  We propose how a developing country like Sri Lanka can benefit from
privacy-enabled machine learning techniques such as Federated Learning to
detect road conditions using crowd-sourced data collection and proposed the
idea of implementing a Digital Twin for the national road system in Sri Lanka.
Developing countries such as Sri Lanka are far behind in implementing smart
road systems and smart cities compared to the developed countries. The proposed
work discussed in this paper matches the UN Sustainable Development Goal (SDG)
9: "Build Resilient Infrastructure, Promote Inclusive and Sustainable
Industrialization and Foster Innovation". Our proposed work discusses how the
government and private sector vehicles that conduct routine trips to collect
crowd-sourced data using smartphone devices to identify the road conditions and
detect where the potholes, surface unevenness (roughness), and other major
distresses are located on the roads. We explore Mobile Edge Computing (MEC)
techniques that can bring machine learning intelligence closer to the edge
devices where produced data is stored and show how the applications of
Federated Learning can be made to detect and improve road conditions. During
the second phase of this study, we plan to implement a Digital Twin for the
road system in Sri Lanka. We intend to use data provided by both Dedicated and
Non-Dedicated systems in the proposed Digital Twin for the road system. As of
writing this paper, and best to our knowledge, there is no Digital Twin system
implemented for roads and other infrastructure systems in Sri Lanka. The
proposed Digital Twin will be one of the first implementations of such systems
in Sri Lanka. Lessons learned from this pilot project will benefit other
developing countries who wish to follow the same path and make data-driven
decisions.
