This CREATIV award is partially funded by the Networking Technologies and Systems (NeTS) program in the Division of Computer Networks and Systems in the Directorate of Computer & Information Science & Engineering, the Animal Behavior program through the the Divisions of Integrative Organismal Systems and Emerging Frontiers in the Directorate of Biological Sciences, the Office of the Division Director in CISE/CNS and the Office of the Assistant Director in CISE. Despite many years of research on animal interactions, our information about the details of those interactions, especially in large groups of animals, is limited. Fortunately, today's technology can be used to extend our ability to track the social interactions of animals to a much larger scale. While collaborations between social networking and sensor networking researchers have started in the right direction, current approaches have not provided the depth of proximity and orientation information necessary to take the next logical step and infer social interactions between animals. The main challenge lies in the need to balance the accuracy of information about the animal interactions with the energy consumed by the devices themselves, with the ultimate goal of an effective, long running system. To this end, we have designed Mingle, an adaptive sensor-based systems that tracks social interactions between animals. The novelty of Mingle comes from the observation that such social interactions can be tracked by monitoring the animals' relative orientation and relative distance to each other. By relying on local information, Mingle optimizes energy efficiency by integrating local collaborative sensing with the judicious use of infrastructure-based solutions based on observations about the mobility of the animals. Finally, Mingle integrates real application constraints to ultimately drive energy-efficient data collection.
Mingle has the potential to change education, science, and how we view our own society. The ability to see the very detailed social interactions about an entire population of animals will change how we understand and study them. Automating the process of the collection of information about animal, and human, interactions will free behavioral scientists from the collection process while providing data at the level of detail and magnitude never before possible. Moreover, entirely new educational curricula can be designed that engage children in scientific inquiry in fundamentally novel ways. For example, students will have the ability to "become the animals", enacting herding and foraging strategies. Additionally, information about the children's own social interactions will change educational research, enabling our understanding of how children learn in a group and through interactions. While we focus on social interactions between animals in this proposal, the results from this research can be taken into the human social networking domain, where many people already carry sensor-rich smartphones, enabling new and exciting social networking applications for interactions between people, exposing social networking information based on actual social interactions, or measuring social interactions to research social behavior and social patterns.
