Abstract
Abstract: In smart cities there are systems which monitor the air pollution levels at different locations. Supervising the information given by the gas sensor devices may be a tedious task because the sensor data is simply too huge to investigate manually. Further because of electrical interference and environmental conditions, the sensors may show false values and trigger alarms. Just in case the sensor gives any anomalous values then the values should be eliminated from analysis and triggering of alarms should be stopped. The damaged sensors must get replaced as early as possible. This paper proposes a Data Acquisition System (DAS) which measures humidity, temperature and air quality index at different locations. Just in case the sensor encounters some problem and provides anomalous values, it is detected in real time by comparing these values with predicted values of the ‘Moving Average Model’ in Python. These anomalous values can then be eliminated from analysis.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call