SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map
文献类型：期刊论文
| 作者 | Luo, Haiyong1; Wei, Jie2; Zhao, Fang2 |
| 刊名 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
 |
| 出版日期 | 2018-11-29 |
| 卷号 | 14期号:11页码:18 |
| 关键词 | Indoor localization shop-level localization crowdsourcing fingerprints shop popularity property supervised learning |
| ISSN号 | 1550-1477 |
| DOI | 10.1177/1550147718815637 |
| 英文摘要 | With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization accuracy. |
| 资助项目 | National Key Research and Development Program[2018YFB0505200] ; National Natural Science Foundation of China[61872046] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000452829800001 |
| 出版者 | SAGE PUBLICATIONS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/3520]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Luo, Haiyong |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Beijing Univ Post & Telecommun, Sch Software Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Haiyong,Wei, Jie,Zhao, Fang. SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map[J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,2018,14(11):18. |
| APA | Luo, Haiyong,Wei, Jie,&Zhao, Fang.(2018).SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map.INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,14(11),18. |
| MLA | Luo, Haiyong,et al."SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map".INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 14.11(2018):18. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。