Chinthalapani Rajashekar Reddy received his MS Dual Degree in Electronics and Communication Engineering (ECE). His research work was supervised by Dr. Sachin Chaudhari. Here’s a summary of his research work on IoT-based Air Pollution Monitoring: Algorithms and Implementation:

Traditional air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. With the advent of internet of things (IoT) technology, millions of smart devices will be connected to each other as well as to the internet enabling easier air pollution monitoring. Low-cost portable sensors along with IoT can overcome the above two issues of traditional monitoring systems.

This thesis focuses mainly on three aspects. Firstly, research, development and deployment of IoT-based dense air pollution monitoring using network low-cost sensor nodes in an Indian urban setting which is first of kind in India in terms of dense deployment and spatial coverage. Second, it deals with improving the energy consumption of the sensor nodes developed and finally, reducing the redundancy of the nodes. More specifically, a new adaptive sensing algorithm and hierarchical clustering based spatial sampling paradigm have been introduced. The results of the proposed implementations have been evaluated upon real-time deployment of the nodes. 

In total, 10 low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are developed and deployed in a small educational campus in Hyderabad. New datasets were created in the process which are thoroughly investigated by applying various preprocessing, signal processing and clustering techniques. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.

For reducing energy consumption, an adaptive, non-parametric method to change the sensing rate using the maximum frequency estimate based on recent historical data. The proposed algorithm has been tested on the data collected over one year from an IoT network consisting of multiple PM sensor nodes. A performance comparison of the proposed scheme with the existing approach shows the effectiveness and performance improvement in terms of Reduction Factor (RF) and Mean Absolute Error (MAE).

For removing the redundant nodes, a framework based on Hierarchical Agglomerative Clustering for deciding an optimal number of nodes and which nodes in an IoT network of sensor nodes has been proposed. The approach proposed is an end to end framework to obtain the required number of nodes and their location based on an error threshold in a deployed IoT network. The framework’s performance has been compared with the brute force approach, which entirely relies on comparing all the possible combinations. Finally the trade-off between the sensor nodes’ spatial density and the error in the spatial reconstruction for an optimal spatial understanding of PM values has been presented.