KNN-Based Approximate Outlier Detection Algorithm Over IoT Streaming Data

Zhu, Rui; Ji, Xiaoling; Yu, Danyang; Tan, Zhiyuan; Zhao, Liang; Li, Jiajia; Xia, Xiufeng

doi:10.1109/access.2020.2977114

Cited by 31 publications

(15 citation statements)

References 32 publications

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“…In order to find the optimum number of gaussians that better approximate the joint probability distribution for a given dataset the sorted values of the vector d k can be (18 where R j represents the jth region, m is the total number of estimated gaussians and n i is the total number of elements in the respective region. The combined multiple gaussians model is then estimated by:…”

Section: Joint Probability Density Estimation Using D-k-nnmentioning

confidence: 99%

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Unsupervised outlier detection in multidimensional data

Rehman

Belhaouari

2021

J Big Data

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Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. In order to detect the anomalies in a dataset in an unsupervised manner, some novel statistical techniques are proposed in this paper. The proposed techniques are based on statistical methods considering data compactness and other properties. The newly proposed ideas are found efficient in terms of performance, ease of implementation, and computational complexity. Furthermore, two proposed techniques presented in this paper use transformation of data to a unidimensional distance space to detect the outliers, so irrespective of the data’s high dimensions, the techniques remain computationally inexpensive and feasible. Comprehensive performance analysis of the proposed anomaly detection schemes is presented in the paper, and the newly proposed schemes are found better than the state-of-the-art methods when tested on several benchmark datasets.

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Section: Joint Probability Density Estimation Using D-k-nnmentioning

confidence: 99%

“…After the initial pivotal works for outlier detection based on the distance measure [15,16], several new methods based on the distance measure were also proposed by different authors in literature [17,18]. The difference between the latterly proposed methods and the previous studies is the use of nearest neighbors in distance calculation.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised outlier detection in multidimensional data

Rehman

Belhaouari

2021

J Big Data

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“…The model has a wide range of applications and is not affected by the characteristics of the study area and the types of targets. Combining actual remote sensing images and existing meteorological conditions, considering that the CHRIS map is small, it can be considered that the aerosol is uniform within the map [ 24 , 25 ]. Furthermore, the important atmospheric parameters on the day the CHRIS image was taken are obtained from the local meteorological department.…”

Section: Results and Analysismentioning

confidence: 99%

Multimedia Image Data Analysis Based on KNN Algorithm

2022

Computational Intelligence and Neuroscience

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In order to improve the authenticity of multispectral remote sensing image data analysis, the KNN algorithm and hyperspectral remote sensing technology are used to organically combine advanced multimedia technology with spectral technology to subdivide the spectrum. Different classification methods are used to classify CHRIS 0°, and the results are analyzed and compared: SVM classification accuracy is the highest 72 8448%, Kappa coefficient is 0.6770, and SVM is used to classify CHRIS images from five angles, and the results are compared and analyzed: the classification accuracy is from high to low, and the order is FZA = 0 > FZA = −36 > FZA = −55 > FZA = 36 > FZA = 55; SVM is used to classify the multiangle combined image, and the result is compared with the CHRIS 0° result: the overall classification accuracy of angle-combined image types is lower than that of single-angle images; the SVM is used to classify the band-combined image, and the result is compared with CHRIS 0°: the overall classification accuracy of the band combination image forest type is very low, and the effect is not as good as the combining multiangle image classification results. It is verified that if CHRIS multiangle hyper-spectral data are used for classification, the SVM method should be used to classify spectral remote sensing image data with the best effect.

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“…where q and p are data points and d is the distance between them. In [44] data streaming in IoT using the k-NN algorithm is proposed. The KNN algorithm is also used for the early detection of agriculture pests, diseases, sensor node failure and fault detection issues [45].…”

Section: Classificationmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

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KNN-Based Approximate Outlier Detection Algorithm Over IoT Streaming Data

Cited by 31 publications

References 32 publications

Unsupervised outlier detection in multidimensional data

Unsupervised outlier detection in multidimensional data

Multimedia Image Data Analysis Based on KNN Algorithm

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

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