Sustainable Vehicle-Assisted Edge Computing for Big Data Migration in Smart Cities

Abstract

Smart cities are based on connected devices generating large quantities of data every instant. These data can be stored at a nearby edge location for initial processing but later sending the data to the backend data centers for storage and further analysis consumes considerable network bandwidth. In this article, we propose a large-scale data migration framework using vehicles. The framework uses a neural network to identify suitable vehicles as data mules, ones moving toward the data destination, potentially reducing the load from backend networks in terms of bandwidth usage and overall energy consumption. We compare the framework with data transfers using the traditional Internet and an approach without machine intelligence. The proposed framework performs well in terms of data loss, transfer time, energy, and CO2 emissions. From experiments, we demonstrate that the approach achieves a 67% success rate with data transfers $193\times $ faster than the average Internet bandwidth of 21.28 Mb/s. Moreover, the resulting CO2 emissions for 30-TB data transfers stood at 6.403 kg, which is significantly lower compared to 1172.8 kg for the Internet.