Download An Enhanced Data Mining Technique for Energy Efficiency

International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 3- June 2016
An Enhanced Data Mining Technique for
Energy Efficiency in WSN
Research paper
Anisha kamboj#1, TanuSharma*2
1
M.Tech.,CSEDepartment, JMIT, Radaur, Kurukshetra University, India
Assistant Professor,CSE Department, JMIT, Radaur, Kurukshetra University, India
Abstract —In this paper we have define how to
in the large numbers, they provide with a very real
improve the energy efficiency of the network by
picture of the field being sensed. A sensor network is
using data mining technique by discovery, usage,
composed of a large amount of small sensor nodes,
and understanding of patterns and knowledge,
which are organized closely either inside network or
mining moving object data, mining text, Web, and
very close to it. The sensor nodes, in wireless sensor
other unstructured data. There has recently been a
network monitor physical situations such as
considerable amount of research work done on
temperature, sound, pressure, etc. and to pass data
using data compression techniques to minimize the
through the network to main position.
volume of the transmitted traffic, and the
consequently assist in reducing power consumption
levels in Wireless Sensor Networks. A Wireless
Sensor Network is the network of small sensor nodes
which are energy constraint devices and have
limited data computational and transmission power.
The nodes are arranged in the form of cluster
around a Base Station (Sink). NS2 consist two key
languages:
Object-oriented
Tool
Command
Language and C++.
2
Keywords — wireless sensor network, data mining,
clustering, energy efficiency.
I. INTRODUCTION
Wireless Sensor Network includes small sensor
nodes; these sensor nodes are the highly energy
constraint devices, have low computational power
and limited data transmission. These sensor nodes
takes information from the environment, processes
the data and then transmits the data via radio signals.
The advantages of wireless sensor network are
improved coverage, energy efficiency, enhanced
target tracking and superior channel capacity.
Generally, wireless sensor network have no fixed
structure, and sometimes there is no monitoring
station of these sensor nodes during lifetime of the
network. Therefore, it is necessary that a wireless
sensor network must have mechanisms for
adaptation and self-configuration in case of failure,
deletion or insertion of a sensor node. Sensor nodes
are the electronic devices and the main components
of sensor nodes are units of storage, sensing,
processing, and transmission. This sensor node
captures the information from the environment,
processes the data and transmits the data through
radio signal. Usually, these nodes have limited
battery life due to some constraints such as limited
battery life (due to cost, size etc.), very low data
rates, low memory, low bandwidth processing,
variable link quality and little computing capability.
Despite these constraints, the sensors were deployed
ISSN: 2231-5381
Fig: 1 Data communication in clustered network
WSN is a collection of small, light-weight sensor
nodes deployed in large numbers to control the
ambient conditions. WSN have many advantages,
Hence energy consumption is a major criterion.
Collection of sensor nodes is called cluster. Each
cluster consists of one or more cluster heads. From
their cluster members the cluster head gathers all the
data. The collected information is routed to the Base
Station (BS). The Base Station is a fixed node,
which is capable to receive and transmit the data
within the entire network. The number of cluster
head selection depends on the number of sensor
nodes. Energy consumption is dynamically
controlled by selecting more than one cluster head.
Analysis of energy consumption depends on the
number of cluster heads needed, when the nodes are
increased. The main reason of energy spending in
WSN relate with communicating the sensor readings
from the sensor nodes to remote sinks. These
readings are typically relayed using an ad hoc multi
hop routes in the WSN. If the energy spent in
http://www.ijettjournal.org
Page 137
International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 3- June 2016
relaying data can be saved then network lifetime can
be extended. Recent research work has proved the
applicability of the mobile elements (mobile robots,
cars etc.) for the retrieval of sensory data from smart
dust motes in comparison with the multi hop
transfers to a centralized element. Mobile sink
awakening through the network deployment region
can collect the data from static sensor nodes over a
single hop radio link when accessing within radio
range of the sensor nodes or with limited hop
transfers if sensor nodes are located further. This
reduces energy consumption at SNs near the base
station, avoids long-hop relaying and prolonging the
network lifetime
Clustering in WSN
The clustering technique in wireless sensor network
deals with sensor constraints. The hierarchical
network structure authorize grouping of sensor
nodes into cluster and to assign a specific task to the
sensor in the cluster, before moving information to
the higher level. Wireless sensor network clustering
technique help to achieve high energy efficiency and
assure about the long network lifetime. In the
hierarchical manner each cluster has a cluster leader,
which supervised all the cluster node known as
cluster head and several common sensor nodes are
the member of cluster. The cluster formulation
process eventually leads to the two levels hierarchy
where the cluster head nodes form higher level and
the cluster- member nodes form lower level. The
sensor nodes transmit the data to the corresponding
cluster head nodes. The criteria for selection of the
cluster head are based on the minimum average
distance from base station and residual energy of the
nodes within the clusters. The CH nodes transmit the
data to the sink either directly or via intermediate
communication with other cluster head node. In
order to balance energy consumption among all
network nodes, there is rotation of cluster head in
WSN [10].
Cluster Support Sensor Networks
The energy limitations in the sensor network makes
hard to managing the sensor energy and
safeguarding of the power is an important factor for
attaining prolonged network lifetime. Clustering
sensors into the group is more adaptive and efficient
approach is adopted for routing and for node
communication by many studies on sensors energy
consideration.
Cluster-based
mechanism
is
employed for routing and for node communication.
In the clustered wireless sensor network the sensors
communicate data only to cluster-head (CH) then the
cluster heads transfers the aggregated data to the
base station or to the processing canter which is
called as sink shown in Fig. 2
The base station (BS) is a specialized device or one
of the sensors. Bandyopadhyay and Coyle selecting
a set of cluster head by all of the nodes in the
network is an essential operation. With the help of
ISSN: 2231-5381
these heads it also clusters the other nodes. The
cluster heads are selected according to some of the
negotiated rules. Normally the node which is more
powerful in the topology play the role of cluster and
the other nodes will forward the sensing data to the
cluster nodes.
Fig 2: Cluster -based Sensor network
In the energy consumption sensors, communication
is the main factor to be considered. The amounts of
energy used are totally dependent on the distance of
the data that has to travel between sender and the
receiver. Since the sensors only communicate to the
cluster heads over smaller distances in clustered
sensor network, total number of sensors in the
network is much smaller than the situation in which
each and every sensor communicates directly to the
sink [6].
Data Mining
With significant increase in the volume of data
stored on the need for the better methods, cheaper
and faster to analyse them was felt. And if this
purpose can’t be effective and efficient mechanism
to extract the knowledge from huge volumes of data
design, then all data available in the world will be
meaningless. A number of scientists discovered the
necessity of such need and thus was born the science
of data mining.
Wireless Sensor Networks with large number of
sensor nodes are used to monitor the sensing field.
Sensor network data-transfer reliability and Sensor
node energy efficiency are the primary design
parameters. Some of the applications may require
heterogeneous sensor nodes with different sensing
phenomena or the different hardware characteristics.
The heterogeneity imposes an added constraint to
mining of useful information from network. At the
same time, a stream of the data that is frequently
reported from each node to the base station may be
needed.
http://www.ijettjournal.org
Page 138
International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 3- June 2016
Use of Data Mining for Energy Efficiency
A Wireless Sensor Network consists of a large
number of the sensor nodes that co-operatively
monitors specific region of interest. Typically, a
sensor node is the small hardware device consisting
of a sensing unit, a power unit, processing unit, and
communication unit that are used for sensing,
communication purposes and data processing. These
nodes collectively gather sensed information and
forward it to the special node called sink or base
station. This acts as interface between the sensor
nodes and users. The uniqueness of the sensor node
lies in its small size and the light weight. However,
there are lots of constraints such as limits on
resources in terms of energy, bandwidth
computational speed, memory, and so on. Because
of these constraints the interactions between the
sensors are limited to the short distances and low
data rates. These sensors are used in wide range of
applications and real time applications such as
security purpose, military applications, habitat
monitoring, nuclear power plants, etc. Sensors
gather useful information in a timely manner and
send to a centralized node named base station or
sink. Sink node which is also known as base station
and is responsible for the further processing such as
node query. Due to large number of the sensor nodes
and the voluminous data that should be reported, the
data communication should be done in the energy
efficient manner. Centralized solutions for the data
collection are not recommended. Obvious the
drawbacks of this type of solutions include the
hindering the network since the sink node becomes
bottleneck, the bandwidth allocated is not efficiently
used and all the sensor nodes consume a lot of
valuable power and scarce to communicate with the
sink node. Hence, the optimum solution becomes a
distributed data collection algorithm, where the data
mining techniques such as clustering are applied to
sensor nodes. When there are large number of the
sensors in the sensing field, sensors will be clustered
to reduce data redundancy. The cluster head CH will
take care of this work. Clustering of the sensor nodes
is to be considered as one of the very successful
techniques to mining useful information from the
distributed environment. It is particularly useful
technique especially for the applications that require
scalability to hundreds and the thousands of nodes.
Clustering also supports the aggregation of data in
order to summarize overall transmitted data.
However, the current literatures either focus on the
node or data clustering alone. Clustering of the
sensor nodes deals with two main operations: 1)
assigning nodes to respective cluster heads, and 2)
identifying cluster heads. These two operations
should be done at very energy-efficient level. On the
other hand, data clustering deals with the collecting
the similar data for aggregation purposes.
ISSN: 2231-5381
Mining Data from Sensor Networks
With the popularity of the sensor networks, cellular
phones, other mobile devices, GPS, tremendous
amount of moving data object has been collected,
calling for the effective analysis. This is especially
true in much scientific, business, engineering
applications. Sensor networks are finding number of
applications in many domains, including smart
buildings, battle fields, and the human body.
Moreover, sensors must process a continuous stream
of data. Data mining in wireless sensor networks is a
challenging area, as the algorithms need to work in
constrained and extremely demanding environment
of sensor networks (like limited energy, storage,
bandwidth, computational power). Wireless sensor
network also require highly decentralized
algorithms. In the designing algorithms for sensor
networks, it is important to keep in mind that the
power consumption has to be minimized. Gathering
the distributed sensor data in single site could be
expensive in terms of battery power consumed, some
attempts have been made towards making data
collection task energy efficient and balance the
energy-quality trade-offs. Clustering the nodes of
sensor networks is an essential optimization
problem. Nodes that are clustered together can be
easily communicate with each other, which can be
applied to developing optimal algorithms and energy
optimization for clustering sensor nodes.
Energy Saving Models for WSN
The SeReNe framework is stands for Selecting
Representatives in a sensor Network (SeReNe) are
an environment for the identification of energysaving models to efficiently query sensor networks.
Figure shows the SeReNe framework integrated into
sensor network architecture. From the sink, Sensor
nodes frequently receive queries (i.e., base station).
The query is implementing by each node over its
sensor data and the result sends to the base station by
means of a multi-hop data collection protocol.
Before transmitting the query results, the sensor data
can be sometimes compressed by compression
/reduction techniques. These in-network processing
techniques are complementary approach that address
the sensor network efficiency at a different level and
that can be successfully applied besides SeReNe.
SeReNe generates sensor network models by means
of two steps: (1) Selection of sensor representatives
and (2) Correlation analysis.
http://www.ijettjournal.org
Page 139
International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 3- June 2016
towards the target using the shortest path. Moreover,
localization can be accomplished without any
assistance from the stationary sensor networks.
Simulation results authenticate nearly a 40%
reduction in target accretion time compared to a
random walk model.
Fig 3: Architecture of the SeReNe framework
Temporal correlations among sensor data streams
are catches by the correlation analysis block, in
terms of correlation time, correlated sensors and
strength. Furthermore, it discovers spatial
correlations
among
faraway
sensors
and
neighbouring sensors. From the set of correlated
sensors, the second phase allows to out a subset of
sensors from the network, called RepresentativeSensors which also known as R- sensors, which best
represent all network nodes. Several criteria may be
considered (e.g. transmission cost among sensors
and distance) during the selection of R-Sensors. The
time window in which the sensor network model is
effectively acting the network state is computed by
guaranteeing a user-provided the error bound τ.
Furthermore, the network model can be reliably
altered to network changes by continuously
analysing the data, collected through the network
itself, thus allowing the apprehension of deviations
from the modelled behaviour Sensor network models
generated by SeReNe are exploited to efficiently
query the network. Smart sensors are characterized
by sensing capabilities computational, and
communication. The execution plan and the query
are broadcast to (interested) sensors to collect sensor
data. Hence, a transmission schedule must be
generated. Transmission schedule generation block
of the SeReNe Framework goal is to identifying an
energy-saving
model,
which
minimizes
communication costs.
II. LITERATURE REVIEW
Cesare Alippi, Giuseppe Anastasi (2009) uses
effective energy management strategies should be
include policies for an economic use of energyhungry sensors, which become one of the
components affecting the network lifetime. In this
paper, the author surveys the main approaches for
efficient energy management in the sensor networks
with energy-hungry sensors.
QingquanZhanga,GeraldE.Sobelmana(2009)
propose a novel gradient-based method which uses
the statistical techniques to evaluate the position of
a stationary target. Mobile nodes can be directed
ISSN: 2231-5381
Xueli Shen and Huan Zhang (2011) present the
radio propagation path loss and the previous
weighted centroid location algorithm is enhanced.
The weighted factor in the algorithm is accessed,
which makes positioning accuracy and positioning
error of the unknown nodes better. Firstly, the paper
uses three-point location to figure location of
unknown nodes approximately and secondly, the
paper uses a modified weighted centroid location
algorithm to figure the unknown nodes precisely.
Finally, the algorithm is affected.
Balasubramaniam Natarajan, Ahmad Ababnahet
et.al (2011) develops a novel optimal control theory
based formulation of this sensor deployment
problem. Exploiting affinity between the problem at
the hand and linear quadratic regulator, an analytical
solution is tested and derived. Unlike prior efforts
that await purely on heuristics, the proposed optimal
control framework gives a theoretical basis for the
resulting solution.
Hongbo Jiang, Chonggang Wang et.al (2011)
develops an energy-efficient framework for the
clustering-based data collection in WSNs by
integrating adaptively enabling & disabling
prediction scheme. The framework is clustering
based. All sensor nodes are representing by the
cluster head in the cluster and collects data values
from them. To the realize prediction techniques
efficiently in WSNs, The author present adaptive
scheme to control prediction used in our framework,
analyse the performance trade-off between limiting
prediction cost and reducing communication cost,
and design algorithms to exploit the profit of
adaptive scheme to enable & disable prediction
operations.
Nithyakalyani
and
S.
Suresh
Kumar
(2012)presents K-Means Data Relay (KMDR)
clustering algorithm for grouping the sensor nodes
there by reducing number of nodes transmitting data
to sink node(base station), it increases the network
performance by reducing the communication
overhead. Furthermore Observe and Conserve
Modes algorithm reduces the numbers of the nodes
within cluster there by without compromising the
analysis face major challenges such as constraints in
power supply and storage resources region of it and
limited communication bandwidth. The contribution
of K-MDR is to reduce power consumption; the
simulation experimental results show that the time
efficiency of the algorithm is achieved.
http://www.ijettjournal.org
Page 140
International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 3- June 2016
LuigiCoppolino,SalvatoreD’Antonio(2013)
propose a lightweight, hybrid, distributed Intrusion
Detection System for WSNs. This Intrusion
Detection System uses both anomaly-based and
misuse-based detection techniques. It alleviate
Central Agent, which performs highly accurate
intrusion detection by using the data mining
techniques, and a number of the Local Agents
running lighter anomaly-based detection techniques
on motes. The accuracy of the proposed IDS has
been validated and measured through an extensive
experimental campaign.
V. Karthik (2013) proposed the data collection
method involves deployment of the multiple mobile
robots whose goal is to gather data from the nodes
whose energy level is below the threshold value.
Navigation of mobile robots is to collect the data
from partitioned nodes usually achieved by the time
and the location based strategies. In proposed hybrid
scheduling, navigation of mobile robots justified by
both the combination of location and time based
approaches with multiple region scheduling.
M. Vijayalakshmi , V. Vanitha (2013) proposed
Prediction and Clustering techniques, which use the
temporal correlation among sensor data, provide a
chance for reducing energy consumption of the
continuous sensor data collection. Thus it can
achieve prolongs and stability network lifetime. An
adaptive scheme is presented which is used to
control the prediction and analyse the performance
trade-offs between reducing prediction cost and
communication cost, and design the algorithms to
take the benefit of adaptive scheme to enable or
disable prediction operations. Localized the
prediction scheme is performed, which takes the
advantages over the previous dual-prediction scheme
to minimize computation and communication cost
thereby reducing the energy consumption.
Shruti Kukreja, Prof Poonam Dabas (2015)
propose a refine clustering method for improving the
network lifetime. Unlike LEACH where the
clustering process is based on the random selection
of cluster heads, the author use k-nearest neighbour
(k-NN) to create cluster in the network then the
author use local heuristic search technique- search to
find CHs. So that minimum energy is consumed in
any transmission. The authors reviewed different
techniques for clustering and optimization and
finally propose the hybrid method of these two
techniques.
III. PROPOSED SYSTEM
To make the lifetime of the entire network to be
increased without comprising coverage area within
its cluster by minimizing the number of nodes
contributing to sense and forward data Algorithm for
sensor activation selects subset of nodes into the
monitoring state while remaining sensor nodes go to
ISSN: 2231-5381
conserve mode. N nodes are uniformly and
randomly dispersed in a square field of size M*M.
All the nodes and the base station are stationary. All
nodes can use power control for different distances
from the transmitter to receiver. All the nodes are the
location unaware (i.e. they are not equipped with
GPS-devices).All the nodes are homogeneous (all
capacities).For long range transmissions the CH are
dominant for performing computations to the base
station. Each and every sensor node transfers data
directly only with the other nodes in its cluster. A
new approach named proposed AODV (pattern
variation discovery) is used for solve this problem.
This approach works in following four steps:
A. Selection of a reference frame. This frame
consists of directions along which we want to
look for irregularities among the multiple
sensory attributes. An analyst can be explicitly
specifying the reference frame. It’s also possible
to discover reference frame that results in a lot
of the irregularities.
B. Definition of normal patterns. This definition
can be models of the multiple sensory attributes
or constraints among the multiple attributes.
C. Discovery of irregularity. Whenever normal
pattern is broken at the some point along the
reference frame, irregularity appears. That is,
the pattern variation happens
IV. EXPERIMENTAL RESULT AND ANALYSIS
Implementation of the whole system carried out
using network simulator NS2 as version 2.34.tar.gz.
NS-2 stands for the Network Simulator version 2.
NS-2 is a discrete event simulator for the networking
research. This simulator works at the packet level.
NS2 is simply an event driven simulation tool that
has proved useful in the studying dynamic nature of
the communication networks. NS-2 uses a TCL as
scripting language. Simulation of wired as well as
wireless network protocols and functions (e.g., TCP,
UDP, routing algorithms) can be done using NS2. In
general, NS2 accommodate users with a way of
specifying such network protocols and simulating
their corresponding behaviours. NS2 consist two key
languages:
Object-oriented
Tool
Command
Language and C++.
Firstly, the proposed data mining technique is
implemented in the TCL and the results are
simulated under the environment of the Network
Silulator-2 (NS-2). Firstly, analysis has been made
to check the number of packets with anomalies
populate in wireless sensor networks (WSNs) as we
keep on increasing simulation time. We have total
1000 packets. The following table and line graph
predict the analysis that has been made:
http://www.ijettjournal.org
Page 141
International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 3- June 2016
V. TABLE I
SHOWING VALUES OF PACKETS WITH ANOMALIES FOR
SIMULATION TIME=15 SEC
Simulation Time
Packet with Anomalies
0
0
1
0
2
6
3
6
4
6
5
41
6
74
7
104
8
142
9
175
10
205
11
240
12
255
13
255
14
255
Fig.5
We have taken total of thirty nodes in our simulation
evaluation process as shown in the figure above. In
above figures, it is being observed that in the starting
of the simulation process we have network with
thirty nodes and the packets are transmitted from one
node to other node, as soon the packets with the
anomalies or irregularities are detected. As the
Anomalies are detected, Energy consumption by
network is less which increases the efficiency of the
Wireless Sensor Networks.
Fig.4
In the fig below, a line graph between the Number of
Packets (abnormal) and the simulation time. After
the analysis, we applied our data mining technique
to obtain results that shows that, this technique is
quite helpful to save the of wireless sensor networks.
There are total 30 nodes in the network and out of
which only 7 are using the energy of complete WSN.
Below are the screenshots of the results that are
obtained by using ns-2 simulator.
V. CONCLUSION
Sensor nodes are capable of transmitting and
sensing. They collect large amount of data in a
highly decentralized manner. The collected data
contain all the information about the region. But
sometimes users are need only the specific
information and rest of information is treated as
irrelevant. So, here we filter out that irrelevant data
or irregularities for the benefit of the users. In future,
same can be used to extract the desired information
from the set of the large information.
AODV is being compared with the proposed AODV.
Proposed AODV has better performance as
efficiency of the system is being increased by
filtering out the unnecessary data. The proposed
AODV has been compared with existing AODV
protocol at different simulation time. After that the
result shows that the packets with anomalies are
being filtered out using the data mining concept and
the proposed AODV is more energy efficient than
the existing AODV. For getting the best results
filtering concept is added.
VI. FUTURE SCOPE
Future aspects for the proposed system are bright.
We can use the concept of the clustering for filtering
out data with anomalies. Other mining concepts are
also available.
ISSN: 2231-5381
http://www.ijettjournal.org
Page 142
International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 3- June 2016
References
[1] Cesare Alippi, Giuseppe Anastasi, “Energy Management in
Wireless Sensor Networks with Energy-hungry Sensors”,
IEEE Instrumentation and Measurement Magazine,April
2009.
[2] QingquanZhanga, Gerald E. Sobelmana, “Gradient-based
target localization in robotic sensor networks”, Pervasive
and Mobile Computing” , February 2009.
[3] Xueli Shen and Huan Zhang*, “Improvement of Centroid
Location
Algorithm
for
Wireless
Sensor
Networks”,International Conference on Computer Science
and Information Technology,2011.
[4] Balasubramaniam Natarajan, Ahmad Ababnah, “Optimal
Control-Based Strategy for Sensor Deployment”,IEEE
transactions on systems, man, and cybernetics ,January2011.
[5] Hongbo Jiang, Chonggang Wang, “prediction or not? an
energy-efficient framework for clustering-based data
collection in wireless sensor networks”,IEEE transactions
on parallel and distributed systems,June2011.
[6] Nithyakalyani and 2S. Suresh Kumar, “Data Relay
Clustering Algorithm for Wireless Sensor Networks: A Data
Mining Approach”,Journal of Computer Science 8 (8), 2012.
[7] Luigi Coppolino, Salvatore D’Antonio, “Applying Data
Mining Techniques to Intrusion Detection in Wireless
Sensor Networks”,Eighth International Conference on P2P,
Parallel, Grid, Cloud and Internet Computing, 2013.
[8] V. Karthik, “Region Based Scheduling With Multiple Mobile
Robots for Data Collection Strategies in Wireless Sensor
Networks”, International Journal of Innovative Research in
Science, Engineering and Technology,July 2013.
[9] M. Vijayalakshmi , V. Vanitha, “Cluster based adaptive
prediction scheme for energy efficiency in wireless sensor
networks”, International Research Journal of Mobile and
Wireless Communications , June2013.
[10] Shruti Kukreja, Prof Poonam Dabas, “ Clustering in WSN
Using
Data
Mining
and
Classification
Technique”,International Journal of Advanced Research in
Computer Science and Software Engineering ,February
2015.
ISSN: 2231-5381
http://www.ijettjournal.org
Page 143