Distributed Abnormal Activity Detection in Smart Environments
The abnormal activity detection in smart environments has experienced increasing attention over years, due to its usefulness in pervasive applications. In order to meet the real-time needs and overcome the high costs and privacy issues, this paper proposes distributed abnormal activity detection app...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2014-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/283197 |
| _version_ | 1797714418275975168 |
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| author | Chengliang Wang Qian Zheng Yayun Peng Debraj De Wen-Zhan Song |
| author_facet | Chengliang Wang Qian Zheng Yayun Peng Debraj De Wen-Zhan Song |
| author_sort | Chengliang Wang |
| collection | DOAJ |
| description | The abnormal activity detection in smart environments has experienced increasing attention over years, due to its usefulness in pervasive applications. In order to meet the real-time needs and overcome the high costs and privacy issues, this paper proposes distributed abnormal activity detection approach ( DetectingAct ), which employs the computing and storage resources of simple and ubiquitous sensor nodes, to detect abnormal activity in smart environments equipped with wireless sensor networks (WSN). In DetectingAct , activity is defined as the combination of trajectory and duration , and abnormal activity is defined as the activity which deviates greater enough from those normal activities. DetectingAct works as follows. Firstly, DetectingAct finds the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopts unsupervised learning instead of supervised learning. Secondly, the distributed knowledge storage mechanism (DKSM) is introduced to store the mined patterns in each node. Then, the current triggered sensor adopts distributed abnormal activity detection algorithm (DAADA), in which the clustering analysis plays a critical role, to compare the present activity with normal activity patterns, by calculating the similarity between them. The feasibility, real-time property, and accuracy of the DetectingAct algorithm are evaluated using both simulation and real experiments case studies. |
| first_indexed | 2024-03-12T07:51:36Z |
| format | Article |
| id | doaj.art-4575a89fba884566aac1511d538aa57e |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T07:51:36Z |
| publishDate | 2014-05-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-4575a89fba884566aac1511d538aa57e2023-09-02T20:33:12ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772014-05-011010.1155/2014/283197283197Distributed Abnormal Activity Detection in Smart EnvironmentsChengliang Wang0Qian Zheng1Yayun Peng2Debraj De3Wen-Zhan Song4 School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA College of Computer Science, Chongqing University, Chongqing 400044, China College of Computer Science, Chongqing University, Chongqing 400044, China Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA Department of Computer Science, Georgia State University, Atlanta, GA 30303, USAThe abnormal activity detection in smart environments has experienced increasing attention over years, due to its usefulness in pervasive applications. In order to meet the real-time needs and overcome the high costs and privacy issues, this paper proposes distributed abnormal activity detection approach ( DetectingAct ), which employs the computing and storage resources of simple and ubiquitous sensor nodes, to detect abnormal activity in smart environments equipped with wireless sensor networks (WSN). In DetectingAct , activity is defined as the combination of trajectory and duration , and abnormal activity is defined as the activity which deviates greater enough from those normal activities. DetectingAct works as follows. Firstly, DetectingAct finds the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopts unsupervised learning instead of supervised learning. Secondly, the distributed knowledge storage mechanism (DKSM) is introduced to store the mined patterns in each node. Then, the current triggered sensor adopts distributed abnormal activity detection algorithm (DAADA), in which the clustering analysis plays a critical role, to compare the present activity with normal activity patterns, by calculating the similarity between them. The feasibility, real-time property, and accuracy of the DetectingAct algorithm are evaluated using both simulation and real experiments case studies.https://doi.org/10.1155/2014/283197 |
| spellingShingle | Chengliang Wang Qian Zheng Yayun Peng Debraj De Wen-Zhan Song Distributed Abnormal Activity Detection in Smart Environments International Journal of Distributed Sensor Networks |
| title | Distributed Abnormal Activity Detection in Smart Environments |
| title_full | Distributed Abnormal Activity Detection in Smart Environments |
| title_fullStr | Distributed Abnormal Activity Detection in Smart Environments |
| title_full_unstemmed | Distributed Abnormal Activity Detection in Smart Environments |
| title_short | Distributed Abnormal Activity Detection in Smart Environments |
| title_sort | distributed abnormal activity detection in smart environments |
| url | https://doi.org/10.1155/2014/283197 |
| work_keys_str_mv | AT chengliangwang distributedabnormalactivitydetectioninsmartenvironments AT qianzheng distributedabnormalactivitydetectioninsmartenvironments AT yayunpeng distributedabnormalactivitydetectioninsmartenvironments AT debrajde distributedabnormalactivitydetectioninsmartenvironments AT wenzhansong distributedabnormalactivitydetectioninsmartenvironments |