Massive Internet of Things (IoT) is foretold to introduce surfeit of applications for a fully connected world. The increasingly degrading quality of air in several regions of the biosphere can be a reliable impact of human difficulties of increasing globalization and urbanization. Air pollution levels in many cities exceed legal and World Health Organization (WHO) limits for particulate matter and gaseous pollutants which can be found in concentrations that are hazardous to health. Regular acquaintance to high pollution intensities results in increase of humans affected from respiratory disorders such as asthma, chronic obstructive lung disease and increased mortality. In most of the cities, there is no chance for peoples to apprehend the levels of pollution they are undergoing in their day-to-day lives which is necessary to reduce their risks from poor air quality conditions. The quality of air can be measured by using a parameter named Air quality index. The Key pollutants to calculate Air quality index are particulate matter (PM2.5 and PM10), Ozone (O3), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2) and Carbon Monoxide (CO). This work deliberates the implementation of cloud based IoT system for air quality monitoring in which the sensors are used to calculate CO, PM2.5 and PM10, O3, SO2 and NOx pollution level with environmental condition like temperature and humidity. The obtained information can be updated in cloud platform using Lora nodes and Lora Gateway. The web-based application developed has a Google map API where the pollution status can be frequently updated. With the collected time series samples, the prediction analysis was done for PM with neural network Multi-Layer perceptron and support vector machine regression (SVMR) learning algorithm. This can helps a person to travel to any other places by automatically rerouting conditions in a pollution free environment.

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In this study, we propose an adaptive transmission power aware cluster scheduling algorithm using multiple channels in a WPAN in the presence of WLAN interference. Results: The algorithm includes node identification, channel allocation, clustering and time scheduling. To evaluate the performance of the proposed algorithm, the performance metrics such as Bit error, Packet Error Rate (PER), Throughput, Average End-End Delay and Average Jitter is measured through Qualnet simulation. PER is calculated from bit error rate. The simulation results are compared with the conventional TDMA scheme. Conclusion/Recommendations: The measurement result shows that the proposed algorithm is effective in an IEEE 802.15.4 cluster-tree network in the presence of multiple IEEE 802.11 interferers.

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