Draft: draft-hong-t2trg-iot-edge-computing

Commentary: This update is for issue : re - ordered use cases and state - of - the - art sections . Also did some minor editorial updates ( on section titles and on use cases text ) . 
 Use cases to introduction State of the art before deriving patterns ... Keep appendix with detailed projects definitely while working on the document ( might also stay in published document ) 
 Closed after publishing -02
In traditional construction domain, heavy equipment and machineries
pause risks to humans and property.  Thus, there have been many
attempts to deploy technology to protect lives and property in
construction sites.  For example, measurements of noise, vibration,
and gas in a construction site can be recorded on a remote server and
reported to an inspector.  Today, data produced by such measurements
is collected by a gateway in a construction site and transferred to a
remote server.  This incurs transmission costs, e.g. over a LTE
connection, and storage costs, e.g. when using Amazon Web Services.
When an inspector needs to investigate some accidents, he checks the
information stored in a server.
If we leverage IoT edge computing, sensor data can be processed and
analyzed in a gateway located within or near a construction site.
And with the help of a statistical analysis or machine learning
technologies, we can predict future accidents in advance and this
prediction can trigger an alarm in a construction site and a
notification to an inspector.
To determine the exact cause of an accident, not only sensor data but
also audio and video data are transferred to a remote server or cloud
networks.  In this case, the data volume of audio and video is quite
big and the cost of transmission can be an issue.  If edge computing
can be leveraged to predict the time of an accident, this can reduce
the data volume of transmission; while during a normal time period
audio and video data may be transmitted with a low resolution, during
an emergency, this transmission may use a high resolution.  This
adjustment can reduce transmission cost significantly.
In future smart city scenarios, the smart grid will be critical in
ensuring highly available/efficient energy saving and control in
city-wide electricity management.  Edge computing is expected to play
a significant role in those systems to improve transmission
efficiency of electricity, to react and restore power after a
disturbance, reduce operation cost and reuse renewable energy
effectively.  In addition, edge computing can help monitoring power
generation and power demand, and making electrical energy storage
decisions in the Smart grid system.
The water system is one of the most important aspects of a city.
Effective use of water, and cost-effective and environment-friendly
treatment of water are critical for water control and management.
This can be facilitated by edge computing in smart water systems, to
help monitor water consumption, transportation and prediction of
future water use.  For example, water harvesting and ground water
monitoring will be supported through edge computing.  Also, a smart
water system is able to analyze collected information related to
water control and management, control the reduction of water losses
and improve the city water system through edge computing.
In traditional construction domain, there are many heavy equipment
and machineries and dangerous elements.  Even though human pay
attention to risk elements, it is not easy to avoid them.  If some
accidents are happened in a construction site, it causes a loss of
lives and property.  Thus, there have been many trials in a
construction area to protect lives and property.
Measurements of noise, vibration, and gas in a construction area are
recorded on a remote server and reported to an inspector.  Today,
data produced bu such measurements is collected by a gateway in a
construction area and transferred to a remote server.  This incurs
transmission cost, e.g. over a LTE connection, and storage cost, e.g.
when using Amazon Web Services.  When an inspector wants to
investigate some accidents, he checks the information stored in a
server.
If we deploy Edge computing in a construction area, the sensor data
can be processed and analyzed in a gateway located within or near a
construction area.  And with the help of a statistical analysis or
machine learning technologies, we can predict future accidents in
advance and this prediction can be used as an alarm in a construction
area and a notification to an inspector.
To determine the exact cause of some accident, not only sensor data
but also audio and video data are transferred to a remote server or
cloud networks.  In this case, the data volume of audio and video is
quite big and the cost of transmission can be a problem.  If Edge
computing can predict the time of accident, it can reduce the data
volume of transmission; in general period, it can transmit the audio
and video data with a low resolution/degree and in emergent period,
it transmits the audio and video data with a high resolution/degree.
By adjusting the resolution/degree of audio and video data, it can
reduce transmission cost significantly.
In future smart cities, Smart grids will be critical in ensuring
availability and efficiency for energy saving and control in city-
wide electricity management.  Edge computing is expected to play a
significant role in those systems to improve transmission efficiency
of electricity, react and restore for power disturbances, reduce
operation cost, reuse renewable energy effectively, save energy of
electricity for future usage, and so on.  In addition, Edge computing
can help monitoring power generation and power demands, and making
electrical energy storage decisions in the Smart grid system.
The Water system is one of the most important aspects for building
smart city.  Effective use of water, and cost-effective and
environment-friendly treatment of water are critical for water
control and management.  This can be facilitated by Edge computing in
Smart water systems, to help monitor water consumption,
transportation, prediction of future water use, and so on.  For
example, water harvesting and ground water monitoring will be
supported from Edge computing.  Also, a Smart water system is able to
analyze collected information related to water control and
management, control the reduction of water losses and improve the
city water system through Edge computing.