Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective
With the explosive growth and wide-spread use of smartphones with Wi-Fi enabled, people are used to accessing the internet through Wi-Fi network interfaces of smartphones. Smartphones periodically transmit Wi-Fi messages, even when not connected to a network. In this paper, we describe the Mo-Fi sys...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/927940 |
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| _version_ | 1849397405923934208 |
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| author | Weijun Qin Jiadi Zhang Bo Li Limin Sun |
| author_facet | Weijun Qin Jiadi Zhang Bo Li Limin Sun |
| author_sort | Weijun Qin |
| collection | DOAJ |
| description | With the explosive growth and wide-spread use of smartphones with Wi-Fi enabled, people are used to accessing the internet through Wi-Fi network interfaces of smartphones. Smartphones periodically transmit Wi-Fi messages, even when not connected to a network. In this paper, we describe the Mo-Fi system which monitors and aggregates large numbers of continuous Wi-Fi message transmissions from nearby smartphones in the area of interest using nonintrusive Wi-Fi sniffer sensors. In this paper, we propose an optimized Wi-Fi channel detection and selection method to switch the best channels automatically to aggregate the Wi-Fi messages based on channel data transmission weights and human presence activity classification method based on the features of human dwell duration sequences in order to evaluate the user engagement index. By deploying in the real-world office environment, we found that the performance of Wi-Fi messages aggregation of CAOCA and CACFA algorithms is over 3.8 times higher than the worst channel of FCA algorithms and about 76% of the best channel of FCA algorithms, and the human presence detection rate reached 87.4%. |
| format | Article |
| id | doaj-art-ae665d8df86749ca9c4d28e40ded8724 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ae665d8df86749ca9c4d28e40ded87242025-08-20T03:39:00ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-12-01910.1155/2013/927940927940Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data ProspectiveWeijun Qin0Jiadi Zhang1Bo Li2Limin Sun3 State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, No. 89, Minzhuang Road, Haidian District, Beijing 100093, China School of Software and Microelectronics, Peking University, Beijing 102600, China School of Software and Microelectronics, Peking University, Beijing 102600, China State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, No. 89, Minzhuang Road, Haidian District, Beijing 100093, ChinaWith the explosive growth and wide-spread use of smartphones with Wi-Fi enabled, people are used to accessing the internet through Wi-Fi network interfaces of smartphones. Smartphones periodically transmit Wi-Fi messages, even when not connected to a network. In this paper, we describe the Mo-Fi system which monitors and aggregates large numbers of continuous Wi-Fi message transmissions from nearby smartphones in the area of interest using nonintrusive Wi-Fi sniffer sensors. In this paper, we propose an optimized Wi-Fi channel detection and selection method to switch the best channels automatically to aggregate the Wi-Fi messages based on channel data transmission weights and human presence activity classification method based on the features of human dwell duration sequences in order to evaluate the user engagement index. By deploying in the real-world office environment, we found that the performance of Wi-Fi messages aggregation of CAOCA and CACFA algorithms is over 3.8 times higher than the worst channel of FCA algorithms and about 76% of the best channel of FCA algorithms, and the human presence detection rate reached 87.4%.https://doi.org/10.1155/2013/927940 |
| spellingShingle | Weijun Qin Jiadi Zhang Bo Li Limin Sun Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective International Journal of Distributed Sensor Networks |
| title | Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective |
| title_full | Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective |
| title_fullStr | Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective |
| title_full_unstemmed | Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective |
| title_short | Discovering Human Presence Activities with Smartphones Using Nonintrusive Wi-Fi Sniffer Sensors: The Big Data Prospective |
| title_sort | discovering human presence activities with smartphones using nonintrusive wi fi sniffer sensors the big data prospective |
| url | https://doi.org/10.1155/2013/927940 |
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