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https://web.cvent.com/event/f9fa585e-9809-4883-b816-4409768c0e0e/websitePage:18647cf2-f444-46d9-8b37-39c723ad529e

A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks

Description: This study aims to keep vehicle connectivity reliable and persistent by designing an artificial intelligence (AI)-assisted framework for automated, adaptive channel switching when severe performance degradation is detected. The research team will achieve their goals via developing the following: (1) A testbed environment for generating and collecting data for training and testing purposes. The team will identify all performance degradation causes that are of interest, deploy transceivers in the vehicles, and test under real or synthetic conditions to collect and record data. (2) A set of machine learning based models for detecting and classifying performance degradation conditions. The team will construct machine learning based models from a variety of supervised machine learning algorithms for detecting performance degradation and classifying it. The team will use the data collected from the testbed environment to train and test the models. (3) An automated AI-assisted framework for integrating determination of performance degradation cause and selection of best channel switching strategy. The team will study how the transceivers can discover and agree on the available channels, and then investigate and establish the relationship between the cause of performance degradation and the channel switching strategy that can best mitigate the cause. An appropriate threshold for performance drop will be found and used as the triggering mechanism of channel switching. Intellectual Merit: In this study, the team proposes to construct a machine learning based model to detect and determine the most likely cause of performance degradation. Currently, there is a lack of an effective mechanism that can quickly switch to a secondary channel when the performance of vehicular communication drops severely to guarantee the quality of performance. The team aims to develop an automated framework that will make use of the learned cause to adaptively select the optimal channel switching strategy to mitigate the performance drop. Their ongoing project shows that there are different patterns of performance degradation that can be attributed to different causes, and researchers have designed some channel switching strategies. The approach will apply AI techniques to automate the procedure to ensure the reliability of vehicular connectivity. Broader Impacts: Machine learning models for detecting performance degradation and determining the most likely cause will be constructed. An adaptive framework that integrates a machine learning model and automated channel switching will be developed and applied in vehicular communication applications. The team anticipates that their models will be a significant step towards modeling more uncertainty of various new causes that will lead to performance degradation in vehicular network communications. Thus, their models would likely be adopted by many researchers and industries to make their applications robust and adaptive to performance degradations.