Current years have witnessed the emergence of
wireless sensor networks (WSNs) as a new informationgathering
paradigm, in which a large number of sensors
scatter over a examination field and extract data of interests
by reading real-world phenomena from the physical
environment. Energy consumption becomes a primary
concern in a WSN, as it is crucial for the network to
functionally operate for an probable period of time. The
WSN’s extraordinary characteristics direct us to innovative
research challenges in some data mining process. Data
mining is one of the most important methods by which
useful patterns in data with minimal user interference are
known and available information of users and analysts to
make decisions relayed on their vital organizations to adopt.
Data mining, as the continuance of multiple intertwined
disciplines, consisting statistics, machine learning, pattern
recognition, database systems, information retrieval, World-
Wide Web, visualization, and lots of application domains,
has made great progress in the past decade. To ensure that
the advances of data mining research and technology will
competently benefit the progress of science and engineering,
it is important to scrutinize the challenges on data mining
posed in data-intensive science and engineering and explore
how to further develop the technology to assist new
discoveries and advances in science and engineering. In
WSNs, hierarchical network structures have the advantage
of supplying scalable and energy efficient solutions.
Published In:IJCSN Journal Volume 5, Issue 4
Date of Publication : August 2016
Pages : 692-698
Figures :03
Tables : 01
Mohd Muntjir : is a Research Scholar in OPJS University .He has
received his B.Sc in Mathematics from Choudhary Charan Singh
University Meerut U.P. in 2002 and M.C.A. degree in Computer
Science from Hemawati Nandan Bahuguna Garhwal University
Srinagar Garhwal Uttarakhand in 2007. His research interests are
Wireless Sensor Network, Data Mining, DBMS, Cloud Computing, ECommerce
and Multimedia Technology.
WSN, Partitioned WSN, Scheduling in WSN,
Location-Based Scheduling, Data Mining
Wireless sensor network technology has the prospective to
enable main breakthroughs in the natural sciences by
giving scientists the potential to collect high-fidelity data
over large geographic regions and extended periods of
time. In WSNs, since the sensor nodes are energy
constrained and have limited lifetime, energy
consumption of sensor nodes becomes as a major issue.
Two main approaches: 1) clustering-based: sensor nodes
form clusters and elect the cluster heads in such a way to
improve energy efficiency, and 2) prediction based:
energy-aware prediction is used to find the slight trade-off among communication and prediction cost. Via
performance evaluation, it is shown that it achieves
energy efficiency even though the object arrived from any
random location and moves randomly. K-Means Data
Relay (K-MDR) clustering algorithm for WSN reduces
the communication overhead and increases the entire
network life time by reducing the number of transmission
between every sensor node to sink. The K-Means Data
Relay algorithm decreases the computational time and
improves the performance of the network when compared
to K-Means algorithm. Hybrid moving based scheduling
strategy for data collection process is improved lifetime of
the network. For large coverage area and more number of
nodes failure in the network, multiple mobile robots used
for separate partition in order to collect data.
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