Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2021-01-08 , DOI: 10.1007/s12652-020-02703-7 Ihsan Ullah , Hee Yong Youn , Youn-Hee Han
Wireless sensor network (WSN) is used for data collection and transmission in IoT environment. Since it consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which substantially deteriorate the network performance. Data aggregation is needed to reduce energy consumption and prolong the lifetime of WSN. In this paper a novel data aggregation scheme is proposed which is based on modified radial basis function neural network to classify the collected data at cluster head and eliminate the redundant data and outliers. Additionally, cosine similarity is used to cluster the nodes having the most similar data. The radial basis function (RBF) is adapted by Mahalanobis distance to support the outlier’s detection and analysis in the multivariate data. The data collected from the sensor node at the cluster head are processed by mahalanbis distance-based radial basis function neural network (MDRBF-NN) before transferred to the based station. Extensive computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing representative data aggregation schemes in terms of data classification, outlier detection, and energy efficiency.
中文翻译:
基于径向基函数神经网络的无线传感器网络高效数据聚集和离群值检测方案
无线传感器网络(WSN)用于IoT环境中的数据收集和传输。由于它由大量传感器节点组成,因此会生成大量冗余数据和异常值,从而大大降低网络性能。需要数据聚合以减少能耗并延长WSN的寿命。本文提出了一种基于改进的径向基函数神经网络的数据聚合方案,对簇头的采集数据进行分类,消除了冗余数据和离群值。另外,余弦相似度用于对具有最相似数据的节点进行聚类。径向基函数(RBF)通过马氏距离进行调整,以支持对多元数据进行离群值的检测和分析。从簇头传感器节点收集的数据在传输到基站之前,先通过基于马哈兰比斯距离的径向基函数神经网络(MDRBF-NN)进行处理。用真实数据集进行的广泛计算机仿真表明,在数据分类,异常值检测和能效方面,该方案始终优于现有的代表性数据聚合方案。