Details
| Original language | English |
|---|---|
| Title of host publication | 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (electronic) | 9781538637319 |
| ISBN (print) | 978-1-5386-3732-6 |
| Publication status | Published - 2 Jul 2017 |
| Event | 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017 - Larnaca, Cyprus Duration: 27 Aug 2017 → 30 Aug 2017 |
Abstract
Position determination in an indoor environment has become a widely discussed problem, due to the growing complexity of building layouts and the lack of any natural heuristics for orientation as compared to the case outdoors. Additionally there is no universal standard for indoor positioning, such as GPS, which however cannot be used for this purpose. Locating oneself in a building serves an increasingly vital function, especially in time-critical scenarios such as airports etc. The use of expensive hardware may assist in solving this problem, which has been studied thoroughly with different technologies being used to achieve a precision of within a few meters. Nevertheless these methods have remained in the academic realm for the most part. This is largely due to the high costs and labour of such hardware installations and the construction of software to interpret the measurements. The goal of this paper is to use existing wireless LAN access points in a building and user-provided smartphones to create a cost-effective positioning system, by omitting the labour and cost of altering building infrastructure, and at the same time simplifying the construction of classifiers for real-life use-cases. An alternative approach using image recognition techniques is presented, for a purely web-based solution.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Hardware and Architecture
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Software
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Social Sciences (miscellaneous)
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2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Implementing real-life indoor positioning systems using machine learning approaches
AU - Becker, Matthias
AU - Ahuja, Bharat
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Position determination in an indoor environment has become a widely discussed problem, due to the growing complexity of building layouts and the lack of any natural heuristics for orientation as compared to the case outdoors. Additionally there is no universal standard for indoor positioning, such as GPS, which however cannot be used for this purpose. Locating oneself in a building serves an increasingly vital function, especially in time-critical scenarios such as airports etc. The use of expensive hardware may assist in solving this problem, which has been studied thoroughly with different technologies being used to achieve a precision of within a few meters. Nevertheless these methods have remained in the academic realm for the most part. This is largely due to the high costs and labour of such hardware installations and the construction of software to interpret the measurements. The goal of this paper is to use existing wireless LAN access points in a building and user-provided smartphones to create a cost-effective positioning system, by omitting the labour and cost of altering building infrastructure, and at the same time simplifying the construction of classifiers for real-life use-cases. An alternative approach using image recognition techniques is presented, for a purely web-based solution.
AB - Position determination in an indoor environment has become a widely discussed problem, due to the growing complexity of building layouts and the lack of any natural heuristics for orientation as compared to the case outdoors. Additionally there is no universal standard for indoor positioning, such as GPS, which however cannot be used for this purpose. Locating oneself in a building serves an increasingly vital function, especially in time-critical scenarios such as airports etc. The use of expensive hardware may assist in solving this problem, which has been studied thoroughly with different technologies being used to achieve a precision of within a few meters. Nevertheless these methods have remained in the academic realm for the most part. This is largely due to the high costs and labour of such hardware installations and the construction of software to interpret the measurements. The goal of this paper is to use existing wireless LAN access points in a building and user-provided smartphones to create a cost-effective positioning system, by omitting the labour and cost of altering building infrastructure, and at the same time simplifying the construction of classifiers for real-life use-cases. An alternative approach using image recognition techniques is presented, for a purely web-based solution.
UR - http://www.scopus.com/inward/record.url?scp=85047884905&partnerID=8YFLogxK
U2 - 10.1109/IISA.2017.8316429
DO - 10.1109/IISA.2017.8316429
M3 - Conference contribution
AN - SCOPUS:85047884905
SN - 978-1-5386-3732-6
SP - 1
EP - 6
BT - 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017
Y2 - 27 August 2017 through 30 August 2017
ER -