Issue |
E3S Web Conf.
Volume 309, 2021
3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|
|
---|---|---|
Article Number | 01024 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/e3sconf/202130901024 | |
Published online | 07 October 2021 |
Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison
1 MTech Student, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
2 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
* Corresponding author: m.srividya1998@gmail.com
Internet of Things connects various physical objects and form a network to do the services for sensing the physical things without any human intervention. They compute the data, retrieve the data by the network connections made through IoT device components such as Sensors, Protocols, Address, etc., The Global Positioning System (GPS) is used for localization in outer areas such as roads, and ground but cannot be used for Indoor environment. So, while using Indoor Environment, finding or locating an object is not possible by GPS. Therefore by using IoT devices such as Wi-Fi routers in Indoor Environment can localize the objects. It can be done by using Received Signal Strengths (RSSs) from a Wi-Fi router. But by using RSSs in Wi-Fi, there are disturbances, reflections, interferences are caused. By using Outlier detection techniques for localization can identify the objects clearly without any interruptions, noises, and irregular signal strengths. This paper produces research about Indoor Situating Environment and various techniques already used for localization and form the effective solution. The several methods used are compared and form a result to make the further computation in the Indoor Environment. The Comparison is done in order to find the effective and more accurate Machine Learning algorithms used for Indoor Localization.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.