Major Thematic Area

Plan of the experimental works year-wise

  • Use of “Edge” and “Fog” technology to undertake fusion - This will primarily involve fusion of existing sensing technology from our or our parner groups that may be appropriate for water analysis. Essentially this means the IOT enabling of existing technology (Year 1).
  • As CDAC network is extended to different places in India , water toxicity mapping will be conducted in the respective centres and sub-centres using the edge based fog technogy that is IOT enabled and can be used to store data in cloud or process data in real time. This will have have a “big data” on water quality in extended map locations. (Year 2).
  • In this phase a set of new technology will be tried so that the portability and scalability of the sensing data can be increased. Fusionof sensors with robotic swans or drones may be done at this stage Similarly, scaled dwon microflidics based analytic device will be treid at this stage . The stage will also see the IPR activities if any singly or jointly with the partners. (year 3). To put it more systematically,
Year Target
IA Water Sensor to Sensor network (D2D)
IB Water based sensor to Cloud and Gate Backend interface (D2C)
IIA Development of Sensor Cloud with D2D and D2C
IIB Water based BIG data analytics
IIIA Microfluid based IOT compatible sensor development
IIIC Toying with Products and Data bases -Planning future development

Interaction and coordination of joint actions within the project

  • Principal components of water based pollutants in BRICS counries.
  • Each BRICS partner we choose one or more deployment locations and data geneeration can be preformed jointly.
  • Sensing methods to identify the same.
  • Joint activities in terms of
    • potential of similar sensors developed by various groups
    • interference studies due to varying pollutants and cliatic conditions

Expected results - description of those with evaluation of their originality - not more than half a page

  • New miniaturized sensors capable of generating big data containing GIS based sensing data.
  • This will be a portable Water Analysis platform and also will be pedagogical for health and environment.
  • SENSOR CLOUD dedicated to water quality (All BRICS Members)
  • Fabrication of Novel IOT comaptible portable sensors and their popularization in BRICS counries (India China and Russia)
  • Development of mathematical and Analytical models based on the SENSOR CLOUD - Big Data Analytics (Russia India & China )
  • Hybrid sensors based on microfluidic measurements and water based lab-on-chip technology (Brazil South Africa and India)

Modern state-of-the-art in your sub-field of the project and its comparison with the current world level – not more than 1 page

  • Potable water monitoring
    Common chemical parameters include pH, nitrates and dissolved oxygen. Measuring O2 (or DO) is an important gauge of water quality. Changes in dissolved oxygen levels indicate the presence of microorganisms from sewage, urban or agriculture runoff or discharge from factories. A right level of ORP minimizes the presence of microorganisms such as E. coli, Salmonella, Listeria. Levels of Turbidity below 1 NTU indicates the right purity of drinking water. One needs to find out whether sensors should be used in multiplexed manner or the fusion should occur in the data space. Similarly, in chemical leakage detection in rivers extreme pH or low DO values signal chemical spills due to sewage treatment plant or supply pipe problems. Unless real time cloud based monitoring is performed it will be impossible to minimize such hazards. The real time pollution level measurements may involve rivers and swimming pools. Remote measurements of oxidation-reduction potential, pH and Cloride levels of water can determine if the water quality in rivers or swimming pools pose no danger to human health. In similar lines we can consider pollution levels in the sea. Measuring levels of temperature, salinity, pH, oxygen and nitrates gives feedback for quality-sensing systems in seawater.
  • Big data and Small Devices
    The greatest challenges in generating big data from small devices (see Morik 2015) is that small devices are limited by memory and computation resources. The interplay of streaming and batch analytics needs appropriate redesigning of the device and reconstruction of the cloud network. The best recent application of small device and big data is thus the resources generated from app cabs concerning the traffic state of a given urban location at a given time.

  • Enabling Technologies
    The concept of combining computers, sensors, and networks to monitor and control devices has existed for decades(O’Flyrm et al. 2007). The recent confluence of several technology market trends, however, is bringing the Internet of Things (IOT) closer to widespread reality. Water seems an appropriate platform to deploy the IOT enabled sensing technology. The conference held a few years back (Perera et al. 2012) in which an IOT conference was held to discuss water air pollution is a proof for this. The subject is of global and national interest of interest to the basic scientists, analytic chemists and data scientists and computer scientists in equal proportions.

  • IOT the mediator of small devices and big data
    The term Internet of Things (IOT) generally refers to scenarios where network connectivity and computing capability extends to objects, sensors and everyday items not normally considered computers, allowing these devices to generate, exchange and consume data with minimal human intervention.

Previously achieved results of your team in the project related areas – not more than 1 page

  • Our previous experience shows that it is possible to have an initial assessment of water quality by E-Nose and E-Tounge technology. However the analytical methods can take robust shape if we can integrate the sensing technologies and make them IOT compatible. We plan to make the existing IOT enabling of the proven sensing technologies.
  • We will work out the right combination technology that be appropriate for enabling large scale detection. The algorithmic design that will enable simultaneous data-dialogue with cloud (which may be shared with the partner countries) will be undertaken as permissible by thelaws existing in the partner nation.
  • The technique will be employed in monitoring of local water bodies e.g. Ganges which flows from Himalaya to the Bay of Bengal.
  • How pollutants or important water biomarkers change water structure, dielectric properties and impedance and how to monitor them using optical and potentiometric sensors will be explored.
  • Conventionally estimation of organophosphorus pesticides residues like Monocrotophos, Quinalphos, Dimethoate, Chlorpyrifos in inland aquatic ecosystem was detected by proven analytical methods, like HPLC, GC-MS, LC-MS etc and the Biosensing System developed in our lab was callibrated against the validated result. The experience geneerated on Pesticide and other contamination detection systems provided us enough experience in benchmarking the strength of the newly developed sensor system and in being aware of some potential weakness that can be avoided in future sensor designs. As, development of biosensor based Sensing System was useful in on site pesticide/s estimation in Aquatic Ecosystem we can exploit the experience in larger scale water system monitoring.
  • In addition to this our previous experience in E-nose E-vision and E-tounge technology will help us in communating our partner counries in a synergetsic was.
  • The main problem of the present state of development in portable sensors capable of water pollutant measurement (Curtis et al. 2009)is that they are dedicated for small lab scale measurements.The scaling up part in water research should be conceived in a way different from that practiced in production engineering in which scaling up means advancements in rate of production of the product, or its usage at larger spatial and temporal scale. For analytical instruments scaling up means translation of data and data availability.Sometimes data itself may be the product in the sense it can be precursors for different macromodel that may be relevant for different policy decision by teh government. If we want to add up data from different portable sensors they have to be IOT and cloud compatble.
  • Water research is challenging for another indepndent reason. Water is a complex fluid and may have perspectives far beyond dealt with conventional sensors. Some interesting NIR and magnetic field based water parameter determination has been invented by our group (Dasgupta and Patra 2016) and (Dasgupta and Raja 2017).

References

Curtis, Theresa M, Mark W Widder, Linda M Brennan, Steven J Schwager, William H van der Schalie, Julien Fey, and Noe Salazar. 2009. “A Portable Cell-Based Impedance Sensor for Toxicity Testing of Drinking Water.” Lab on a Chip 9 (15). Royal Society of Chemistry: 2176–83.

Dasgupta, Anjan Kr, and Hirak Kumar Patra. 2016. NIR fluorescence of heavy water,US patent 9,354,170, issued may~31 2016.

Dasgupta, Anjan Kr, and Sufi Oasim Raja. 2017. Systems and methods for liquid quality assessment. us patent 9,759,668, issued sep~12 2017.

Morik, Katharina. 2015. “Big Data and Small Devices.” Banff International Research Station for Mathematical Innovation; Discovery.

O’Flyrm, B, R Martinez, John Cleary, C Slater, F Regan, D Diamond, and Heather Murphy. 2007. “SmartCoast: A Wireless Sensor Network for Water Quality Monitoring.” In Local Computer Networks, 2007. Lcn 2007. 32nd Ieee Conference on, 815–16. Ieee.

Perera, Charith, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos. 2012. “Ca4iot: Context Awareness for Internet of Things.” In Green Computing and Communications (Greencom), 2012 Ieee International Conference on, 775–82. IEEE.