Sensor network data fault types

By Kevin Ni, Nithya Ramanathan, M. Chehade, Laura Balzano, Sheela Nair, Sadaf Zahedi, Eddie Kohler, G. Pottie, Mark H. Hansen, Mani B. Srivastava. TOSN, 2009

442 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/1176f16cd808370f12a2d6858b3359493a15718f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Min Chen
2. Giancarlo Fortino
3. Ramesh Govindan
4. Leana Golubchik
5. Antonio Liotta

Citations in the corpus

• Smart Anomaly Detection in Sensor Systems (2020, background): “At this aim, authors in [24] present a more detailed taxonomy of sensor faults, with more categories, as well as possible causes and implications.”
• Sensor faults: Detection methods and prevalence in real-world datasets (2010, methodology): “The three fault types (SHORT faults, NOISE faults, and CONSTANT faults), that we focus on in this paper, cause the faulty sensor readings to deviate from the normal pattern exhibited by true (or non-faulty) sensor readings, and are derived from a “data centric” view of sensor faults [Ni et al. 2008]”