Simulation is a critically important enabler for the scalable verification and testing of Internet-of-Things (IoT) systems. There has been considerable research in recent years on developing IoT simulators. The existing research nevertheless does not adequately address the optimization of simulators for the competing objectives that one typically has to contend with, e.g., in terms of costs and resources.
The main objective of the project is to upgrade the existing system at Cheetah Networks to make use of Canadian cellular CAT M1 monitored network data to develop innovative QoE analytics that can be used to provide actionable insights. The system will explore applying new techniques to capture in real-time QoE visibility into experiences locally, regionally and nationally. The primary methodologies that we will be employing are based on machine learning and deep learning techniques for data classification, clustering and analysis.
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