Internet users from Canadian rural and remote communities suffer from frequent Internet interruptions, which generally result from various network issues. The lack of human resources, expertise and support make these issues difficult to identifyand fix. Remote areas lack responsive and cost-effective operations or maintenance efforts.
Professor Yeying Zhu and her collaborators will use today’s artificial intelligence (AI) and machine learning (ML) technologies to develop data-driven approaches to an automated diagnosis and trouble shooting process of network operational issues for remote communities in Canada. This includes AI-assisted solutions for Internet fault detection and the use of unmanned aerial vehicles (drones) for fault compensation in under-served and under-connected areas. The solutions developed as part of the project will lead to self-maintainable and zero-touch management for future community networks.
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