INSPIRE: Mingle: Sensing the Interactions of Animals

该 CREATIV 奖的部分资金由计算机与信息科学与工程局计算机网络和系统司的网络技术和系统 (NeTS) 计划、生物科学局综合有机系统和新兴前沿司的动物行为计划、CISE/CNS 司长办公室和 CISE 助理主任办公室资助。尽管对动物相互作用进行了多年的研究，但我们关于这些相互作用的细节的信息，特别是在大群动物中，仍然是有限的。幸运的是，今天的技术可以用来扩展我们在更大范围内追踪动物社交互动的能力。虽然社交网络和传感器网络研究人员之间的合作已经朝着正确的方向开始，但当前的方法尚未提供采取下一个逻辑步骤并推断动物之间的社会互动所需的接近深度和方向信息。主要挑战在于需要平衡动物相互作用信息的准确性与设备本身消耗的能量，最终目标是建立一个有效、长期运行的系统。为此，我们设计了 Mingle，这是一种基于自适应传感器的系统，用于跟踪动物之间的社交互动。 Mingle 的新颖之处在于观察到这种社交互动可以通过监测动物的相对方向和彼此之间的相对距离来跟踪。通过依靠本地信息，Mingle 将本地协作传感与基于动物流动性观察的基于基础设施的解决方案的明智使用相结合，从而优化能源效率。最后，Mingle 整合了真实的应用限制，最终推动节能数据收集。Mingle 有潜力改变教育、科学以及我们如何看待自己的社会。 看到整个动物种群非常详细的社会互动的能力将改变我们理解和研究它们的方式。自动化收集有关动物和人类互动的信息的过程将使行为科学家从收集过程中解放出来，同时提供前所未有的详细程度和规模的数据。 此外，可以设计全新的教育课程，让孩子们以全新的方式参与科学探究。例如，学生将有能力“成为动物”，制定放牧和觅食策略。此外，有关儿童自身社交互动的信息将改变教育研究，使我们能够了解儿童如何在群体中以及通过互动进行学习。 虽然我们在本提案中关注的是动物之间的社交互动，但这项研究的结果可以纳入人类社交网络领域，其中许多人已经携带了富含传感器的智能手机，为人与人之间的互动提供了新的、令人兴奋的社交网络应用程序，基于实际社交互动公开社交网络信息，或测量社交互动以研究社会行为和社会模式。

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.