November 2022
Souradeep received his Master of Science in Electronics and Communication Engineering (ECE). His research work was supervised by Dr. Sachin Chaudhari. Here’s a summary of his research work on Stationary vs Mobile IoT Nodes: A Spatio-Temporal Analysis of PM, Air Pollution:
Air quality monitoring and analysis of a given location can be challenging without being able to determine the factors influencing its behavior. Stationary air pollution monitoring systems mostly account for temporal variations in few locations due to the traditional choice of sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. Internet of things (IoT) has allowed deployment of cost-efficient networks of smart devices with an ease in connectivity to the internet enabling easier air pollution monitoring. This thesis mainly proposes two test cases that highlight prominent factors that deeply influence the pollution pattern in a location. In the first test case, a temporal variation analysis is performed on two networks of pollution monitoring systems to understand the effects of implementing a nationwide lockdown during the COVID-19 pandemic. The first network comprises of the costly and bulky air quality monitoring stations of Central Pollution Control Board (CPCB) sparsely deployed in Hyderabad city and the second network comprises of the smaller and low-cost IoT nodes densely deployed inside the IIIT-H campus. Differential analyses were conducted on the data to understand the effects of lockdown on Particulate Matter (PM) levels by factoring in the yearly and seasonal trends, followed by Welch’s test to check whether the PM values have changed w.r.t. values in pre-lockdown period. Lastly, Pearson pair-wise correlation coefficient is estimated between PM and temperature values to show the effect of temperature changes on the PM values irrespective of lockdown. This test case established the effects of human-centric factors such as vehicular and industrial emissions, commercial activities and commotion on ambient PM variations as a sudden drop in the anthropogenic activities during the nationwide lockdown has caused a decline in PM levels. In the second test case, a novel methodology to analyze a moving object database is proposed by performing a case study on mobile IoT data collecting Particulate Matter across a road stretch of India, and look into the neighboring spatial and anthropogenic factors such as human activities, settlement patterns and vegetation profile corresponding to each geo-location of the PM data. The thematic interactions of spatial and anthropogenic factors of each location with the corresponding ambient PM levels resulted in a factor-based data structure that highlights the PM distribution mapped to each factor. This case study not only enabled to showcase the influence of spatial and anthropogenic factors of a location in influencing its ambient PM levels, but also provided a use case for handling mobile object databases, a challenging issue prevalent amidst the GIS community. Both the test cases effectively demonstrated the contribution of different surrounding factors in affecting the PM values, thus providing a gateway for accurately modeling the ambient air quality data based on key parameters.