Edge-Twin based Framework for Real-Time AI Applications for Vehicular Scenarios

Edge computing is expected to play a transformative role for future AI applications in 5G networks by bringing cloud-style resource provisioning closer to the devices that have the data. Instead of running resource-intensive AI applications at the end devices, we can consolidate their execution at the edge, which brings many benefits, such as eliminating the redundant task processing, running machine learning (ML) tasks with sizeable data sets and running ML tasks in a spatial context that is shared by many devices. The downside of using edge computing for AI is the latency of transporting the data from the end device (e.g., a vehicle) to the edge servers and processing it there and getting the results back to the end device. This project will focus on developing a novel edge twin concept that extends the digital twin idea to maintain computing state at edge computing systems. The objective of edge twin is to model and control a complex system of IoT in a highly responsive manner. To achieve this objective, we will investigate many ideas, and one of them is time shifting.

Intern: 
Mahmoud Kamel;Richard Olaniyan;Olamilekan Fadahunsi;Tianzi Yang
Faculty Supervisor: 
Muthucumaru Maheswaran
Province: 
Quebec
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