Publication Type:

Conference Paper


Procedia Computer Science, Elsevier, Volume 92, p.583-588 (2016)



Anomaly detection, Artificial intelligence, Developing countries, Forecasting, Intelligent computing, Internet, Internet of things, Learning systems, Machine learning techniques, Monitoring and management, Population growth, Population statistics, Signal detection, Smart cities, Smart solutions, Water demand forecasting, Water distribution systems, water management, Water quality, Water quality monitoring, Water supply systems


The rapid growth of population and industrialization has paved way for the use of technologies like the Internet of Things which gave rise to the concept of smart cities. India as a developing country has a great prospect in developing technologies to make the cities smart. As urbanization occurs the demand for resources and efficient servicing will increase. To achieve this in a smart and efficient way, connected device (IoT) could be used. The possible design of an IoT system based on surveys performed on similar smart solutions implemented has been discussed in this paper. Urbanization and population growth has led to higher demand for resources like water which are of scarce. There is a keen interest from the organizations and government to make proper usage of water. The same can be achieved by proper monitoring and management of water distribution systems. The paper discusses the use of Machine learning techniques to smart city management aspects like smart water management which include water demand forecasting, water quality monitoring and anomaly detection. © 2016 The Authors. Published by Elsevier B.V.


cited By 0; Conference of 2nd International Conference on Intelligent Computing, Communication and Convergence, ICCC 2016 ; Conference Date: 24 January 2016 Through 25 January 2016; Conference Code:123267

Cite this Research Publication

P. Vijai and Sivakumar, P. B., “Design of IoT Systems and Analytics in the Context of Smart City Initiatives in India”, in Procedia Computer Science, 2016, vol. 92, pp. 583-588.