التفاصيل البيبلوغرافية
| العنوان: |
Smart Building: A Low Cost Indoor Positioning and Intelligent Path Finding |
| المؤلفون: |
Kiani, Farzad, Gunagwera, Alex |
| المصدر: |
International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 3 No. 01 (2019): Volume No 03 Issue No 01; 16-34 ; 2581-3242 |
| بيانات النشر: |
SR Informatics, New Delhi, India |
| سنة النشر: |
2019 |
| المجموعة: |
International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) |
| مصطلحات موضوعية: |
Indoor navigation, tracking algorithm, fingerprinting, visually impaired, dead reckoning |
| الوصف: |
Despite the rapid improvement in mobile devices, overall gradual growth in the ubiquitous computing field, the wide applicability, more usefulness of location based services in general and indoor navigation. The Global Positioning System (GPS) has undergone tremendous improvement since the 1900s and it, indeed is considered one of the most successful navigation systems known to date. However, it is still inefficient for sufficiently accurate positioning in both indoor environments and environments with many tall buildings such as skyscrapers since such buildings block or interfere with its signal transmissions. In particular, building a sufficiently accurate, efficient and relatively cheap indoor navigation system in a GPS-free environment is still a challenging task with a lot of tradeoffs and constraints to put into consideration. In this paper, a simple yet robust, low-cost, context-aware user-interactive, user-friendly hybrid of fingerprinting and dead reckoning indoor navigation system suitable for both the visually and the physically disabled as well that takes advantage of the results yielded by sensor fusion is proposed. The presented system is also designed to allow for efficient evacuation of users in cases of emergences. The prototype is made majorly of the following parts; user tracking, optimal, context-aware and dynamic route calculation and planning and dynamic route representation with an upper bound of 2m and an average of 0.8-1.3m accuracy. All that is required from the user is a smart phone without installation of extra hardware. |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| العلاقة: |
http://www.mlnce.net/index.php/Home/article/view/67/49Test; http://www.mlnce.net/index.php/Home/article/view/67Test |
| الإتاحة: |
http://www.mlnce.net/index.php/Home/article/view/67Test |
| حقوق: |
Copyright (c) 2019 International Journal of Machine Learning and Networked Collaborative Engineering |
| رقم الانضمام: |
edsbas.EA373B40 |
| قاعدة البيانات: |
BASE |
