Segmentation of the Sensor Data Stream in Pervasive Smart Environments
Nowadays, pervasive environment development has garnered lots of attentions. In such environments, user-object interactions along time are recorded via several sensors, and sensor events are processed as a stream of data. In this process, user’s activities are recognized, and accordingly, essential...
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| Format: | Article |
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University of Qom
2020-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_1273_d4ee9fa811f31019d8d1a6a702006b28.pdf |
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| author | Vahid Ghasemi Mohammad Javadian Sajad Hayati |
| author_facet | Vahid Ghasemi Mohammad Javadian Sajad Hayati |
| author_sort | Vahid Ghasemi |
| collection | DOAJ |
| description | Nowadays, pervasive environment development has garnered lots of attentions. In such environments, user-object interactions along time are recorded via several sensors, and sensor events are processed as a stream of data. In this process, user’s activities are recognized, and accordingly, essential services are provided. In many activity recognition approaches, firstly the input data stream is segmented, then the activity pertaining to each segment is induced. In such approaches, sensor data stream segmentation is a predominant phase. In this paper, this problem is investigated and a novel method, based on a difference of convex programming problem, is proposed to solve it. In the proposed method a feature vector is calculated for each sensor event in the data stream using a Bayesian approach, and the sequence of such vectors is hired in a difference of convex cost function. The cost function and feature vectors has been calculated by considering heuristics adopting to smart environments. Data segments are extracted by minimizing the cost function. The segmentation purity and conditional entropy have been calculated to measure the performance. Evaluations show that the proposed method has an acceptable performance comparing to some existing approaches. |
| format | Article |
| id | doaj-art-5148ae3d344540d6bdecedba7bab1247 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2020-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-5148ae3d344540d6bdecedba7bab12472025-01-30T20:17:43ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752020-09-0162233910.22091/jemsc.2018.12731273Segmentation of the Sensor Data Stream in Pervasive Smart EnvironmentsVahid Ghasemi0Mohammad Javadian1Sajad Hayati2Kermanshah University of Technology (KUT), Kermanshah, Iran.Department of Computer Engineering, Kermanshah University of Technology (KUT), Kermanshah, Iran.Department of Mechanical Engineering, Kermanshah University of Technology (KUT), Kermanshah, Iran.Nowadays, pervasive environment development has garnered lots of attentions. In such environments, user-object interactions along time are recorded via several sensors, and sensor events are processed as a stream of data. In this process, user’s activities are recognized, and accordingly, essential services are provided. In many activity recognition approaches, firstly the input data stream is segmented, then the activity pertaining to each segment is induced. In such approaches, sensor data stream segmentation is a predominant phase. In this paper, this problem is investigated and a novel method, based on a difference of convex programming problem, is proposed to solve it. In the proposed method a feature vector is calculated for each sensor event in the data stream using a Bayesian approach, and the sequence of such vectors is hired in a difference of convex cost function. The cost function and feature vectors has been calculated by considering heuristics adopting to smart environments. Data segments are extracted by minimizing the cost function. The segmentation purity and conditional entropy have been calculated to measure the performance. Evaluations show that the proposed method has an acceptable performance comparing to some existing approaches.https://jemsc.qom.ac.ir/article_1273_d4ee9fa811f31019d8d1a6a702006b28.pdfpervasive environmentsensor data streamconvex programming problem |
| spellingShingle | Vahid Ghasemi Mohammad Javadian Sajad Hayati Segmentation of the Sensor Data Stream in Pervasive Smart Environments مدیریت مهندسی و رایانش نرم pervasive environment sensor data stream convex programming problem |
| title | Segmentation of the Sensor Data Stream in Pervasive Smart Environments |
| title_full | Segmentation of the Sensor Data Stream in Pervasive Smart Environments |
| title_fullStr | Segmentation of the Sensor Data Stream in Pervasive Smart Environments |
| title_full_unstemmed | Segmentation of the Sensor Data Stream in Pervasive Smart Environments |
| title_short | Segmentation of the Sensor Data Stream in Pervasive Smart Environments |
| title_sort | segmentation of the sensor data stream in pervasive smart environments |
| topic | pervasive environment sensor data stream convex programming problem |
| url | https://jemsc.qom.ac.ir/article_1273_d4ee9fa811f31019d8d1a6a702006b28.pdf |
| work_keys_str_mv | AT vahidghasemi segmentationofthesensordatastreaminpervasivesmartenvironments AT mohammadjavadian segmentationofthesensordatastreaminpervasivesmartenvironments AT sajadhayati segmentationofthesensordatastreaminpervasivesmartenvironments |