Ensemble based sensing anomaly detection in wireless sensor networks

Curiac, Daniel-Ioan; Voloşencu, Constantin

doi:10.1016/j.eswa.2012.02.036

Cited by 58 publications

(28 citation statements)

References 21 publications

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“…Curiac et al [90]: in this work, an ensemble based classification system which is composed of five diverse binary classifiers was proposed for anomaly detection in sensor measurements. These classifiers are the average based classifier, autoregressive linear predictor based classifier, neural network based classifier, neural network autoregressive predictor based classifier, and adaptive neuro-fuzzy inference system (ANFIS)-based classifier.…”

Section: Detection Method-based Classification Of Anomaly Detection Mmentioning

confidence: 99%

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Rassam

Zainal

Maarof

2013

Sensors

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Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.

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Section: Detection Method-based Classification Of Anomaly Detection Mmentioning

confidence: 99%

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Rassam

Zainal

Maarof

2013

Sensors

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“…The concept of ensemble based anomaly detection is also employed in the broader domain of networks, such as wireless sensor networks (WSNs) and mobile ad-hoc networks (MANETs) [28][29][30], where the quality of data sensed and obtained by nodes is affected by anomalies due to various reasons, such as node failures, malicious attacks, measurement errors, or unusual environmental changes. In other works [28][29][30], anomaly detection is performed using an ensemble of classifiers, i.e., the decision is made by a combination of the classifiers' outputs.…”

Section: Ensemble Anomaly Detection Using Multiple Baseline Modelsmentioning

confidence: 99%

“…In other works [28][29][30], anomaly detection is performed using an ensemble of classifiers, i.e., the decision is made by a combination of the classifiers' outputs.…”

Section: Ensemble Anomaly Detection Using Multiple Baseline Modelsmentioning

confidence: 99%

Human error tolerant anomaly detection based on time-periodic packet sampling

Uchida

2016

Knowledge-Based Systems

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“…Authors in [3] propose an algorithm to detect faulty mea surements on the LPU of WSNs. They use five different classifiers, each of which classifies the sensed data as normal or abnormal.…”

Section: Related Workmentioning

confidence: 99%

Reliable vital sign collection in medical Wireless Sensor Networks

Naseem

Salem

Liu³

et al. 2013

2013 IEEE 15th International Conference on E-Health Networking, Applications and Services (Healthcom 2013)

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Ahstract-The aim of this paper is to propose a new approach for the detection and isolation of faulty measurements in medical wireless sensors networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. We begin by collecting physiological data and then we cluster the data collected during the first few minutes using the Gaussian mixture decomposition. We use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules, which are used to detect abnormal values. Finally, we exploit the spatial correlation between monitored attributes to differentiate between faulty sensor readings and emergency situations. Our experimental results on real patient dataset show that our proposed approach achieves a high level of detection accuracy, which in turn proves the effectiveness of this approach in enhancing the reliability of medical wireless sensors networks.

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Ensemble based sensing anomaly detection in wireless sensor networks

Cited by 58 publications

References 21 publications

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Human error tolerant anomaly detection based on time-periodic packet sampling

Reliable vital sign collection in medical Wireless Sensor Networks

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