2026-04-13T01:13:24Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4422522026-03-25T10:51:27Zcom_2072_1033col_2072_452950

ESOD: an Edge Streaming Data Outlier Detection framework for IoT platforms Allka, Xhensilda Ferrer Cid, Pau Barceló Ordinas, José María García Vidal, Jorge Avila Torrado, Antonio Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica Edge computing Edge intelligence (EI) Internet of Things Outlier detection (OD) Streaming data Intelligent computing at the IoT edge allows tasks that are normally performed in the cloud to be performed at the IoT node, enabling low-latency applications and more efficient control and management of services. Among the tasks that can be performed on the IoT edge is improving data quality, such as outlier detection (OD). This task is challenging because IoT nodes have fewer computational and storage resources than the cloud. In this article, we propose an OD framework adapted to IoT edge nodes that is lightweight, consumes few resources, adapts to changes in signal trends, and has the ability to provide real-time responses. The framework presented is based on the use of two windows. A sliding window for the current data, which captures the short-term changes in the signal, and a window that stores a summary of historical values, which captures whether the values are within the long-term range of the signal. We show how models using two windows reduce the number of false positives compared to models using only one window. A version for near real-time applications is also proposed, which improves detection by identifying trend changes in the signal at the cost of delaying the decision by a few samples. This work is supported by Grant PID2022-138155OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, CDTI MIG-20221061, by regional projects 2021CDTI and 2023 CLIMA 0097, and with the support of Secretaria d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu. Peer Reviewed Postprint (author's final draft) 2025-06-19 Article https://ieeexplore.ieee.org/document/11045333 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-138155OB-I00/ES/TECNICAS BASADAS EN DATOS PARA MEJORAR LA CALIDAD DE LA INFORMACION EN REDES DE NODOS IOT/ Open Access Institute of Electrical and Electronics Engineers (IEEE)