Survey on Prediction Algorithms in Smart Homes

被引：84

作者：

Wu, Shaoen ^{[1
,2
]}

Rendall, Jacob B. ^{[2
]}

Smith, Matthew J. ^{[2
]}

Zhu, Shangyu ^{[2
]}

Xu, Junhong ^{[2
]}

Wang, Honggang ^{[3
]}

Yang, Qing ^{[4
]}

Qin, Pinle ^{[5
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China

[2] Ball State Univ, Dept Comp Sci, Muncie, IN 47306 USA

[3] Univ Massachusetts, Dept Elect & Comp Engn, Dartmouth, MA 02747 USA

[4] Montana State Univ, Dept Comp Sci, Bozeman, MT 59717 USA

[5] North Univ China, Sch Comp Sci & Control Engn, Taiyuan 030051, Peoples R China

来源：

IEEE INTERNET OF THINGS JOURNAL | 2017年 / 4卷 / 03期

基金：

美国国家科学基金会;

关键词：

Intelligent systems; Internet of Things; prediction methods; smart homes; HIDDEN MARKOV-MODELS;

D O I：

10.1109/JIOT.2017.2668061

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The world has entered into a "smart" era. One area becoming smart is the place where we live-homes. Smart homes are expected to be equipped with numerous sensors to continually monitor, sense, and actuate the space. The data from these sensors can be used to provide various types of services by automating common tasks while causing minimal disruption to daily life. In order to provide these services, a system must have sufficient intelligence to predict future events based on its observations. This paper first examines the requirements for smart home predictions. It then comprehensively reviews prediction algorithms and variations that have been proposed and investigated in smart environments, such as smart homes. It is these prediction algorithms that provide the intelligence required by a smart home. Comparisons are also made upon these prediction algorithms on their features and models.

引用

页码：636 / 644

页数：9

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