Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments
Neighbor discovery for moving individual is considered an important technology submitting to location-based service (LBS), which includes such things as recruitment flow of information, logical localization, and health monitoring. Based on the tradeoff between universality and accuracy of neighbor d...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/246916 |
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| _version_ | 1850168063791464448 |
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| author | Lin Wang Jing Yang Wenyuan Liu |
| author_facet | Lin Wang Jing Yang Wenyuan Liu |
| author_sort | Lin Wang |
| collection | DOAJ |
| description | Neighbor discovery for moving individual is considered an important technology submitting to location-based service (LBS), which includes such things as recruitment flow of information, logical localization, and health monitoring. Based on the tradeoff between universality and accuracy of neighbor discovery, we propose the environmental characteristics participatory extraction method benefiting to mobile individual discovery. First, we fuse lightweight accelerometer, light sensors, and microphone collaboratively. Furthermore, support vector machine (SVM), Tanimoto coefficient, and Manhattan distance are used to calculate three kinds of fingerprint similarity, respectively, and then the principal component analysis based method reduces data dimension in order to obtain neighbor similarity rank. Finally, the experiment data are collected by 25 volunteers, and trace-driven simulations show that Euclidean distance error is below 4.69 and the convergence time is within 0.75 s. |
| format | Article |
| id | doaj-art-e6411e94597b45e7bfa4ba81e3f8d3f6 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-e6411e94597b45e7bfa4ba81e3f8d3f62025-08-20T02:21:03ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-10-01910.1155/2013/246916Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded EnvironmentsLin Wang0Jing Yang1Wenyuan Liu2 The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, ChinaNeighbor discovery for moving individual is considered an important technology submitting to location-based service (LBS), which includes such things as recruitment flow of information, logical localization, and health monitoring. Based on the tradeoff between universality and accuracy of neighbor discovery, we propose the environmental characteristics participatory extraction method benefiting to mobile individual discovery. First, we fuse lightweight accelerometer, light sensors, and microphone collaboratively. Furthermore, support vector machine (SVM), Tanimoto coefficient, and Manhattan distance are used to calculate three kinds of fingerprint similarity, respectively, and then the principal component analysis based method reduces data dimension in order to obtain neighbor similarity rank. Finally, the experiment data are collected by 25 volunteers, and trace-driven simulations show that Euclidean distance error is below 4.69 and the convergence time is within 0.75 s.https://doi.org/10.1155/2013/246916 |
| spellingShingle | Lin Wang Jing Yang Wenyuan Liu Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments International Journal of Distributed Sensor Networks |
| title | Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments |
| title_full | Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments |
| title_fullStr | Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments |
| title_full_unstemmed | Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments |
| title_short | Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments |
| title_sort | leveraging participatory extraction to mobility sensing for individual discovery in crowded environments |
| url | https://doi.org/10.1155/2013/246916 |
| work_keys_str_mv | AT linwang leveragingparticipatoryextractiontomobilitysensingforindividualdiscoveryincrowdedenvironments AT jingyang leveragingparticipatoryextractiontomobilitysensingforindividualdiscoveryincrowdedenvironments AT wenyuanliu leveragingparticipatoryextractiontomobilitysensingforindividualdiscoveryincrowdedenvironments |