| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Ceara, Dept Engn Teleinformat, Grp Redes Comp Engn Software & Sistemas GREat, BR-60020181 Fortaleza, Ceara - Brazil
[2] Univ Fed Alagoas UFAL, Inst Comp, BR-57072900 Maceio, Alagoas - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | SENSORS; v. 19, n. 20 OCT 2 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
Here we propose an online method to explore the multiway nature of urban spaces data for outlier detection based on higher-order singular value tensor decomposition. Our proposal has two sequential steps: (i) the offline modeling step, where we model the outliers detection problem as a system; and (ii) the online modeling step, where the projection distance of each data vector is decomposed by a multidimensional method as new data arrives and an outlier statistical index is calculated. We used real data gathered and streamed by urban sensors from three cities in Finland, chosen during a continuous time interval: Helsinki, Tuusula, and Lohja. The results showed greater efficiency for the online method of detection of outliers when compared to the offline approach, in terms of accuracy between a range of 8.5% to 10% gain. We observed that online detection of outliers from real-time monitoring through the sliding window becomes a more adequate approach once it achieves better accuracy. (AU) | |
| FAPESP's process: | 15/24544-5 - Data sampling in wireless sensor networks: integrating applications through the internet |
| Grantee: | André Luiz Lins de Aquino |
| Support Opportunities: | Regular Research Grants |