NSF Award
2319487
Mobile Internet measurement is critical to network design, resource allocation, and troubleshooting network issues. However, sharing of mobile Internet measurement data can potentially compromise user privacy. Given the wide introduction of artificial intelligence to mobile Internet measurement and traffic analytics, there is an urgent need for data sharing solutions that provide explainability in terms of the trade-offs among data quality, utility and quantity. To close the gap, the objective of this project is to develop new methods to augment data with explainable data quality and utility, to access and share collected data in a privacy-preserving manner, and to collaboratively analyze Internet data with intelligence and autonomy.<br/><br/>This collaborative project brings together investigators from University of Nebraska-Lincoln, Utah State University, and University of Wisconsin-Madison. It aims to lay a solid foundation for mobile Internet measurement with privacy preservation, collaborative and distributed intelligence, and autonomy. Methodologies and methods will be developed for quality-explainable data synthesis and augmentation; privacy-preserving data sharing; and collaborative and privacy-preserving analysis of Internet measurement data. Moreover, a mobile Internet traffic generator will be developed for evaluating the proposed methods. This project can significantly advance the prior research in Internet traffic analytics, quality-explainable and privacy-preserving data processing, mobile Internet traffic analytics, distributed artificial intelligence and machine learning algorithms, optimizations, modeling, simulations, and testbed experiments. <br/><br/>The research efforts associated with this project will greatly advance the understandings of the critical issues in the next-generation mobile Internet measurement with distributed and collaborative intelligence to provide privacy-preserving data sharing and Internet traffic analytics. The outcomes of the project can potently foster the transition of our society into data sharing with privacy and intelligent era. Research and education will be integrated in this project by introducing emerging mobile Internet measurement and privacy-preserving data processing with advanced topics such as 6G wireless systems, data augmentation, artificial intelligence and machine learning models into the current curricula in the three collaborative institutions.<br/><br/>The project website is hosted at: cns.unl.edu/imr-ppds. The collected data, simulation codes, and publication list will be published on the project website. Copies of technical reports and accepted manuscripts will also be published on the project website. The website will be maintained during the project years, and remain accessible for least 2 years after the completion of the project.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
