International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 3- April 2016
Data Mining Approach to Energy Efficiency
in Wireless Sensor Networks
Anisha kamboj#1 , Tanu Sharma*2
1
M.Tech., CSE Department, JMIT, Radaur, Kurukshetra University, India
2
Assistant Professor, CSE Department, JMIT, Radaur, Kurukshetra University, India
Abstract —Collection of sensor nodes systematized
into a network is known as WSNs. A sensor is a
small device which observes the environment of
physical parameters like pressure, temperature,
sound, pollutants or vibration. In sensor networks
data mining is the method of selecting applicationoriented standards and patterns with acceptable
accuracy from fast and continuous flow of data
streams from sensor networks (SN). In this case,
data must be processed quickly and cannot be stored.
Data mining methods has to be fast to process highspeed arriving data. Main objective of this review
paper is comparative analysis of various data
aggregation; mining techniques are discuses with
associates’ advantage of accuracy, complexity,
reduce energy consumption. Other factors such data
mining techniques that affect the prediction are also
discussed.
Keywords — Wireless Sensor Network; data
aggregation; Clustering; Data Mining
Introduction
Collection of sensor nodes which is organized into a
network is known as wireless sensor network. A
small device i.e sensor which observes the
environment of physical parameters like relative
humidity, pressure, temperature, motion, sound
pollutants or vibration, at different locations. WSN
is highly distributed networks of wireless sensor
nodes, used in large numbers to monitor the system
or environment. Sensor nodes organize themselves
in an ad hoc manner and the transmission range of
sensor nodes is limited, which means that two sensor
nodes cannot reach each other directly, transmits on
other sensor nodes to carry data between them. In
general, data packets from the source node have to
traverse multiple hops before they reach the
destination. A wireless sensor network is an Adhoc
network which consist sensor nodes, cluster heads
and sink nodes. Sensor networks are self organized
networks. Large numbers of tiny devices in the
wireless sensor network are known as sensor nodes
which are distributed to check physical and
environmental conditions. Sensor nodes have to
coordinate with each other, to get information about
the environment [10].
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Fig 1 Wireless Sensor Network
A. Routing mechanism and Data
Collection for WSNs
The conventional routing mechanisms and data
collection for WSNs can be largely divided into two
categories:
1) Periodic data collection: All sensor nodes are
periodically change their awareness to the sink based
on the current information of the interested data. In
multi hop-relay data delivery, typical traditional
wireless sensor network relay sensor observations to
a sink through a tree-based structure. However,
multi hop-relay approaches inevitably involve large
amount of data exchange between nodes, in addition
many overheads to maintain the network
architecture.
2) Event-based data collection: Sensors are
responsible for reporting and detecting a specific
event to one or more sinks in event-based data
collection [10].
B. Applications of WSN
Different types of sensors which are consist by the
sensor network such as thermal, magnetic, visual,
radar and infrared, which are able to monitor a wide
variety of conditions. These sensor nodes (SN) can
be put for location sensing, continuous sensing,
motion sensing event detection and continuous
sensing. There are the following applications are as
follows:a. Environmental applications -few Environmental
applications of sensor networks include forest fire
detection, flood detection and air pollution detection,
landslide detection. They can also be used for
tracking the movement of insects, planetary
exploration, small animals and birds, monitoring
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 3- April 2016
conditions that affect facilitating irrigation and
livestock and crops.
b. Area monitoring applications-It is a very
common application of WSNs. The WSN is
deployed over a region in this application where
some phenomenon or physical activity is to be
monitored. When the sensors detect the event being
monitored (vibration, sound), the event is reported to
the BS (base station), which then takes some action
(e.g., send a message on the internet or to a satellite).
Equivalently, WSN can be deployed in security
systems to detect motion of the unwanted, highspeed vehicles is to be catches by the traffic control
system. Also wireless sensor network finds
application in military area for battled surveillance,
ammunition and equipment, monitoring friendly
forces, terrain reconnaissance of opposing forces,
battle damage assessment targeting.
c. Industrial applications-wireless sensor network
are widely used in industries, like in machinery
condition-based maintenance. Previously impassable
locations, rotating machinery, mobile assets and
restricted areas or hazardous can now are reached
with wireless sensors. They can also be used to
monitor and measure the water levels within all
ground wells; control leach ate accumulation and
removal.
d. Health applications - For sensor networks the
health applications are providing interfaces for the
disabled, diagnostics, integrated patient monitoring,
patients inside a hospital, monitoring the movements
and internal processes of small animals or insects.
C. Data Mining in WSN
In sensor network Data mining is the method of
selecting patterns with acceptable accuracy from
continuous and application-oriented standard,
probably non ended flow of data streams from
sensor networks. In this case, all data processed
quickly and can’t be stored. Data mining is a
sufficient method to process high-speed arriving data.
Data mining methods are meant to handle the multi
scan mining algorithms for reviewing constant datasets and stationary data. Therefore, new data mining
methods are not suitable for handling the high
dimensionality, large quantity, and the wireless
sensor network is used to generate distributed data.
The aim of the data mining process is to extract
information from huge volume of a data and convert
it into an understandable structure for further use
[10]. The fast development of information
technology and computer in the last few years has
fundamentally changed almost every field in science
and engineering, calling for the development of new
data methods to conduct research in science and
engineering and converting many disciplines from
data-poor to increasingly data-rich. Make ensure that
the advancements of data mining research and
technology will effectively asset the progress of
science and engineering, the challenges on data
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mining in engineering is important to examine and
explore how to develop the technology to advances
in science and engineering and facilitate new
discoveries [5].
D. Data aggregation in WSNs
WSN typically consists of a sink node referred to as
a number of small wireless sensor network and a
base station. Assumed the sensors nodes are to be
unsecured with limited available energy while
assumed to the base station are being secure with
unlimited available energy. The sensor nodes control
a geographical area and collect sensory information.
Through wireless hop by hop transmissions the
Sensory information is communicated to the base
station. To conserve energy, by applying aggregation
function on the received data this information is
accumulated at intermediate sensor nodes.
Aggregation reduces energy consumption on sensor
nodes by reduce the amount of network traffic, It
however complicates already existing security
challenges for WSN and requires new security
techniques. Providing security to aggregate data in
WSN is known as secure data aggregation in WSN.
II. Related Study
Onur tekdas & volkan isler et.al [1], Explore
synergies among wireless sensor networks and
mobile robots in the environmental monitoring
through a system in which measurement gathered
by sensing nodes which is collect by data mules.
Daniele Apiletti & Elena Baralis et.al [2],
Represents the complete design, validation, and
implementation of the SeReNe framework. Given
historical sensor readings, SeReNe acquire efficient
sensor network data by discovering energy-saving
models SeReNe exploits different clustering
algorithms to discover temporal correlations and
spatial which allow the identification of sets of
sensor data streams and correlated sensors. Select
the representative sensors from the given clusters of
correlated sensors, to reduce the communication
computation and power Costs, only the
representative sensors are queried rather than
directly querying all network nodes
S.Anandamurugan & C.Venkatesh et.al [3],
Presents several advantages of heterogeneous
architecture for WSNs. It consists of some resource
rich simple undynamic nodes and mobile relay
nodes. The mobile relays have high energy as
compare to undynamic nodes. The mobile relays
help relieve sensors that are highly burdened by
heavy network traffic and can dynamically move
around the entire network, thus improving the
lifetime.
Tzung-Cheng Chen & Tzung-Shi Chen et.al [4],
Suggested from a wireless sensor network (WSN) a
novel data-collecting algorithm using a mobile robot
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 3- April 2016
to get sensed data that possesses islanded/
partitioned WSNs is proposed in this paper. This
algorithm allows the improvement of data collecting
performance by the base station by identifying the
locations of navigating a mobile robot and
partitioned/islanded WSNs to the desired location.
Two control approaches, a global- and local-based
approach are proposed to identify the locations of
the partitioned/islanded WSNs.
Laxmi Choudhary et.al [5], Define about large
amount of data in science and engineering has been
and continuously generated with the fast
development of computer and information
technology in the last few years. Moreover, such
data has been widely made available via the Internet.
Such tremendous amount of data has fundamentally
changed science and engineering, converting many
disciplines from data-poor to data-rich, dataintensive methods to conduct research in science and
engineering.
Miao Zhao & Yuanyuan Yang et.al [6], represents
a great benefit can be achieved for data gathering in
WSNs by employing mobile collectors that gather
data by short-range communications. A mobile
collector should traverse the transmission range of
each sensor in the field to pursue maximum energy
saving at sensor nodes.
Emad M. Abdelmoghith & Hussein T. Mouftah
et.al [7], presents a amount of research work to
minimize the volume of transmitted traffic by using
data compression techniques and reducing power
consumption levels in WSNs. In this paper, the
present a data oriented approach called Model-based
Clustering (MBC) which reduces the flows of
communication between sink node and sensor nodes.
S.Nithyakalyani & Dr.S.Suresh Kumar et.al [8],
given a brief comparative study is made from
different research proposals; it suggests some of the
cluster head selection approach for the data
aggregation. The algorithms under study are
Voronoi based Genetic clustering algorithm, Fuzzy
C- means clustering algorithms and Data relay Kmeans clustering algorithm.
Phiros Mansur & Sasikumaran Sreedharan et.al
[9], represents some techniques which associates
advantages of energy conservation and reduction of
missing rate, accuracy of tracking. Moreover other
factors like data mining techniques and clustering
that affect the prediction.
Vickey Sharma et.al [10], define the goal of the
data mining process is to get information from a
data set and converts it into an understandable form
for further use. The main objective of this review
paper to analyse and study of various data mining
algorithms and data collection.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 3- April 2016
TABLE I
COMPARISON OF VARIOUS KEY MANAGEMENT TECHNIQUES
S.No
Key management
Technique
Proposed by
Findings
Advantage
Disadvantage
1
Sensing device
motes
ONUR
TEKDAS AND
VOLKAN
ISLER
Robots as data
mules.
This approach is feasible and
yields important savings in
energy costs, thus prolonging
the lifetime of the network
The motes can spend the
majority of their energy stores
broadcasting collected data,
especially because they might
be required to forward the
data for other motes in the
Network.
2
SERENE
Framework(Sele
cting
Representative in
a Sensor
Network)
Daniele
Apiletti · Tania
Cerquitelli
energy-saving
models to
efficiently access
sensor network data
SERENE prototype has
designed to be stable, fast,
able to manage a huge
amount of sensor data stream
and easy to access.
The issue in this context is
energy saving during data
collection.
3
Mobile Relay
Approach
S.Anandamuru
gan,
C.Venkatesh
AR (Aggregation
Routing) Algorithm
Efficient Improve lifetime of
the network. It has high
energy then undynamic
nodes.
The problem of maximizing
lifetime as a linear
programming problem and
arrive the optimal schedule
for the mobile node.
4
Mobile Robots
Tzung-Cheng
Chen, TzungShi Chen
GBA(global-Based
approach
LBA(local Based
approach)
Improve sensed data
collecting performance in
apportion or islanded WSNs.
When energy depletion
of a sensor node results in a
dead mode that is unable to
Communicate with other
nodes.
5
Data Mining
Laxmi
Choudhary
Machine learning
pattern recognition
database
Extract the knowledge from
huge volume of data.
One such issue is the
development of invisible data
mining (DM) functionality for
science and engineering.
6
Mobile
collector
Miao Zhao and
Yuanyuan
Yang
Polling – based
mobile gathering
approach
Improve efficiency
The number of transmission
hops should not be arbitrarily
large as it may increase the
energy consumption on
packet relay.
7
Data
compression
technique
Emad M.
Abdelmoghith,
Hussein T.
Mouftah
Model Based
Clustering(data
oriented approach)
Reducing energy power
consumption level in WSN.
Added complication
Effect of errors in
transmission
Slower of sophisticated
method.
8
Data aggregation
technique
S.Nithyakalyani
Dr.S.Suresh
Kumar
Node clustering
algorithm
Aggregation reduces energy
consumption on sensor nodes
by reduce the amount of
network traffic.
If a cluster head is
compromised, then the base
station cannot be ensure about
the correctness of the
aggregate data that has been
send to it.
9
Prediction
algorithm for
object tracking
Phiros Mansur,
Sasikumaran
Sreedharan
clustering and data
mining
Make the prediction process
more accurate that will help
to reduce the missing rate.
The major drawback of this
method is that they assume
the target always follows the
same movement pattern.
10
data collection
and routing
mechanisms
Vickey Sharma
Data Mining
Data mining process is to
extract information from a
data set and alter t it into an
understandable form for
further use.
These schedules are either
inapplicable to or inefficient
in sharing with dynamic
traffic patterns.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 3- April 2016
III. CONCLUSIONS
This paper describes the various techniques which
uses the concept of clustering and data mining.
Sensing device motes using the approach is feasible
and yields important savings in energy costs.
SERENE Framework (Selecting Representative in a
Sensor Network) discovers energy-saving models to
efficiently acquire sensor network data while
minimizing energy consumption for data collection.
Data mining technique is used to extract the
knowledge from huge volume of the data. Data
compression technique introduced an unsupervised
learning scheme called Model-based Clustering
(MBC) to shrink the amount of transmitted packets
between sensor nodes and the sink node. Data
aggregation technique describes the comparison
between different clustering algorithms for data
aggregation using Voronoi diagram are classified
and discussed. Algorithms used in this paper for
comparative studies are Data relay K-means
clustering algorithm, Fuzzy C-means clustering
algorithms and Voronoi based Genetic clustering
algorithm. Finally it is concluded from the study
that, all the algorithms are good enough for
energy efficiency and stable clustering method,
for data aggregation in wireless sensor networks.
REFERENCES
[1] Onur tekdas and volkan isler, jong hyun lim and andreas terzis,
“using mobile robots to harvest data from sensor fields”, IEEE
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[2] Daniele Apiletti · Elena Baralis · Tania Cerquitelli, “Energysaving models for wireless sensor networks”, London Limited :
Springer-Verlag, april 2010.
[3] S.Anandamurugan, C.Venkatesh, “Increasing the Lifetime of
Wireless Sensor Networks by using AR (Aggregation Routing)
Algorithm”, IJCA, 2010.
[4] Tzung-Cheng Chen, Tzung-Shi Chen, “On Data Collection
Using Mobile Robot in Wireless Sensor Networks”, IEEE
Transactions On Systems, November 2011.
[5] Laxmi Choudhary, “Challenges for data mining”, IJREAS,
February 2012.
[6] Miao Zhao and Yuanyuan Yang, “Bounded Relay Hop Mobile
Data Gathering in Wireless Sensor Networks”, IEEE Transactions
on Computers, February 2012.
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in WSN”, IJARCSSE , April 2015.
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