Sensor-based occupancy detection using neutrosophic features fusion
Occupancy detection using ambient sensors has many benefits such as saving energy and money, enhancing security monitoring systems, and maintaining the privacy. However, sensors data suffers from uncertainty and unreliability due to acquisition errors or incomplete knowledge. This paper presents a n...
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doaj-26aeff4d21cd450f928e182200a452232020-11-25T02:07:06ZengElsevierHeliyon2405-84402019-09-0159e02450Sensor-based occupancy detection using neutrosophic features fusionN.S. Fayed0Mervat Abu-Elkheir1E.M. El-Daydamony2A. Atwan3Department of Information Technology, Faculty of Computers and Information, Mansoura University, Egypt; Corresponding author.Department of Information Technology, Faculty of Computers and Information, Mansoura University, EgyptDepartment of Information Technology, Faculty of Computers and Information, Mansoura University, EgyptDepartment of Information Technology, Faculty of Computers and Information, Mansoura University, Egypt; Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi ArabiaOccupancy detection using ambient sensors has many benefits such as saving energy and money, enhancing security monitoring systems, and maintaining the privacy. However, sensors data suffers from uncertainty and unreliability due to acquisition errors or incomplete knowledge. This paper presents a new heterogeneous sensors data fusion method for binary occupancy detection which detects whether the place is occupied or not. This method is based on using neutrosophic sets and sensors data correlations. By using neutrosophic sets, uncertain data can be handled. Using sensors data fusion, on the other hand, increases the reliability by depending on more than one sensor data. Accordingly, the results of experiments applied using Random Forest (RF), Linear Discriminant Analysis (LDA), and FUzzy GEnetic (FUGE) algorithms prove the new method to enhance detection accuracy.http://www.sciencedirect.com/science/article/pii/S2405844019361109Computer scienceSensors data fusionWireless sensor networksNeutrosophic setsSensors data correlationsHeterogeneous sensors |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
N.S. Fayed Mervat Abu-Elkheir E.M. El-Daydamony A. Atwan |
spellingShingle |
N.S. Fayed Mervat Abu-Elkheir E.M. El-Daydamony A. Atwan Sensor-based occupancy detection using neutrosophic features fusion Heliyon Computer science Sensors data fusion Wireless sensor networks Neutrosophic sets Sensors data correlations Heterogeneous sensors |
author_facet |
N.S. Fayed Mervat Abu-Elkheir E.M. El-Daydamony A. Atwan |
author_sort |
N.S. Fayed |
title |
Sensor-based occupancy detection using neutrosophic features fusion |
title_short |
Sensor-based occupancy detection using neutrosophic features fusion |
title_full |
Sensor-based occupancy detection using neutrosophic features fusion |
title_fullStr |
Sensor-based occupancy detection using neutrosophic features fusion |
title_full_unstemmed |
Sensor-based occupancy detection using neutrosophic features fusion |
title_sort |
sensor-based occupancy detection using neutrosophic features fusion |
publisher |
Elsevier |
series |
Heliyon |
issn |
2405-8440 |
publishDate |
2019-09-01 |
description |
Occupancy detection using ambient sensors has many benefits such as saving energy and money, enhancing security monitoring systems, and maintaining the privacy. However, sensors data suffers from uncertainty and unreliability due to acquisition errors or incomplete knowledge. This paper presents a new heterogeneous sensors data fusion method for binary occupancy detection which detects whether the place is occupied or not. This method is based on using neutrosophic sets and sensors data correlations. By using neutrosophic sets, uncertain data can be handled. Using sensors data fusion, on the other hand, increases the reliability by depending on more than one sensor data. Accordingly, the results of experiments applied using Random Forest (RF), Linear Discriminant Analysis (LDA), and FUzzy GEnetic (FUGE) algorithms prove the new method to enhance detection accuracy. |
topic |
Computer science Sensors data fusion Wireless sensor networks Neutrosophic sets Sensors data correlations Heterogeneous sensors |
url |
http://www.sciencedirect.com/science/article/pii/S2405844019361109 |
work_keys_str_mv |
AT nsfayed sensorbasedoccupancydetectionusingneutrosophicfeaturesfusion AT mervatabuelkheir sensorbasedoccupancydetectionusingneutrosophicfeaturesfusion AT emeldaydamony sensorbasedoccupancydetectionusingneutrosophicfeaturesfusion AT aatwan sensorbasedoccupancydetectionusingneutrosophicfeaturesfusion |
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1724931084020350976 |