Canoe: An Autonomous Infrastructure-Free Indoor Navigation System
The development of the Internet of Things (IoT) has accelerated research in indoor navigation systems, a majority of which rely on adequate wireless signals and sources. Nonetheless, deploying such a system requires periodic site-survey, which is time consuming and labor intensive. To address this i...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/5/996 |
| _version_ | 1798041026774958080 |
|---|---|
| author | Kai Dong Wenjia Wu Haibo Ye Ming Yang Zhen Ling Wei Yu |
| author_facet | Kai Dong Wenjia Wu Haibo Ye Ming Yang Zhen Ling Wei Yu |
| author_sort | Kai Dong |
| collection | DOAJ |
| description | The development of the Internet of Things (IoT) has accelerated research in indoor navigation systems, a majority of which rely on adequate wireless signals and sources. Nonetheless, deploying such a system requires periodic site-survey, which is time consuming and labor intensive. To address this issue, in this paper we present Canoe, an indoor navigation system that considers shopping mall scenarios. In our system, we do not assume any prior knowledge, such as floor-plan or the shop locations, access point placement or power settings, historical RSS measurements or fingerprints, etc. Instead, Canoe requires only that the shop owners collect and publish RSS values at the entrances of their shops and can direct a consumer to any of these shops by comparing the observed RSS values. The locations of the consumers and the shops are estimated using maximum likelihood estimation. In doing this, the direction of the target shop relative to the current orientation of the consumer can be precisely computed, such that the direction that a consumer should move can be determined. We have conducted extensive simulations using a real-world dataset. Our experiments in a real shopping mall demonstrate that if 50% of the shops publish their RSS values, Canoe can precisely navigate a consumer within 30 s, with an error rate below 9%. |
| first_indexed | 2024-04-11T22:15:44Z |
| format | Article |
| id | doaj.art-c808a47b70db4568ba817ef179d0fd7a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:15:44Z |
| publishDate | 2017-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c808a47b70db4568ba817ef179d0fd7a2022-12-22T04:00:26ZengMDPI AGSensors1424-82202017-04-0117599610.3390/s17050996s17050996Canoe: An Autonomous Infrastructure-Free Indoor Navigation SystemKai Dong0Wenjia Wu1Haibo Ye2Ming Yang3Zhen Ling4Wei Yu5School of Computer Science and Engineering, Southeast University, Nangjing 211189, ChinaSchool of Computer Science and Engineering, Southeast University, Nangjing 211189, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nangjing 210016, ChinaSchool of Computer Science and Engineering, Southeast University, Nangjing 211189, ChinaSchool of Computer Science and Engineering, Southeast University, Nangjing 211189, ChinaDepartment of Computer and Information Sciences, Towson University, Towson MD 21252, USAThe development of the Internet of Things (IoT) has accelerated research in indoor navigation systems, a majority of which rely on adequate wireless signals and sources. Nonetheless, deploying such a system requires periodic site-survey, which is time consuming and labor intensive. To address this issue, in this paper we present Canoe, an indoor navigation system that considers shopping mall scenarios. In our system, we do not assume any prior knowledge, such as floor-plan or the shop locations, access point placement or power settings, historical RSS measurements or fingerprints, etc. Instead, Canoe requires only that the shop owners collect and publish RSS values at the entrances of their shops and can direct a consumer to any of these shops by comparing the observed RSS values. The locations of the consumers and the shops are estimated using maximum likelihood estimation. In doing this, the direction of the target shop relative to the current orientation of the consumer can be precisely computed, such that the direction that a consumer should move can be determined. We have conducted extensive simulations using a real-world dataset. Our experiments in a real shopping mall demonstrate that if 50% of the shops publish their RSS values, Canoe can precisely navigate a consumer within 30 s, with an error rate below 9%.http://www.mdpi.com/1424-8220/17/5/996IoTknowledge extractionindoor navigationlocation fingerprintingmissed AP problemmaximum likelihood estimation |
| spellingShingle | Kai Dong Wenjia Wu Haibo Ye Ming Yang Zhen Ling Wei Yu Canoe: An Autonomous Infrastructure-Free Indoor Navigation System Sensors IoT knowledge extraction indoor navigation location fingerprinting missed AP problem maximum likelihood estimation |
| title | Canoe: An Autonomous Infrastructure-Free Indoor Navigation System |
| title_full | Canoe: An Autonomous Infrastructure-Free Indoor Navigation System |
| title_fullStr | Canoe: An Autonomous Infrastructure-Free Indoor Navigation System |
| title_full_unstemmed | Canoe: An Autonomous Infrastructure-Free Indoor Navigation System |
| title_short | Canoe: An Autonomous Infrastructure-Free Indoor Navigation System |
| title_sort | canoe an autonomous infrastructure free indoor navigation system |
| topic | IoT knowledge extraction indoor navigation location fingerprinting missed AP problem maximum likelihood estimation |
| url | http://www.mdpi.com/1424-8220/17/5/996 |
| work_keys_str_mv | AT kaidong canoeanautonomousinfrastructurefreeindoornavigationsystem AT wenjiawu canoeanautonomousinfrastructurefreeindoornavigationsystem AT haiboye canoeanautonomousinfrastructurefreeindoornavigationsystem AT mingyang canoeanautonomousinfrastructurefreeindoornavigationsystem AT zhenling canoeanautonomousinfrastructurefreeindoornavigationsystem AT weiyu canoeanautonomousinfrastructurefreeindoornavigationsystem |