LearningCity: Knowledge Generation for Smart Cities

Authors: Dimitrios Amaxilatis, Georgios Mylonas, Evangelos Theodoridis, Luis Diez, Katerina Deligiannidou

Preprint of chapter submitted to "Smart Cities Performability, Cognition, & Security". EAI/Springer Innovations in Communication and Computing. Springer, Cham. arXiv admin note: text overlap with arXiv:2103.16998

Abstract: Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.

Submitted to arXiv on 12 Apr. 2021

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.