258
I. S. Donbosco and U. K. Chakraborty
7 Conclusion and Future Works
The water distribution can be optimized, and the water can be conserved better by
the application of the above method in a real-life scenario. The proposed methodology can be improved with the application of better sensors and industry grade
materials which would make it efficient. Motorized valves can be replaced with a
better alternative and the Arduino with the ESP-8266 upgraded with a NodeMCU
or even better ones. This can make the working faster and better. Improvements in
the analytics can be done by using advanced ML algorithms with neural networks
or similar techniques for a better outcome.
In the current paper, as already mentioned, simplifying assumptions have been
made about the layout and plan of the city where it can be implemented. This has
been done purely for ease of identification of the individual households, as analysis
of the data gathered plays a pivotal role in the proposed model. It becomes essential
therefore to pinpoint every holding and regulate the supply accordingly. The scalability of the proposed model in its current form is limited only by the organization of
the cities household. The same may however be implemented on any scale, small or
big, provided a proper distinction can be made between individual houses to enable
the analysis to be based on that identity.
Another novelty of the proposed model is its flexibility. The same model can be
used for farmland water management, wherein the identification of every household
would be replaced by individual farm plots. The analytics can be appropriately tuned
without major changes.
References
1. Mohanasundaram SV, Joyce A, Naresh SK, Gokulakrishnan G, Kale A, Dwarakanath T,
Haribabu P (2018) Smart water distribution network solution for smart cities: Indian scenario.
2018 Global Internet of Things Summit (GIoT). https://doi.org/10.1109/GIOTS.2018.8534524
2. Agarwal N, Agarwal G Role of cloud computing in development of smart city. Int J Sci Technol
Eng (IJSTE), (online) ISSN: 2349-784X
3. Merchanta A, Mohan Kumar MS, Ravindra PN, Vyas P, Manohar U (2013) Analytics driven
water management system for Bangalore city. In: 12th International conference on computing
and control for the water industry, CCWI2013, Procedia Engineering, vol 70, pp 1137–1146
4. Karwot J, Ka´ zmierczak J, Wyczolkowski R, Paszkowski W, Przystałka P (2016) Smart water
in smart city: a case study, conference: SGEM 16th international scientific conference on earth
and Geosciencesat: Albena, Bulgaria vol 3/I. https://doi.org/10.5593/sgem2016B31.
5. Kaur MJ, Maheshwari P (2016) Building smart cities applications using IoT and cloud-based
architectures. In: Proceedings of 2016 international conference on industrial informatics and
computer systems (CIICS). https://doi.org/10.1109/GIOTS.2018.8534524.
6. Su K, Li J, Fu H (2011) Smart city and the applications. In: Proceedings of the 2011 international conference on electronics, communications and control (ICECC). https://doi.org/10.
1109/ICECC.2011.6066743
7. Koo D, Piratla K, John Matthews C (2015) Towards sustainable water supply: Schematic
development of big data collection using internet of things (IoT). Procedia Eng 118:489–497
I. S. Donbosco and U. K. Chakraborty
7 Conclusion and Future Works
The water distribution can be optimized, and the water can be conserved better by
the application of the above method in a real-life scenario. The proposed methodology can be improved with the application of better sensors and industry grade
materials which would make it efficient. Motorized valves can be replaced with a
better alternative and the Arduino with the ESP-8266 upgraded with a NodeMCU
or even better ones. This can make the working faster and better. Improvements in
the analytics can be done by using advanced ML algorithms with neural networks
or similar techniques for a better outcome.
In the current paper, as already mentioned, simplifying assumptions have been
made about the layout and plan of the city where it can be implemented. This has
been done purely for ease of identification of the individual households, as analysis
of the data gathered plays a pivotal role in the proposed model. It becomes essential
therefore to pinpoint every holding and regulate the supply accordingly. The scalability of the proposed model in its current form is limited only by the organization of
the cities household. The same may however be implemented on any scale, small or
big, provided a proper distinction can be made between individual houses to enable
the analysis to be based on that identity.
Another novelty of the proposed model is its flexibility. The same model can be
used for farmland water management, wherein the identification of every household
would be replaced by individual farm plots. The analytics can be appropriately tuned
without major changes.
References
1. Mohanasundaram SV, Joyce A, Naresh SK, Gokulakrishnan G, Kale A, Dwarakanath T,
Haribabu P (2018) Smart water distribution network solution for smart cities: Indian scenario.
2018 Global Internet of Things Summit (GIoT). https://doi.org/10.1109/GIOTS.2018.8534524
2. Agarwal N, Agarwal G Role of cloud computing in development of smart city. Int J Sci Technol
Eng (IJSTE), (online) ISSN: 2349-784X
3. Merchanta A, Mohan Kumar MS, Ravindra PN, Vyas P, Manohar U (2013) Analytics driven
water management system for Bangalore city. In: 12th International conference on computing
and control for the water industry, CCWI2013, Procedia Engineering, vol 70, pp 1137–1146
4. Karwot J, Ka´ zmierczak J, Wyczolkowski R, Paszkowski W, Przystałka P (2016) Smart water
in smart city: a case study, conference: SGEM 16th international scientific conference on earth
and Geosciencesat: Albena, Bulgaria vol 3/I. https://doi.org/10.5593/sgem2016B31.
5. Kaur MJ, Maheshwari P (2016) Building smart cities applications using IoT and cloud-based
architectures. In: Proceedings of 2016 international conference on industrial informatics and
computer systems (CIICS). https://doi.org/10.1109/GIOTS.2018.8534524.
6. Su K, Li J, Fu H (2011) Smart city and the applications. In: Proceedings of the 2011 international conference on electronics, communications and control (ICECC). https://doi.org/10.
1109/ICECC.2011.6066743
7. Koo D, Piratla K, John Matthews C (2015) Towards sustainable water supply: Schematic
development of big data collection using internet of things (IoT). Procedia Eng 118:489–497