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

互联汽车技术能够为驾驶员提供比当今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。这个项目研究了一种方法，结合车载无线网络与觅食理论，这是一个广泛用于描述熊蜂行为的概念。具体来说，该研究将在车辆网络和大黄蜂觅食花蜜之间进行比较，以建立一个新的框架来提高联网车辆的性能。该跨学科项目将通过指导和培训电气计算机工程和生物科学的研究生，重点是从代表性不足的群体中识别合格的学生，从而为教育做出贡献。为了实现可靠和高效的连接车辆网络架构，该项目研究如何将基于动态频谱接入（DSA）的车辆网络与觅食理论概念相结合。尽管无线网络研究以前曾着眼于昆虫世界，以了解网络内多个通信节点之间的实时决策，以实现某种程度的分布式优化，例如，蚁群优化、蜜蜂群技术，所有这些方法显著地依赖于在这些物种中发现的高水平的社会依赖性和信息交换，以便执行这些操作。相反，大黄蜂的特点是与其他大黄蜂共享过去和现在的信息，但仍然能够做出独立的决策，这与车辆网络环境中的节点非常相似。用于在时间上对车辆联网节点之间共享的信息进行加权以及预测不久的将来的条件的数学工具（诸如自回归移动平均（阿尔马）滤波器和卡尔曼滤波器）的应用从未在用于描述大黄蜂行为的模型中采用。因此，这项工作可以通过提供可以在大黄蜂的信息加权过程中使用的数学工具来对生物科学产生影响。

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.