Remote Imaging of Community Ecology via Animal-borne Wireless Networks

Abstract:

This project features conception, design, and deployment of a wireless network of embedded devices, for monitoring the behavior of animals in the wild.  The system is being deployed and tested in biologically relevant scenarios. This project has been the first to deploy animal-borne wirelessly networked devices that are capable of providing not only geo-location data, but also of executing cooperative strategies that save battery-life by selectively recording bandwidth-intensive audio and high-definition video footage of occurrences of animal group behavior of interest, such as predation. In addition to enabling autonomous video capture, the wireless network registers the relative positions of the animals and other sensory information that will be useful in sociobiological characterizations. This project is addressing three primary technical challenges:

1. Investigating methods to design and analyze the performance of distributed algorithms that implement autonomous decisions (for video capture) at the mobile agents, subject to communication and computational constraints.  We have proposed a new class of distributed state estimation algorithms, and characterized networks properties under which asymptotic state omniscience can be achieved. We have also shown that ours is the most general class of algorithms for which asymptotic state omniscience can be achieved. Our algorithms have been implemented in our systems to facilitate event-driven distributed decision making.
2. We are pursuing data-driven fundamental research on the modeling of animal group motion for multiple sociobiological configurations that will promote a formal understanding of the mechanisms of social interaction.  We have developed an analytically tractable model of pursuit and evasion in the plane in order to study individual evasive strategies and group-level dynamics for a heterogeneous herd under pursuit from a single predator. Heterogeneity in individual maximum speeds is used to represent variation in age and ability among herd members. We show that a pursuer strategy of optimal target selection guarantees capture of an individual in bounded time for both global and local sensing regimes. We propose evasion strategies and prove conditions under which they guarantee capture avoidance. Properties of networks to identify leaders and collective decision-making principles have been identified.
3. This project has a significant component of applied research on methods for hardware integration for building distributed networks of embedded devices that are capable of executing our newly developed algorithms, subject to power and weight constraints. In collaboration with Gorongoza National Park (Mozambique), we have performed our second deployment of 13 units of last-generation devices on two animal species. The rich variety and volume of collected data will enable the investigation of unique animal group behavior as various species return to their habitat after a long civil war. We will also perform data-driven performance validation and optimization of our system for future deployments.

We have participated in outreach activities and integrated various undergraduate research projects throughout the duration of the grant. More details about the technical progress, publications and other activities can be found in the poster and video.