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J. Sens. Actuator Netw. 2018, 7(1), 2; https://doi.org/10.3390/jsan7010002

Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks

Graduate School of Applied Informatics, University of Hyogo, Computational Science Center Building 5-7F 7-1-28 Minatojima-minamimachi, Chuo-ku Kobe, Hyogo 6570047, Japan
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Received: 31 October 2017 / Revised: 25 December 2017 / Accepted: 29 December 2017 / Published: 2 January 2018
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
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Abstract

We propose a new peak searching algorithm (PSA) that uses Bayesian optimization to find probability peaks in a dataset, thereby increasing the speed and accuracy of clustering algorithms. Wireless sensor networks (WSNs) are becoming increasingly common in a wide variety of applications that analyze and use collected sensing data. Typically, the collected data cannot be directly used in modern data analysis problems that adopt machine learning techniques because such data lacks additional information (such as data labels) specifying its purpose of users. Clustering algorithms that divide the data in a dataset into clusters are often used when additional information is not provided. However, traditional clustering algorithms such as expectation–maximization (EM) and k - m e a n s algorithms require massive numbers of iterations to form clusters. Processing speeds are therefore slow, and clustering results become less accurate because of the way such algorithms form clusters. The PSA addresses these problems, and we adapt it for use with the EM and k - m e a n s algorithms, creating the modified P S E M and P S k - m e a n s algorithms. Our simulation results show that our proposed P S E M and P S k - m e a n s algorithms significantly decrease the required number of clustering iterations (by 1.99 to 6.3 times), and produce clustering that, for a synthetic dataset, is 1.69 to 1.71 times more accurate than it is for traditional EM and enhanced k - m e a n s ( k - m e a n s ++) algorithms. Moreover, in a simulation of WSN applications aimed at detecting outliers, P S E M correctly identified the outliers in a real dataset, decreasing iterations by approximately 1.88 times, and P S E M was 1.29 times more accurate than EM at a maximum. View Full-Text
Keywords: peak searching; clustering; Gaussian mixture model; Bayesian optimization; Gaussian process; outlier detection peak searching; clustering; Gaussian mixture model; Bayesian optimization; Gaussian process; outlier detection
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Zhang, T.; Zhao, Q.; Shin, K.; Nakamoto, Y. Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks. J. Sens. Actuator Netw. 2018, 7, 2.

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