IoT for Smart Cities

SATVAM: Streaming Analytics over Temporal Variables from Air quality Monitoring

Srijith Nair and Sumit Monga, in collaboration with ECE/IISc, IIT-K, IIT-B and Duke University

Motivation

Air pollution is ranked as the second most serious risk for public health in India after malnutrition.  Vehicular pollution, industrial pollution and burning of crop residue are some of the major causes of air pollution.  Tackling this issue requires a careful study of air quality monitoring at city-wide scales and at finer spatial and temporal granularity than currently done.  The collection of spatially and temporally distributed air quality information is thus necessary to enhance the scientific study of its impact on human health and on the national economy.  The limited number of reference air quality monitors – largely due to their high cost – is a major hindrance for such a study.  In this project, we aim at using cost-effective and low-power gas sensor nodes along with Edge and Cloud technologies to enable data-driven policies for real time urban air quality management.

Contributions

  • Calibration of electrochemical gas sensors for measuring the concentration of harmful gases such as NO2, SO2, Oand particulate matter categorized as PM2.5 , PM10 present in the air by co-locating them with higher quality sensors (reference monitors) so as to validate the gas sensor nodes.
  • Air quality data acquisition from the gas sensor nodes connected through a WiSUN compliant low-power wide area network (LPWAN), utilizing Edge devices for basic analytics and subsequent visualization in the Cloud.

Software

  • TBD

Publications

  • Toward SATVAM: An IoT Network for Air Quality Monitoring, Rashmi Ballamajalu, Srijith Nair, Shayal Chhabra, Sumit K Monga, Anand SVR, Malati Hegde, Yogesh Simmhan, Anamika Sharma, Chandan M Choudhary, Ronak Sutaria, Rajesh Zele, Sachchida N. Tripathi, arXiv:1811.07847, 2018

Sponsors

  • Indo US Science and Technology Forum (IUSSTF), Intel and DST

EQWATER: Intelligent Water Supply Network Monitoring and Control for EQuitable Distribution of WATER within a Mega city

Richu Saxena and Prithvi Alva, with ICWAR, Civil, RBCCPS and ECE Departments at IISc

Motivation

It is challenging to provide potable water in sufficient quantity, at adequate pressure and at an acceptable quality at a consumer’s tap. However, with the incorporation of IoT technologies of sensing and communications, along with advanced models of hydraulics, algorithms for controls, optimization and scheduling, we believe that equity in the water distribution can be addressed. This proposal brings together experts from multiple disciplines in IISc, Bangalore Water Supply Sewerage Board (BWSSB), and companies that are delivering services to BWSSB, to undertake this project. The data gathered at the division level will be analysed to understand the supply/demand pattern (rate of supply and pressure), adequacy of infrastructure, and issues of quality. Sensor data and the utility information will be used and the develpoed analytical models will be applied. A pilot project of rigorous modelling and monitoring will be taken up at a service station to offer guidelines for better management and operation of water systems.

Contributions

We’ve built a platform whose architecture consists of a data ingest pipeline, a data storage layer, a query and analytics engine, and a visualization portal. It acquires observational data periodically, and uses Apache Spark SQL to perform data quality checks and transformations to generate sanitized and standardized data. An analytics service offers ad hoc and pre-defined queries related to inequity and UFW (Unaccounted for Water). This is accessed from a visualization portal for water managers and analysts, which has a geo-spatial interface to conduct independent analysis on individual DMAs (District Meter Area).

Software

  • TBD

Publications

  • Under review

Sponsors

  • IMPRINT, MHRD and MU

Data Management, Analytics and Planning for Drone Fleet Operations

Motivation

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Contributions

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Software

  • TBD

Publications

  • TBD