Improving air pollution detection accuracy and quality monitoring based on bidirectional RNN and the Internet of Things

Apart from the detection of air pollution, the accuracy in obtained data has to be focussed, and quality has to be monitored. This can be achieved by analyzing the environment through IoT and adapting neural network. Analysis of air pollution monitoring system involves high precision due to cost and maintenance. When the Internet of Things (IoT) is involved in predicting the nature of the environment, the process becomes dynamic. Hardware cost is reduced while the system accommodates the monitoring area through the sensor network. Air pollution quality monitoring is performed by forecasting the status frequently within time slots by neural network technology on the perception system. The proposed work involves integrating a bidirectional Recurrent Neural Network (RNN), which handles forecasting and modelling air pollution on a timely basis as required. It engages neurons that are self-connected for implementing cyclic structure in the network. RNN handles both current input and historical input for monitoring the quality of air pollution detection. It involves processing the temporal dependencies directly.