Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey

Shahid, Nauman; Naqvi, Ijaz Haider; Qaisar, Saad

doi:10.1007/s10462-012-9370-y

Cited by 85 publications

(61 citation statements)

References 73 publications

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“…Generally, the backbone of any event detection approach is outlier detection, which consists of observations of significant differences from a normal dataset of values [39]. Outliers could be noise or sensor faults.…”

Section: Outlier Detection Algorithmmentioning

confidence: 99%

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

Khan

2017

Applied Sciences

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Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner's localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH 4 , CO 2 , and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner's localization (with <1.8 m of error) in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines.

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Section: Outlier Detection Algorithmmentioning

confidence: 99%

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

Khan

2017

Applied Sciences

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“…1) Selecting the abnormal eigenvalue To determine the sequence similarity degree of the adjacent nodes for the anomaly detection sensor, we used the distance of the corresponding measurements between the nodes [2]. However, we observed that CO 2 leakage caused by the change in the sensor does not have a one-to-one relationship, as shown in Figure 6.…”

Section: Spatial-temporal Abnormal Judgmentmentioning

confidence: 99%

“…However, the data measured and collected by WSNs is sometimes unreliable because of the resource containments of the nodes or status changes in the monitoring object. Abnormal data in sensor networks can be divided into abnormal points called "outliers" and abnormal events called "events" [2]. Abnormal points are a result of resource limitations of WSNs and sensor nodes in poor environments, which often lead to node failure and therefore result in abnormal data [3].…”

Section: Introductionmentioning

confidence: 99%

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The Spatial-Temporal Anomaly Detection Algorithm in Wireless Sensor Networks

Liu

Zhang

2015

TELKOMNIKA

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Keywords: wireless sensor networks, spatial-temporal anomaly, data streamCopyright © 2015 Universitas Ahmad Dahlan. All rights reserved. IntroductionIn recent years, wireless sensor networks (WSNs) have been applied in many fields, such as environmental and habitat monitoring, object and inventory tracking, health and medical monitoring, battlefield observation, and industrial safety and control [1]. However, the data measured and collected by WSNs is sometimes unreliable because of the resource containments of the nodes or status changes in the monitoring object. Abnormal data in sensor networks can be divided into abnormal points called "outliers" and abnormal events called "events" [2]. Abnormal points are a result of resource limitations of WSNs and sensor nodes in poor environments, which often lead to node failure and therefore result in abnormal data [3]. Abnormal events [4] are often described as a series of abnormal values in a data stream.At present, the methods widely used in sensor anomaly detection are mainly based on several categories, including statistical model technology [5], adjacent degree technology, wavelet analysis technology [6], and cluster technology [7]. The method based on the statistical model is unsuitable for abnormal distribution data. The method based on clustering depends on the number of clusters. The methods based on adjacent degree technology and wavelet analysis are complex.The traffic forecast model [8] uses the correlation coefficient of predicted traffic sequences and the actual flow sequence for anomaly detection. The spatial-temporal correlation characteristics of the sensor data were considered in [9], which used time and spatial correlations to generate outliers. The local outliers are converged to sink for the global outliers. This method is applied only to detect abnormal points, but sometimes, abnormal sequence detection helps to reveal abnormal events that occur. Thus, the time series of anomaly detection was more valuable in [10], which proposed to rapidly compare the similarities of two time series based on the Chebyshev coefficient and found an abnormal time sequence. The literature focused on outlier detection and a single time series of anomaly detection in the sensor data. The spatial-temporal characteristics of the sensor when the abnormal event occurred was overlooked [11].The objective of this study is to identify the phenomenon of CO 2 leakage by analyzing the abnormal readings of sensors. Considering the analysis of CO 2 data streams, we analyze

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“…They do not attempt to distinguish between the erroneous outliers and genuine outliers associated with unusual events [114,116].…”

Section: Outlier Detection For Wireless Sensor Network Data Streamsmentioning

confidence: 99%

Semantic annotation and reasoning for sensor data streams

Gao¹

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School of Information Technology and Electrical Engineeringii Abstract Sensor technologies and wireless sensor networks are enabling the capture and storage of large volumes of sensor data streams. However there are a number of characteristics associated with sensor data streams that hinder the sharing, analysis and re-use of such data on the Web. For example, because sensor data is both temporal and spatial in nature, its multi-dimensionality, combined with variations in granularity, makes it more difficult to analyse and interpret. These issues have created major challenges associated with the management, representation, analysis and indexing of large volumes of sensor data streams. Consequently, there is an urgent need to markup sensor data streams with well-defined semantics to drive the development of advanced applications such as situation awareness, predictive models and event detection. Given well-structured and semantically annotated sensor data streams, it is possible to reason across annotated sensor data streams to deduce new or implicit knowledge, discover significant (and erroneous) data and events and answer complex queries.This thesis focuses on the application area of ecosystem monitoring. As such, it investigates novel solutions to the semantic annotation and reasoning challenges associated with sensor data streams acquired by ecosystem scientists who are monitoring: a) species behaviour and b) micro-climate changes within environmentally-sensitive regions. Within this context, this thesis focuses on the design, implementation and evaluation of innovative methods to tackle different challenges associated with the semantic annotation and reasoning of two classes of sensor data: a) animal accelerometry data streams (acquired via animal-attached tri-axial accelerometers); and b) environmental sensor data streams (acquired from wireless sensor networks). These two categories of sensor data are of particular interest because they are rapidly growing in volume, they present different but similar challenges and there is a need to correlate them in order to determine if changes in the environment are impacting on species behaviour.The first component of the thesis investigates optimum methods of combining domain expert annotations and machine learning to improve the precision and efficiency of semantic annotations on 3D accelerometry data streams (to support animal behaviour recognition and analysis). The second component seeks to minimize the cost and effort involved in developing training corpuses for machine learning approaches, by evaluating an Optimal Graph Learning approach to automatic semantic annotation of 3D accelerometry data streams. The third component of the thesis tackles the problem of detecting, annotating and filtering errors and outliers in sensor data streams, from wireless sensor networks, employed for environmental monitoring. The fourth and final component investigates, implements and evaluates an approach for reasoning across multiple environmental sensor data streams to infer h...

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Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey

Cited by 85 publications

References 73 publications

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

The Spatial-Temporal Anomaly Detection Algorithm in Wireless Sensor Networks

Semantic annotation and reasoning for sensor data streams

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