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

Worcester Polytechnic Institute, Worcester MA

Investigators

Abstract

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.