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

与目前相比，联网汽车技术有能力为驾驶员提供更高水平的环保意识。因此，实现可靠、无缝和高效的无线接入以支持车辆连接是该安全技术的核心。该项目研究了一种将车载无线网络与觅食理论相结合的方法，觅食理论被广泛用于描述大黄蜂的行为。具体来说，该研究将在车辆网络和觅食花蜜的大黄蜂之间建立相似之处，以便建立一个新的框架来提高联网车辆的性能。这个跨学科的项目将通过指导和培训来自电气与计算机工程和生物科学的研究生做出教育贡献，重点是从代表性不足的群体中识别合格的学生。为了实现可靠高效的车联网架构，本课题研究基于动态频谱接入（DSA）的车联网如何与觅食理论概念相结合。尽管无线网络研究之前着眼于昆虫世界，以了解网络内多个通信节点的实时决策，以实现某种程度的分布式优化，例如蚁群优化，蜂群技术，但所有这些方法都明显依赖于这些物种中发现的高水平的社会依赖和信息交换，以执行这些操作。相反，大黄蜂的特点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，这与汽车网络环境中的节点非常相似。应用数学工具来临时权衡车辆网络节点之间共享的信息，以及预测不久的将来的情况，如自回归移动平均（ARMA）滤波器和卡尔曼滤波器，从未在用于描述大黄蜂行为的模型中使用。因此，这一努力可以通过提供数学工具来影响生物科学，这些工具可以在大黄蜂的信息权衡过程中使用。

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.