Monitoring of air pollution to establish optimal less polluted path by utilizing wireless sensor network

An efficient air pollution monitoring (APM) scheme is proposed to establish an optimal less polluted path using WSN (wireless sensor network), in which the sensor node (SN) senses the temperature and CO gas (carbon monoxide) concentration’s existent in the air. Initially, the sensed information from the SNs is preprocessed. During preprocessing, the value that is missed in the sensed information is imputed. Next, Hadoop's distributed file system (HDFS) MapReduce (MR) is implemented on the preprocessed data and subsequently, the resulting data is saved in the cloud server. The resulting data is analyzed using Improved-Adaptive Neuro-Fuzzy Inference System (I-ANFIS) Algorithm for checking air pollutions severities and its location is then presented in the Google Map. After that, the multi-path routing is established through the less polluted area. Lastly, the optimal path is chosen with the assistance of KHOA (Krill Herd Optimization Algorithm). The outcomes are evaluated by contrasting the proposed and prevailing techniques.