Outlier Detection Techniques for Localization in Wireless Sensor Networks: A Survey

Abukhalaf, Hala; Wang, Jianxin; Zhang, Shigeng

doi:10.14257/ijfgcn.2015.8.6.10

Cited by 14 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Outlier, which is often caused by malicious attacks, non-line-of-sight (NLoS), hardware malfunction, means abnormal measurements far beyond the normal range. There are numerous attempts that try to detect distance outliers whose distance measurement errors are significantly large and hence are most likely outliers (see [8] for a survey). For instance, Jian et al [7] use triangle inequality to detect outlier distances that are a factor of ten away from accurate measurements.…”

Section: A Noise Reduction Methodsmentioning

confidence: 99%

Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

Nguyen

Georgiou

Suppakitpaisarn

2018

2018 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

Wireless localization methods are often subject to errors due to radio signal fluctuations that are used to estimate inter-device separation distances. We propose a novel method called MLRefine to counter these effects by refining RSS measurement data to obtain more accurate values that can enhance ranging and localization accuracies. MLRefine uses machine learning methods to model the relationship between accurate values and features extracted from in silico RSS values. MLRefine then applies the trained model to features extracted from real RSS measurement values to return a predicted set of refined RSS values. The refined RSS values are shown through computer simulations and real experiments to improve localization accuracy.

show abstract

Section: A Noise Reduction Methodsmentioning

confidence: 99%

Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

Nguyen

Georgiou

Suppakitpaisarn

2018

2018 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

show abstract

“…This table reveals how each defined technique can be applied for outlier detection in WSN based on their characteristics, usability, and drawbacks. [88,190] Spectral The detection performance is highly depending on the choices of features and distance measure Robust to parameter perturbations and good performances with different anomaly scoring metrics [117,191] Gaussian Use of the spatial correlation to determine outlying sensors and event boundaries…”

Section: Spectral Decomposition-based Approachesmentioning

confidence: 99%

A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

et al. 2020

View full text Add to dashboard Cite

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

show abstract

“…In general, most of the existing outlier detection techniques consider outlier distances more than outlier anchors [15]. The works such as [22][23][24][25] consider distance measurement outliers, the study in [22] showed that triangle inequality had some limitations and then designed and to identify outlier distance measurements beyond triangle inequality.…”

Section: Related Workmentioning

confidence: 99%

Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Abukhalaf¹,

Wang

Zhang

2016

IJFGCN

Self Cite

View full text Add to dashboard Cite

show abstract

Outlier Detection Techniques for Localization in Wireless Sensor Networks: A Survey

Abstract: In wireless sensor networks (WSNs)

Cited by 14 publications

References 35 publications

Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Contact Info

Product

Resources

About