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Coalescing IoT and Wi-Fi technologies for an optimized approach in urban route planning
Bhavya Deep1,2, Iti Mathur3, Nisheeth Joshi3
1Bhaskaracharya College of Applied Sciences, University of Delhi, Delhi, India. bhavya.deep@bcas.du.ac.in.
Abstract:
The quality of air that we breathe is one of the more serious environmental challenges that the government faces all around the world. It is a matter of concern for almost all developed and developing countries. The National Air Quality Index (NAQI) in India was first initiated and unveiled by the central government under the Swachh Bharat Abhiyan (Clean India Campaign). It was launched to spread cleanliness, and awareness to work towards a clean and healthy environment among all citizens living in India. This index is computed based on values obtained by monitoring eight types of pollutants that are known to commonly permeate around our immediate environment. These are particulate matter PM10; particulate matter PM2.5; nitrogen dioxide; sulfur dioxide; carbon monoxide; lead; ammonia; and ozone. Studies conducted have shown that almost 90% of particulate matters are produced from vehicular emissions, dust, debris on roads, and industries and from construction sites spanning across rural, semi-urban, and urban areas. While the State and Central governments have devised and implemented several schemes to keep air pollution levels under control, these alone have proved inadequate in cases such as the Delhi region of India. Internet of Things (IoT) offers a range of options that do extends into the domain of environmental management. Using an online monitoring system based on IoT technologies, users can stay informed on fluctuating levels of air pollution. In this paper, the design of a low-price pollution measurement kit working around a dust sensor, capable of transmitting data to a cloud service through a Wi-Fi module, is described. A system overview of urban route planning is also proposed. The proposed model can make users aware of pollutant concentrations at any point of time and can also act as useful input towards the design of the least polluted path prediction app. Hence, the proposed model can help travelers to plan a less polluted route in urban areas.
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