In-network outlier detection in wireless sensor networks (original) (raw)

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.

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...

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.

Outlier Detection Techniques and Cleaning of Data for Wireless Sensor Networks: A Survey

2012

Pattern recognition is the scientific discipline where the goal is the classification of objects into a number of categories or classes. Pattern recognition is an integral part in most sensing networks built for outlier detection. The significant deviations from the pattern of sensed data are considered as outliers in wireless sensor networks. These outliers include noise, errors, and malicious attack on the network. This affects the performance of the wireless sensor networks. Mostly the nature of sensor data is multivariate but it may be univariate also. Because of this, the traditional techniques are not directly applicable to wireless sensor networks. This contribution overviews existing outlier detection techniques developed for wireless sensor networks. It also presents a outlier detection technique framework to be used as a guideline to select a technique for outlier detection suitable for application based on the characteristics, such as, data type, outlier type and outlier ...

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.

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.

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.

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.