Outliers detection and classification in wireless sensor networks (original) (raw)

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.

An Outlier detection approach with data mining in wireless sensor network

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.

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...

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.

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.

Spatial-Temporal Outlier and Event Detection in Wireless Sensor Networks

2013

Many spatial phenomena are changing continuously in time and space. Thus, it is the emergence of accessing frequent, up-to-date, spatially dense measurements for monitoring and tracking. Compared to conventional earth observation data collection technologies, wireless sensor networks (WSNs) can provide continuous observations of physical phenomenon by means of dense deployment of sensor nodes. Most WSN applications require accurate, energy friendly and real-time data analysis in order to provide timely information for decision makers. Quality of data provided by WSNs is highly critical while, provided raw data may be drawn in from of a low quality and reliability level expectedly, because of the sensors inexpensive nature.

Spatial anomaly detection in sensor networks using neighborhood information

Information Fusion, 2017

The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological advances in electronics and networked systems. An important remaining challenge now is to extract meaningful information from the ever-increasing amount of sensor data collected by WSNs. In particular, there is strong interest in algorithms capable of automatic detection of patterns, events or other out-of-the order, anomalous system behavior. Data anomalies may indicate states of the system that require further analysis or prompt actions. Traditionally, anomaly detection techniques are executed in a central processing facility, which requires the collection of all measurement data at a central location, an obvious limitation for WSNs due to the high data communication costs involved. In this paper we explore the extent by which one may depart from this classical centralized paradigm, looking at decentralized anomaly detection based on unsupervised machine learning. Our aim is to detect anomalies at the sensor nodes, as opposed to centrally, to reduce energy and spectrum consumption. We study the information gain coming from aggregate neighborhood data, in comparison to performing simple, in-node anomaly detection. We evaluate the effects of neighborhood size and spatio-temporal correlation on the performance of our new neighborhood-based approach using a range of real-world network deployments and datasets. We find the conditions that make neighborhood data fusion advantageous, identifying also the cases in which this approach does not lead to detectable improvements. Improvements are linked to the diffusive properties of data (spatio-temporal correlations) but also to the type of sensors, anomalies and network topological features. Overall, when a dataset stems from a similar mixture of diffusive processes precision tends to benefit, particularly in terms of recall. Our work paves the way towards understanding how distributed data fusion methods may help managing the complexity of wireless sensor networks, for instance in massive Internet of Things scenarios.

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.

An Outlier Detection and Rectification Method in Cluster Based Wireless Sensor Network

The evolution of Wireless Sensor networks (WSNs) is a demandable, efficient and emerging area of Computer Science Engineering which has been currently employed in various field of engineering particularly in communication system to make it effective and reliable. For successful application of WSN it is important to maintain the basic security level, both from external and internal attacks else entire network may collapse. In the field of WSNs, if the sensed patterns that significantly deviate from the normal pattern are considered as outlier. The possible source of outlier includes noise, errors, events and malicious attack on the network. WSNs are more likely to generate outlier due to their special characteristics like constrained available with the resources causing frequent physical failure and harsh deployment area. In this paper we have investigated and experimented different outlier attack in WSN and how these attacks are efficiently detected and rectified. The usual outlier detection techniques are not directly applicable to wireless sensor network due to the nature of sensed data, specific requirements and limitations of the WSNs. Our proposed method will train and test data set for detection as well as rectification of outlier due the malicious attack and identify the affected sensor nodes in a largely deployed cluster based wireless sensor network under a common outlier detection framework which is designed by some well specified supervised learning and classification based data mining technique.