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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\u2009s. <\/jats:p>","DOI":"10.1155\/2013\/246916","type":"journal-article","created":{"date-parts":[[2013,10,21]],"date-time":"2013-10-21T22:01:00Z","timestamp":1382392860000},"page":"246916","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments"],"prefix":"10.1177","volume":"9","author":[{"given":"Lin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"},{"name":"The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, 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