The use of Synthetic Data to solve the scalability and data availability problems in Smart City Digital Twins

TL;DR

This paper proposes using synthetic data to enhance the scalability and data availability of Smart City Digital Twins, demonstrated through a NO2 pollution case study, reducing costs and risks of real-world experimentation.

Contribution

It introduces a synthetic data approach to address data scarcity and scalability issues in Digital Twins for Smart Cities, enabling safer and more cost-effective experimentation.

Findings

Synthetic data can effectively simulate localized Smart City data.

The approach reduces costs associated with data collection.

Successful proof-of-concept with NO2 pollution data.

Abstract

The A.I. disruption and the need to compete on innovation are impacting cities that have an increasing necessity to become innovation hotspots. However, without proven solutions, experimentation, often unsuccessful, is needed. But experimentation in cities has many undesirable effects not only for its citizens but also reputational if unsuccessful. Digital Twins, so popular in other areas, seem like a promising way to expand experimentation proposals but in simulated environments, translating only the half-baked ones, the ones with higher probability of success, to real environments and therefore minimizing risks. However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection. We present an alternative based on synthetic data that given some conditions, quite common in Smart Cities, can solve these two problems together with a proof-of-concept based on NO2 pollution.