Sparse sampling for sensing temporal data - Building an optimized envelope

Menachem Domb, Guy Leshem, Elisheva Bonchek-Dokow, Esther David, Yuh-Jye Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

IoT systems collect vast amounts of data which can be used in order to track and analyze the structure of future recorded data. However, due to limited computational power, bandwith, and storage capabilities, this data cannot be stored as is, but rather must be reduced in such a way so that the abilities to analyze future data, based on past data, will not be compromised. We propose a parameterized method of sampling the data in an optimal way. Our method has three parameters - an averaging method for constructing an average data cycle from past observations, an envelope method for defining an interval around the average data cycle, and an entropy method for comparing new data cycles to the constructed envelope. These parameters can be adjusted according to the nature of the data, in order to find the optimal representation for classifying new cycles as well as for identifying anomalies and predicting future cycle behavior. In this work we concentrate on finding the optimal envelope, given an averaging method and an entropy method. We demonstrate with a case study of meteorological data regarding El Ninio years.

Original languageEnglish
Title of host publicationTAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages241-247
Number of pages7
ISBN (Electronic)9781509057320
DOIs
StatePublished - 16 Mar 2017
Event2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016 - Hsinchu, Taiwan
Duration: 25 Nov 201627 Nov 2016

Publication series

NameTAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings

Conference

Conference2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016
CountryTaiwan
CityHsinchu
Period25/11/1627/11/16

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