New adaptive techniques for Location of Things data quality management
Location of Things (LoT) is an Internet of Things paradigm for mobility analytics. In LoT, massive mobility data is being gathered, processed and transmitted among heterogeneous data nodes in a decentralized architecture. Thus, managing data quality for LoT has become a prominent challenge as traditional techniques cannot cope with the aforementioned characteristics of LoT. In our project MALOT, we aim at designing a set of new techniques to manage data quality for LoT effectively and efficiently. MALOT includes
- a core model for assessing mobility data quality at individual LoT nodes;
- effective data enhancement algorithms based on the quality model for resolving data heterogeneity and inconsistency;
- a task scheduling mechanism for improving overall efficiency of data quality management in LoT.
Info
Principal Investigator: Huan Li
Project Supervisor: Prof. Hua Lu
Host: Aalborg University
This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No.882232.