Edge preprocessing for machine learning in the cloud

• Grant number: 23/05124-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: June 01, 2023
• End date: April 30, 2026
• Field of knowledge: Interdisciplinary Subjects
• Agreement: BG E&P Brasil (Shell Group)
• Principal Investigator: Emílio Carlos Nelli Silva
• Grantee: Antonio Carlos Daud Filho
• Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Company: Universidade de São Paulo (USP). Escola Politécnica (EP)
• Associated research grant: 20/15230-5 - Research Centre for Greenhouse Gas Innovation - RCG2I, AP.PCPE

Abstract

In this proposal, a combination of edge and cloud framework will be adopted, implemented and tested in the context of GHG emissions data collection and analysis. The critical issue of the proposed edge cloud architecture investigated in this project is that it should provide a discussion of the division of tasks that must be performed in the cloud and the tasks implemented close to the user or data source at the edge of the network. The target processes of the study are trajectory planning algorithms for autonomous aerial exploration using robotic platforms. The proposed planner employs an optimization scheme with a decreasing horizon: in an online environment, the path to be taken must optimize the search, observing quantities of interest such as computed trails, temperature, pollution, deforestation, etc. to find the best route, the quality of which is determined by the amount of unmapped space that can be explored, taking into account the aircraft range restrictions. The proposed planner must be able to be processed online, aboard a UAV with limited resources. Its high performance is evaluated in detailed simulation studies as well as a challenging real-world experiment using a helicopter micro air vehicle. Analysis of the computational complexity of the algorithm must also be analyzed to verify the proposal's ability to deal with large-scale issues. The system must take into account the capacity of data storage, communication and computational division of tasks with a base station and also with cloud storage and processing technology. (AU)