EARS: Adaptive Behavioral Responses for Dynamic Spectrum Access-Based Connected Vehicle Networks

与当今相比，联网车辆技术能够为司机提供更高水平的环境意识。因此，实现可靠、无缝和高效的无线接入以支持车辆连接是这项安全技术的核心。该项目研究一种将车载无线网络与觅食理论相结合的方法，觅食理论是一个被广泛用来描述大黄蜂行为的概念。具体地说，这项研究将把车辆网络和大黄蜂觅食花蜜相提并论，以建立一个提高联网车辆性能的新框架。这个跨学科的项目将通过指导和培训电气与计算机工程和生物科学的研究生来做出教育贡献，重点是从代表不足的群体中识别合格的学生。为了实现可靠和高效的互联车联网架构，该项目研究如何将基于动态频谱接入(DSA)的车载网络与觅食理论概念相结合。尽管无线网络研究以前已经从昆虫世界中寻找关于网络内多个通信节点之间的实时决策的洞察力，以便实现某种程度的分布式优化，例如，蚁群优化、蜜蜂群技术，但所有这些方法都严重依赖于在这些物种中发现的高度的社会依赖性和信息交换，以便执行这些操作。相反，大黄蜂的特征是与其他大黄蜂分享过去和现在的信息，但仍然能够做出独立的决定，这非常类似于车辆网络环境中的节点。用于对车联网节点之间共享的信息进行时间权衡以及预测近期条件的数学工具的应用，如自回归移动平均(ARMA)过滤器和卡尔曼过滤器，从未被用于描述大黄蜂行为的模型。因此，这一努力可能会对生物科学产生影响，因为它提供了可以在熊蜂信息衡量过程中使用的数学工具。

Connected vehicle technology has the ability to provide drivers with a significantly higher level of environmental awareness relative to the present day. Thus, enabling reliable, seamless, and efficient wireless access to support vehicular connectivity is core to this safety technology. This project studies an approach that combines vehicular wireless networking with foraging theory, a concept that is extensively employed to describe the behavior of bumblebees. Specifically, the research will draw parallels between vehicular networks and bumblebees foraging for nectar in order to establish a novel framework for enhancing the performance of connected vehicles. This interdisciplinary project will make an educational contribution via the mentorship and training of graduate students from both Electrical & Computer Engineering and the Biological Sciences, with an emphasis on identifying qualified students from underrepresented groups.In order to achieve a reliable and efficient connected vehicle networking architecture, this project studies how dynamic spectrum access (DSA)-based vehicular networks can be combined with foraging theory concepts. Although wireless networking research has previously looked to the insect world for insights on real-time decision-making across multiple communication nodes within a network in order to achieve some level of distributed optimization, e.g., ant colony optimization, honeybee swarm techniques, all of these approaches significantly depend on the high level of social dependency and information exchange found in these species in order to perform these operations. Conversely, bumblebees have been characterized as socially sharing past and present information with other bumblebees, but are still capable of making independent decisions, which is very similar to nodes within a vehicular networking environment. The application of mathematical tools used to temporally weigh the information shared between vehicular networking nodes as well as predict conditions in the near-future, such as autoregressive moving average (ARMA) filters and Kalman filters, have never been employed in models used to describe bumblebee behavior. Consequently, this effort could make an impact on biological sciences by providing mathematical tools that can be employed during the information weighing process of bumblebees.