Rohit is an Electronics and Communications Engineering graduate. His expertise is in the Internet of Things (IoT) devices. IoT includes “smart” home appliances, health trackers, environmental monitors and Transport Data Loggers (such as TDL- Bosch GmBh, Germany).
His employs a variety of deep learning techniques. These range from data analysis and machine learning, to human-computer interaction methods such as Neural Networks and Artificial Intelligence based modelling.
The World Health Organisation (WHO) estimates that air pollution was responsible for 1 in 8 of the total number of deaths worldwide in 2012.
This project aims to develop models and techniques to improve how we monitor and communicate air pollution levels in cities. This new approach should be achievable without the need to significantly invest in monitoring equipment. The project will explore how nitrogen dioxide relates to particulates (PM10/2.5) and how can other indicators in cities be used to evaluate air quality. The aim is to allow people to avoid areas with a high concentration of pollution.
Additionally, the project explores how high-quality data (e.g. AURN – DEFRA data sets) can support the development of models and techniques. Using these data sets could considerably improve near real-time monitoring and communication of the pollution level in cities.
The model will be tested in Sheffield. We will work with local electronics company Pimoroni to develop the analytical equipment. After this we will test the low-cost IoT network in a city in a developing country (Kampala).
The DEFRA high quality fixed sensors will be combined with high-quality mobile sensing vehicles from the Urban Flows Observatory. We will also use Sentinel-5P Satellite data. This satellite data focuses on the measurement of reactive gas pollutants (such as NO2, CO, and SO2, along with PM2.5 & PM10) under prevailing meteorological conditions.
These will be used to calibrate and validate data from a network of Internet of Things based Low-Cost sensors (LCSs). Through an app on mobile phones, these sensors track NO2, CO, SO2 and fine particulate matter concentration. This will allow us to assess how the DEFRA data sets can be integrated and utilised with the LCS network when assessing gas and particulate readings in a city.
The project will comprise but is not limited to i) design, development and construction of a pollution analysis instrument ii) data analysis and visualization, iii) development and validation of statistical models and algorithms for detection and estimation and short term prediction of air pollution concentration and the inference of particulate levels, iv) energy efficiency of the proposed approaches, iv) integration in a decision making system.
Unlike previous models that only encode data related to spatial locations, this project develops and validates statistical models and deep learning algorithms to capture the spatiotemporal dependency for detection, estimation and multistep short-term prediction of air pollution concentration and the inference of particulate levels.
We will also identify and incorporate other types of data provided by “social sensors”, e.g. people equipped with mobile wearable sensors such as Flow sensors from Plume labs. The project will develop methods both for people-centric and environment-centric applications. The sensor node can be on a mobile app, such as a cell phone but can also be on a vehicle platform. The research will focus on the development of the workflow to integrate DAFNI with live DEFRA and IoT sensor data novel algorithms in the context of three particular drivers.
The research will explore the development of scalable approaches for big data management. Compressed sensing methods will be adopted that allow for sampling and retaining only the most informative data.
Currently, Rohit is working with a Paris based company Plume labs on use of mobile sensors. He is also working with Insplorion – a Swedish startup – to understand how to use Nano-Plasmonic sensing to measure NOx Gases.
Rohit is involved in citizen-science based projects. For example, organising workshops to build low-cost monitors. He has also used mobile trackers on school-runs to assess how much a child is exposed to pollutants in Sheffield. The hope is that the awareness will help bring a paradigm shift in policy making and reducing pollution overall.
For more updates follow me on Twitter @rcrohit7
Read Rohit’s blog: low-cost ‘Volunteer Sensors’ to help solve the air pollution problem in Sheffield