| Title |
Design of IoT Systems and Analytics in the context of Smart City initiatives in India |
| ID_Doc |
37277 |
| Authors |
Vijai, P; Sivakumar, PB |
| Title |
Design of IoT Systems and Analytics in the context of Smart City initiatives in India |
| Year |
2016 |
| Published |
|
| DOI |
10.1016/j.procs.2016.07.386 |
| Abstract |
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. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. |
| Author Keywords |
Smart City; Internet of Things; Machine Learning; Smart Water Management; Forecasting; Anomaly Detection |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000383062700082 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://doi.org/10.1016/j.procs.2016.07.386
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