Next Article in Journal
Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
Next Article in Special Issue
Social Welfare Control in Mobile Crowdsensing Using Zero-Determinant Strategy
Previous Article in Journal
A Lever Coupling Mechanism in Dual-Mass Micro-Gyroscopes for Improving the Shock Resistance along the Driving Direction
Previous Article in Special Issue
Time-Aware Service Ranking Prediction in the Internet of Things Environment
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 996; doi:10.3390/s17050996

Canoe: An Autonomous Infrastructure-Free Indoor Navigation System

1
School of Computer Science and Engineering, Southeast University, Nangjing 211189, China
2
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nangjing 210016, China
3
Department of Computer and Information Sciences, Towson University, Towson MD 21252, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Yunchuan Sun, Zhipeng Cai and Antonio Jara
Received: 28 February 2017 / Revised: 24 April 2017 / Accepted: 25 April 2017 / Published: 30 April 2017
View Full-Text   |   Download PDF [522 KB, uploaded 5 May 2017]   |  

Abstract

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%. View Full-Text
Keywords: IoT; knowledge extraction; indoor navigation; location fingerprinting; missed AP problem; maximum likelihood estimation IoT; knowledge extraction; indoor navigation; location fingerprinting; missed AP problem; maximum likelihood estimation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Dong, K.; Wu, W.; Ye, H.; Yang, M.; Ling, Z.; Yu, W. Canoe: An Autonomous Infrastructure-Free Indoor Navigation System. Sensors 2017, 17, 996.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top