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Title

IoT Based Street Lighting Using Dual Axis Solar Tracker and Effective Traffic Management System Using Deep Learning: Bangladesh Context

Author

, Nazmun Nessa Moon, Syeda Farjana Shetu,

Email

moon@daffodilvarsity.edu.bd

Abstract

With the rapid increase of population, nonrenewable energy loss has become one of the prior concerns worldwide in recent years. Researchers are working on the proper utilization of renewable energy to save the rest of the non-renewable energy sources for future generations. One of the purposes of this research is to develop an intelligent automation system that can decide luminous control operation based on light intensity. Here, LDR sensor is installed in such a way that it can be able to identify Night and Day mode using a certain intensity value, and as well as IR sensors take over the control of street light. The solar cell will be used for power supply here and DC current will work as a secondary backup. The article also involves automatic monitoring of traffic controls through a video system. The camera incorporates automatic text retrieval from video data utilizing lip reading by decoding facial expression utilizing deep learning methods. The proposed solution includes a test data collection, an interpretation of the picture frame, and a text production of the specified words. The test data collection will be structured in the proposed methodology by integrating all potential facial expressions of different words.

Keywords

Adruino Uno , Automation , Audio , Conversion , Dual Axis Sun Tracker , Energy Efficiency , Frame , IoT , Identification , IR sensor , LDR , LED , Monitoring , Solar Panel , Sequence , Training Data , Video , Word

Journal or Conference Name

11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020

Publication Year

2020

Indexing

scopus