Title: Enhanced-KNN (M-KNN) based outlier detection and sensor data aggregation for large data streams in the IoT-cloud

Authors: Y.R. Sampath Kumar; H.N. Champa

Addresses: Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, Karnataka, India ' Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, Karnataka, India

Abstract: IoT is a technology that facilitates several applications of the modern age. The pervasive deployment of Sensor Nodes (SN) affects the sensor data acquisition. The IoT-data requires utilisation of Dimensionality Reduction (DR) and classification techniques. An advanced Data Aggregation (DA) technique decreases the number of total data transmissions and facilitates data accuracy. Here, a data analysis framework is suggested for DA and Outlier Detection (OD) by employing a modified K-Nearest Neighbour (M-KNN) algorithm. Further, the recovery error rate as well as Energy Consumption (EC) of proposed methodology is analysed and compared with the existing Recursive Principal Component Analysis (R-PCA) technique. The techniques are compared by varying the cluster size and the results depict that the proposed M-KNN attains the lowest relative error and lowest EC for variable number of cluster size for both Intel and NDBC-TAO data. The proposed model is crucial for exploring the sensor data and predicting future events based on observed sensor data analysis.

Keywords: data abstraction; data acquisition; data aggregation; IoT-cloud; outlier detection.

DOI: 10.1504/IJIPT.2022.125960

International Journal of Internet Protocol Technology, 2022 Vol.15 No.3/4, pp.236 - 249

Received: 22 Oct 2020
Accepted: 17 May 2021
Published online: 05 Oct 2022 *

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