A Smart High Way based on Deep Learning using IOT Devices

Abstract

Internet of Things is an emerging technology that enhances our daily life activities efficiently and effectively. It reduces the cost of living by automating manual processes. Solar systems are often built along highways where electric utilities are not yet available. These systems are operated manually by humans. Therefore, there is a need for an efficient approach that automatically controls and monitors current, voltage and other parameters of solar systems and provides real-time statistics to users. A novel Toll Google Net is proposed to overcome these issues. The solar panel is utilized to develop lithium battery-storage capable renewable energy. The Adafruit software, which is used to assess the pollutants and save daily usage in the cloud, is interfaced with the IOT monitoring system. The proposed system’s experimental setup gathers real-time field data like temperature, air quality, IR, and proximity sensor readings. The cloud system receives these sensed instances for timely analysis. The experimental arrangement of the proposed technique based smart appliances was implemented using MATLAB. Accuracy, specificity, precision, and recall are the different metrics used to evaluate it. Experimental results shows that the proposed Toll Google Net attains better accuracy than existing IoT-SGE, EMS-IoT, and MODDA respectively.