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Title	Towards An Optimization Model For Outlier Detection In Iot-Enabled Smart Cities
ID_Doc	58019
Authors	Tahiri Alaoui M.L.; Belhiah M.; Ziti S.
Year	2023
Published	Lecture Notes in Networks and Systems, 712 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-35251-5_32
Abstract	In a connected world, the growing attention given to the IoT (Internet of Things) is driven by its economic, societal, and ecological impact among others as well as its vast applications and services. The insights provided by these applications and services are based on the data gathered from different networks of IoT sensors. A poor quality of data forwarded to control centers may lead to ill-informed decisions, inadequate services, and thus impact adversely the business objectives. In this paper, we will address the parameters that influence the levels of Data Quality (DQ) in the context of IoT devices. These issues may be due to errors in measurements, precision of data collection devices, energy restrictions, intermittent connectivity, interference with other devices, sampling frequency, noisy environment, and data volume among others. On the other hand, DQ levels are evaluated against a set of dimensions. Herein, we will focus our research on the most important dimensions for end users, such as accuracy, completeness, and timeliness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Data quality; Internet of Things; Outlier Detection

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