Outlier detection approaches for wireless sensor networks: A survey (original) (raw)

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In-network outlier detection in wireless sensor networks

Knowledge and Information Systems, 2013

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage, (3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.

A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

Symmetry, 2020

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...

AN ANALYSIS OF OUTLIER DETECTION TECHNIQUES FOR WIRELESS SENSOR NETWORK APPLICATIONS

Outlier detection is the process of finding data objects whose behaviour are highly varying from expectation. It is considered to be one of the fundamental tasks of data mining. In Wireless sensor networks, outliers can be defined as those measurements that significantly deviate from the normal pattern of sensed data. Due to various reasons that includes fault in sensors, communication error etc., wireless sensors tend to generate outliers. The presence of outliers in a dataset leads to a biased outcome and erroneous conclusions, when the data is further analyzed. Identifying outliers before data analysis helps improvise the quality of data. In this paper, we analyze the performance of two different techniques and confirm the suitability of outlier detection technique for multivariate data.

A Comparison of Outlier Detection Algorithm for Wireless Sensor Network.

International Journal of Engineering Sciences & Research Technology, 2014

Wireless sensor networks had been deployed in the real world to collect large amounts of raw sensed data. However, the key challenge is to extract high level knowledge from such raw data. Sensor networks applications, outlier/anomaly detection has been paid more and more attention. The propose of a classification approach that provides outlier detection and data classification simultaneously. Experiments on Intel Berkley lab sensor dataset show that the proposed approach outperforms other techniques in both effectiveness & efficiency.

Outliers detection and classification in wireless sensor

2013

In the past few years, many wireless sensor networks had been deployed in the real world to collect large amounts of raw sensed data. However, the key challenge is to extract high-level knowledge from such raw data. In the applications of sensor networks, outlier/anomaly detection has been paid more and more attention. Outlier detection can be used to filter noisy data, find faulty nodes, and discover interesting events. In this paper we propose a novel in-network knowledge discovery approach that provides outlier detection and data clustering simultaneously. Our approach is capable to distinguish between an error due to faulty sensor and an error due to an event (probably an environmental event) which characterize the spatial and temporal correlations between events observed by sensor nodes in a confined network neighborhood. Experiments on both synthetic and real datasets show that the proposed algorithm outperforms other techniques in both effectiveness and efficiency.

Outlier Detection in WSN- A Survey

International Journal of Advanced Research in Computer Science and Software Engineering, 2013

In the field of wireless sensor networks, the measurements that deviate from the normal behaviour of sensed data are taken to be as outliers. The potential sources of outliers can be noise and errors, events, and malicious attacks on the network. This paper give an overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Also, a technique-based taxonomy will be discussed with characteristics of outlier data such as data type, outlier type, outlier identity, and outlier degree.

A Communication Efficient Framework for Finding Outliers in Wireless Sensor Networks

2010 Eleventh International Conference on Mobile Data Management, 2010

Outlier detection is a well studied problem in various fields. The unique challenges of wireless sensor networks make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we present a new communication technique to find outliers in a wireless sensor network. Communication is minimized through a creative method of controlling sensor behavior. At the same time, minimal assumptions are made about the nature of the data set as to minimize the loss of generality in the architecture.

High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

International Journal of Modern Education and Computer Science

The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs). The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel. Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs. In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data. This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time. The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM). Real datasets gathered from a WSN configured in a local lab are used for testing the techniques. Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements. The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases. The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance. On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT. This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.

Outliers detection and classification in wireless sensor networks

Egyptian Informatics Journal, 2013

In the past few years, many wireless sensor networks had been deployed in the real world to collect large amounts of raw sensed data. However, the key challenge is to extract high-level knowledge from such raw data. In the applications of sensor networks, outlier/anomaly detection has been paid more and more attention. Outlier detection can be used to filter noisy data, find faulty nodes, and discover interesting events. In this paper we propose a novel in-network knowledge discovery approach that provides outlier detection and data clustering simultaneously. Our approach is capable to distinguish between an error due to faulty sensor and an error due to an event (probably an environmental event) which characterize the spatial and temporal correlations between events observed by sensor nodes in a confined network neighborhood. Experiments on both synthetic and real datasets show that the proposed algorithm outperforms other techniques in both effectiveness and efficiency.

An Efficient Approach for Outlier Detection in Wireless Sensor Network

Wireless Sensor Networks are those networks which include many sensors, sensors have many sensor nodes that are spread all over the world. A wireless sensor network (WSN) normally has many sensor nodes which are very modest, having minimum cost dispersed over the world having high-powered sink nodes which collect the readings of the sensor nodes. These nodes are comprised with high sensing power, processing and wireless communication abilities. A sensor network involves large number of sensors, collecting and communicating the sensor measurements of observing physical world scenarios. The main problem in WSN is outlier. Basically outlier is an element of a data set that falls in an abnormal range. For the capabilities of WSNs, it is clear that we need an accurate and robust technique which can be used for multivariate data too and can give the optimum results in output threshold, and that technique should help to increase the special characteristics of WSNs such as node mobility, network topology change and making distinction between errors and events. Among all the techniques KERNEL FUNCTIONS is the best technique because it can be used for multivariate data also. Except it FTDA and LSH are also produce optimum results.