Towards an AI-Driven Data Reduction Framework for Smart City Applications

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Pioli, L (Author) Other; de Macedo, DDJ (Author) Other; Costa, DG (Author) FEUP; Dantas, MAR (Author) Other

Journal

Title: Sensors

Serial No. 22 Vol. 24 No. 9569

Final page: 358

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-PVC

DOI: 10.3390/s24020358

Abstract (EN): The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20