Outlier detection strategies for WSNs: A survey

Published on Mar 4, 2021in Journal of King Saud University - Computer and Information Sciences13.473
· DOI :10.1016/J.JKSUCI.2021.02.012
Bhanu Chander4
Estimated H-index: 4
(Pondicherry University),
G. Kumaravelan4
Estimated H-index: 4
(Pondicherry University)
Abstract Wireless Sensor Networks (WSNs) are developed significantly from the last decades and attracted the attention of scientific and industrial domains. In WSNs, sensor nodes distributed autonomously in harsh environments are easily vulnerable to faults and attacks that cause sensor readings unreliable and inaccurate. In this scenario, sensor readings that have differed considerably from healthy behaviors will be considered abnormal data or anomalies/outliers. The inclusion of such outliers in data analytics will inevitably affect the outcome of the decision-making process. Thus, detecting outliers in WSNs using data-driven approaches becomes a novel technique among the Machine Learning (ML) communities. Meanwhile, various research issues are there in measuring the performance of the deployed ML algorithms in detecting outliers in WSNs, which generally contains minimum resources in terms of computational capability and power sources to ensure data quality. Hence, this paper presents a comprehensive overview of the state-of-the-art Statistical and Artificial Intelligence (AI) based techniques used in WSNs to detect outliers in the view of architecture, type, degree, approach, and detection mode. Furthermore, each aforementioned outlier detection approach is presented with detailed discussions and future scope for developments.
#1K. Thangaramya (Anna University)H-Index: 6
#2K. Kulothungan (Anna University)H-Index: 11
Last. Kannan Arputharaj (VIT University)H-Index: 11
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In wireless sensor networks (WSNs), energy optimization and the provision of security are the major design challenges. Since the wireless sensor devices are energy constrained, the issue of high energy consumption by the malicious nodes must be addressed well in order to enhance the network performance by making increased network lifetime, reduced energy consumption and delay. In the past, many researchers worked in the provision of new techniques for providing improved security to WSN in order ...
#1Nikos Giatrakos (TUC: Technical University of Crete)H-Index: 12
#2Antonios Deligiannakis (TUC: Technical University of Crete)H-Index: 22
Last. Yannis Kotidis (OPA: Athens University of Economics and Business)H-Index: 32
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Abstract Wireless Sensor Networks (WSNs) have become an integral part of cutting edge technological paradigms such as the Internet-of-Things (IoT) which incorporates a variety of smart application scenarios. WSNs include tiny sensors (motes), with constrained hardware capabilities and limited power supply that can collaboratively function in an unsupervised manner for a long period of time. Their purpose is to continuously monitor quantities of interest and provide answers to application queries...
With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantiti...
#1Sourabh BhartiH-Index: 6
#2Kiran Kumar Pattanaik (Indian Institute of Information Technology and Management, Gwalior)H-Index: 8
Last. Anshul Pandey (Indian Institute of Information Technology, Allahabad)H-Index: 5
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The quality of dataset measured and collected by wireless sensor networks (WSN) is often affected by noise and error that are inherent to resource-constrained sensor nodes. The affected data points deviating from the normal pattern are termed as outlier(s). However, detected outlier can be a result of the occurrence of an actual event. Outlier detection techniques developed for WSNs perform binary labelling of the data points and does not indicate the context stating whether the outlier is the r...
#1Mahmood Safaei (UTM: Universiti Teknologi Malaysia)H-Index: 6
#2Abul Samad Ismail (UTM: Universiti Teknologi Malaysia)H-Index: 1
Last. Mitra Safaei (Leibniz University of Hanover)H-Index: 2
view all 7 authors...
Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this r...
#1Tianwei Dai (University of Manchester)H-Index: 1
#2Zhengtao Ding (University of Manchester)H-Index: 39
Abstract One key challenge for sensor networks is to provide real-time high reliable sensor data with the minimum resource consumption. Outlier clearance in sensor networks can ensure the quality of sensor data and dependable monitoring. In this paper, we propose two online distributed outlier clearance approaches with low computational complexity and memory usage that can identify and remove the spurious sensor data. The proposed approaches are operated locally and thus save communication overh...
#1I. Gethzi Ahila Poornima (National Engineering College)H-Index: 1
#2B. Paramasivan (National Engineering College)H-Index: 9
Abstract Security in the Wireless Sensor Network(WSNs) is an essential and a challenging task. Anomaly detection is a key challenge to ensure the security in WSN. WSNs are vulnerable to various threats which may cause the node to get damaged and produce faulty measurements. The detection of such anomalous data is required to reduce false alarms. Machine learning algorithm based detection of anomalous data becomes popular now. Most of the current machine anomaly detection algorithms run in a stat...
#1Paulo Gil (UC: University of Coimbra)H-Index: 17
#2Hugo Martins (NOVA: Universidade Nova de Lisboa)H-Index: 4
Last. Fabio Januario (NOVA: Universidade Nova de Lisboa)H-Index: 5
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Detection and accommodation of outliers are crucial in a number of contexts, in which collected data from a given environment is subsequently used for assessing its running conditions or for data-based decision-making. Although a significant number of studies on this subject can be found in literature, a comprehensive empirical assessment in the context of local online detection in wireless sensor networks is still missing. The present work aims at filling this gap by offering an empirical evalu...
#1Xueqiang Yin (NPU: Northwestern Polytechnical University)H-Index: 1
#2Shining Li (NPU: Northwestern Polytechnical University)H-Index: 1
Trust management is considered as an effective complementary mechanism to ensure the security of sensor networks. Based on historical behavior, the trust value can be evaluated and applied to estimate the reliability of the node. For the analysis of the possible attack behavior of malicious nodes, we proposed a trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks. To mitigate the malicious attacks such as packet dropping or packet...
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