Edinburgh Napier University

  With the excessive rise in the environmental pollution in cities, governments have begun to make global agreements to reach zero carbon emission levels. Though collecting data from IoT sensors on smart cities helps to analyze and predict carbon emissions up to some extent, it fails to put a pathway on how to balance carbon emissions in the long term. This is mainly because a variety of domains produce data in multi contexts, which is an inconvenience for ML algorithms and leads to low prediction results. Thus, societies and academicians turn towards the Digital Twin concept with the high-precision collection, very large volumes of data, and tiny instant communication goals. Nevertheless, there is still a considerable gap in defining a data model in which related telemetry data is obtained through a narrow bandwidth IoT network. Consequently, we propose eCityGML, extended with YANG data model on top of the traditional CityGML data model. The preliminary results show that eCityGML gives low latency on a large volume of data to enable tiny instant communication.