavior analysis and interaction modeling. The overall goal of the research is to develop a long-lived and unobtrusive wildlife video monitoring system capable of real-time video streaming with remote control capability. The captured video in real time will be transmitted over wireless sensor networks to a remote monitoring center for real-time viewing and camera control. Because real-time transmission requirements are particularly challenging to wireless sensor network design, the research will address important issues on energy minimization and performance optimization in video sensing over mobile wireless sensor networks. The PIs will engineer a portable, low-energy, rugged, wireless network video/GPS/motion sensor coupled with an optimized transmission protocols and routing schemes for transferring the video images through sensor node design, access control, and robust routing protocol. Deer-net is a highly innovative interdisciplinary engineering, computer science, and wildlife science project in the field of cognitive ecology. The video monitoring has the potential to significantly advance this area of science and engineering and has broader application to other fields including surveillance, security, process monitoring, and other industrial applications. The broader impacts will arise from multi-disciplinary collaboration among computer scientists, engineers, and wildlife biologists; participation by students spanning undergraduate to doctoral levels; integration into ongoing courses; recruitment of under-represented groups; and educational opportunities for K-12 students to observe wildlife behavior.