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Location Privacy-Preserving Edge Demand Response (LEDR)

Code and dataset for tackling location privacy-preserving edge demand response (LEDR) problem are presented here comprehensively and systematically.

EDR-Dataset:

Here, we synthesize a new dataset named EDR based on the information from AWS wavelength, Alibaba Cloud, and a real-world dataset EUA, including edge server capacities, edge server coverages, edge server and user locations within the Melbourne CBD area, server start-up and maintenance costs, etc.

  • edge servers: contains datasets of edge server locations and capabilities.
  • users folder: contains datasets of user location and resource demands.

GEES-Code:

The code of GEES is presented systematically. Different stages regarding the LEDR problem are simulated and modeled by respective functions.

Language Version:

Python 3.8.9

Packages:

Libraries including Numpy, Pandas, Scipy, POT, and Math are leveraged for computing, while Matplotlib and Timeit are utilized for figure presentation and time recording.

Quick Set-up:

Following are the packages required during the project of GEES. This is a quick set-up to help you get your environment prepared.

pip install numpy # numpy 1.25.1
pip install pandas # pandas 1.5.3
pip install scipy # scipy 1.11.1
pip install POT # POT 0.9.0
pip install math 
pip install timeit