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I. S. Donbosco and U. K. Chakraborty
etc. which can later be used in analytical models to draw inferences to significantly
change the quality of life for the citizens. “Smart city” or a “digital city” is the use of
modernized techniques in communication, sensing, analysis and integration to run
everyday living conditions [6].
Smart implementation of technology can give intelligent and prompt responses to
different needs including but not limited to commute and traffic management, public
safety, resource distribution and management and commercial trade and activities.
In layman terms, a smart city is a way of living with and aided by technology and
data. Unlike conventional cities, smart cities integrate technology with governance
and that is what makes smart cities different. Automation is made from the smallest
entities such as a simple traffic light to more complex infrastructure such as water
supply, energy transfer, governance and emergency situation handling. A smart city is
therefore portrayed as being better equipped to face growth-related changes through
a simple transnational relationship of governance with citizenry and resource usage
patterns.
3.3 Data Analytics (DA)
Increasing environmental awareness among people and the desire to work fast and
effectively through the reduction of time spent on unproductive activities is forcing
the realization on governments toward building smart cities. While one important
aspect of this lies in connectivity through sensors, the other is data analytics.
Data analytics (DA) is the basic science of analyzing trends and features in data.
As the name suggests, it deals with analyzing patterns in data to derive information on which decisions can be based. Data analytics is a sub-section of machine
learning, as it uses several machine learning techniques to analyze the data including
regression, classification techniques and also bagging and boosting techniques such
as XGBoost and AdaBoost [6]. Use of analytical techniques in IoT and smart citybased applications can improve their performance [7]. Analysis of traffic patterns
can help in deciding traffic flow regulation. Weather data analysis can help in street
light control, which may additionally also depend on the traffic density saving power.
Analysis of carbon emission data may help in devising means of reduction of emission. A host of such application can be found through intelligent sensor usage and
data analytics. Data analytics in IoT is almost always clubbed with cloud techniques
to improve data retrieval management so that the data is safely stored and accessible
for use. The current paper proposes an IoT-based water management technique,
which augmented with data analytics can be used in smart cities for effective water
management and sustainable town planning.
I. S. Donbosco and U. K. Chakraborty
etc. which can later be used in analytical models to draw inferences to significantly
change the quality of life for the citizens. “Smart city” or a “digital city” is the use of
modernized techniques in communication, sensing, analysis and integration to run
everyday living conditions [6].
Smart implementation of technology can give intelligent and prompt responses to
different needs including but not limited to commute and traffic management, public
safety, resource distribution and management and commercial trade and activities.
In layman terms, a smart city is a way of living with and aided by technology and
data. Unlike conventional cities, smart cities integrate technology with governance
and that is what makes smart cities different. Automation is made from the smallest
entities such as a simple traffic light to more complex infrastructure such as water
supply, energy transfer, governance and emergency situation handling. A smart city is
therefore portrayed as being better equipped to face growth-related changes through
a simple transnational relationship of governance with citizenry and resource usage
patterns.
3.3 Data Analytics (DA)
Increasing environmental awareness among people and the desire to work fast and
effectively through the reduction of time spent on unproductive activities is forcing
the realization on governments toward building smart cities. While one important
aspect of this lies in connectivity through sensors, the other is data analytics.
Data analytics (DA) is the basic science of analyzing trends and features in data.
As the name suggests, it deals with analyzing patterns in data to derive information on which decisions can be based. Data analytics is a sub-section of machine
learning, as it uses several machine learning techniques to analyze the data including
regression, classification techniques and also bagging and boosting techniques such
as XGBoost and AdaBoost [6]. Use of analytical techniques in IoT and smart citybased applications can improve their performance [7]. Analysis of traffic patterns
can help in deciding traffic flow regulation. Weather data analysis can help in street
light control, which may additionally also depend on the traffic density saving power.
Analysis of carbon emission data may help in devising means of reduction of emission. A host of such application can be found through intelligent sensor usage and
data analytics. Data analytics in IoT is almost always clubbed with cloud techniques
to improve data retrieval management so that the data is safely stored and accessible
for use. The current paper proposes an IoT-based water management technique,
which augmented with data analytics can be used in smart cities for effective water
management and sustainable town planning.