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Heuristic and Statistical Prediction Algorithms Survey for Smart Environments

There is a growing interest in the development of smart environments through predicting the behaviors ofinhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities tofacilitate residents and city administration. Prediction algorithms are broadly used in...

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Bibliographic Details
Published in:Journal of information processing systems 2020, 16(5), 65, pp.1196-1213
Main Authors: MalikSehrish, Israr Ullah, 김도현, KyuTae Lee
Format: Article
Language:English
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Summary:There is a growing interest in the development of smart environments through predicting the behaviors ofinhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities tofacilitate residents and city administration. Prediction algorithms are broadly used in the smart fields in orderto well equip the smart services for the future demands. Hence, an accurate prediction technology plays a vitalrole in the smart services. In this paper, we take out an extensive survey of smart spaces such as smart homes,smart farms and smart cars and smart applications such as smart health and smart energy. Our extensive surveyis based on more than 400 articles and the final list of research studies included in this survey consist of 134research papers selected using Google Scholar database for period of 2008 to 2018. In this survey, we highlightthe role of prediction algorithms in each sub-domain of smart Internet of Things (IoT) environments. We alsodiscuss the main algorithms which play pivotal role in a particular IoT subfield and effectiveness of thesealgorithms. The conducted survey provides an efficient way to analyze and have a quick understanding of stateof the art work in the targeted domain. To the best of our knowledge, this is the very first survey paper on maincategories of prediction algorithms covering statistical, heuristic and hybrid approaches for smart environments. KCI Citation Count: 0
ISSN:1976-913X
2092-805X
DOI:10.3745/JIPS.04.0191