Multi-Component V2X Applications Placement in Edge Computing Environment

#Edge #Computing #multi-component #placement #optimization #V2X #vehicular #technology
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Atonomous and connected vehicles are garnering increasing attention from a host of stakeholders that include researchers, car manufacturers and governments. These vehicles are enabled by a set of applications that gather and analyze data from their surrounding environment. Each of these applications, also known as vehicle-to-everything (V2X) applications, has very stringent delay and resource requirements that are needed to realize the envisioned functionality of autonomous vehicles.

 

The proposed multi-component placement approach addresses these challenges by integrating edge computing environment and breaking down the applications into separate components known as services. The placement of these components is realized by an optimization model that considers the limited resources at the edge, delay requirements of vehicular applications, and the resource requirements of vehicular services.


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  • London, Ontario
  • Canada
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  • Co-sponsored by Optimized Computing and Communications (OC2) Laboratory
  • Starts 31 July 2020 03:00 PM UTC
  • Ends 06 August 2020 08:30 PM UTC
  • No Admission Charge

  Speakers

Ibrahim Shaer Ibrahim Shaer

Biography:

My research includes a wide umbrella of topics that fall in the category of applying optimization solutions for networking-related problems. The topics are the placement of vehicular applications and network traffic engineering. To further advance my research journey, I have decided to pursue my PhD, which will give me the opportunity to expand my set of skills to be ready and well-equipped for my future endeavors.

 

In the research context and beyond my current research work, I am interested in the topics pertaining to Industry 4.0 especially applying data mining techniques on the data generated by enabling machinery. Additionally, I am enthusiastic in delving into the theoretical details of optimization and machine learning models and applying them from scratch.