Water is the major natural resource. In every day
busy life of people to remember to switch of the water-tap in
bathroom / washroom is very small task for them and everyone
tend to ignore it. But at the same time, it is obligatory to save
water even at every household and tap + bucket / sink /basin
level. The aim of this research study is that the existing tap
should interact with bucket via sensors and stop overflowing of
water. This way not only water, but heat / electricity and
individual human energies are banked and every individual will
be part of global social initiative. This proposed “anti water
flooding” idea can be extended further by acquiring water and
power time series data. This acquired data can be analyzed
further using machine learning concepts for decision making,
predication of water utilization per household.
Preeti Mulay : is working in areas related to data mining,
incremental clustering and machine learning. Her PhD is related to
topic of incremental clustering and incremental learning. She is
MS, M.Tech in Software Engineering, completed her MS from
Wayne State University, MI, USA. Her contribution to research
includes research papers in indexed journal and chapters in
technical book published by Elsevier.
Water-Level Sensors
Tap+Bucket
Electricity /
Power
Energy
Energy
Wireless Communication
Water is one of the most important basic needs for all
living beings. But unfortunately a huge amount of water is
being wasted by uncontrolled use. Some other automated
water level monitoring system is also offered so far but
most of the method has some shortness in practice. To
overcome these problems this proposed efficient
automated water level monitoring / supervisory system is
suggested. The sole intension of this research work was to
establish a flexible, economical and easy configurable
system which can solve our water losing problem. Existing
tap, buckets, wash-basins can be equipped with sensors to
have economical solutions.
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Congress on Engineering and Computer Science 2010
Vol I WCECS 2010, October 20-22, 2010, San
Francisco, USA.
