N. Rama Kalpana, Adithya Institute of Technology, Coimbatore, Tamilnadu, India.
DOI : 01.0401/ijaict.2017.03.04
International Journal of Advanced Information and Communication Technology
Received On : August 10, 2019
Revised On : September 11, 2019
Accepted On : October 12, 2019
Published On : November 05, 2019
Volume 06, Issue 11
Pages : 1186-1190
Abstract
Coal mining has various fatal factors which menace the people. After a coal mine disaster the situation inside the tunnel is very dangerous as well as situation inside the tunnel is not known to the rescuers. Entering into a mine without knowing the exact situation is dangerous as the chances of second explosion is high as well as the disaster results in increased level of harmful gases like CO,CO2,decreased oxygen level and high temperature. Many rescuers are killed by this increased level of harmful gases and second explosion. Detecting this situation inside the tunnel is main objective of this paper. The robot can go into the explosive environment with autonomous obstacle avoidance system. The sensors Senses the harmful gases such as CO, CO2, Methane, temperature and transmits the data through wireless module. A coal mine robot can move into the mine and detect the level of different toxic gases and temperature level and send the result to the control room. At present, rescue robot using 8052 microcontroller senses the harmful gases & temperature condition in the mine and transmit the data via zigbee wireless technology. In the proposed method, the data can be transmitted through MSP430 based long range wireless module(CC1120) and the controller used for this project is MSP430.The coal mining rescue robot having ultralow power consumption and compact in size.
Keywords
Big Data, Relational Database, Information Privacy, Revolution Analytics.
Cite this article
N. Rama Kalpana, “Revolution Data Analytics in Hadoop” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.1186-1190, November 05, 2019.
Copyright
© 2019 N. Rama Kalpana. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.