A New Clustering Technique for Energy Efficient Wireless Sensor Networks with Static Base Stations...

A New Clustering Technique for Energy Efficient Wireless Sensor Networks with

Static Base Stations using SVD and DWT

ABSTRACT

A Wireless Sensor Network (WSN) is a powerful

network connected to the central locations or base

stations via wireless communication to monitor the

environment activities by collecting the information

sensed from the small sensors or nodes with low

power consumption.The collected data need to be

transmitted to the static base station for the data

manipulation, including storing and processing.

Once the sensor nodes have collected the data from

the environment it should be sent to the base station

for the processing to obtain the feature of the

environment and identify it activities.

Since the levels of energy in the sensor nodes are

limited the lifetime and activities of each sensor

node must be carefully considered. One of the most

effective methods that can be used to save the

energy in WSN is the clustering techniques in

which the sensor nodes with almost the same signal

strength will be grouped as one cluster. We also

choose one Cluster Head (CH) for each group and

the sensor nodes collected data will be transmitted

to its CH in which it will transmit all received data

to the base stations for the processing.

In this paper, we have proposed a new Clustering

model using Static Base Stations (BSs). In addition

we have applied two mathematical techniques,

Discrete Wavelet Transform (DWT) and Singular

Value Decomposition (SVD) to decrease the energy

consumption of the sensor nodes and increase the

network lifetime.

Finally, to achieve the low data redundancy and to

increase the accuracy of the collected data, once the

clustering technique is applied, the correlation

algorithm is applied.

KEYWORDS

Wireless Sensor Network, Static Base Station,

DWT, SVD, Correlation

I. INTRODUCTION

Energy-efficiency is a main challenge in WSNs

[1], one powerful technique for energy-efficient

communication is to track the energy-latency

tradeoffs by tuning the transmission time [1]. A

key perception is that in many channel-coding

designs, the transmission energy can be

considerably decreased by reducing the

transmission power and incrementing the

period of transmission [2].

In this paper, we have proposed the Static Multi

Agency- Base Station (MA-BS) in the context

of data collecting in WSNs.

Clustering Objectives:

Clustering algorithms have the different

objectives. Generally the clustering objective is

determined for facilitating meeting the

applications demands. For instance if the

application is data latency sensitive,

connectivity inside the cluster and the length of

the data routing ways are commonly considered

as criteria for CH selection and node

categorization. The popular objectives for

network clustering are as following:

1) Load Balancing:

Although broadcasting of sensor nodes

between the all clusters is generally an

Amir Aghasharif

Student of Department of Computer Engineering

Islamic Azad University, UAE branch, Dubai, UAE

[email protected]

Dr. Mohammad V. Malakooti

Faculty and Head of Department of Computer Engineering

Islamic Azad University, UAE branch, Dubai, UAE

[email protected]

The Proceedings of Second International Conference on Electrical and Electronics Engineering, Clean Energy and Green Computing, Konya, Turkey, 2015

ISBN: 978-1-941968-12-3 ©2015 SDIWC 34

mailto:[email protected]

important goal for establishment where

CHs execute information processing or

powerful clustering administration

needs [1], [2], [3]. Given the needs of

CHs, which it is important to balance

the load between them thus they can

meet the expected performance goals

[1], [2], [3]. Load balancing is a more

critical problem in WSNs where CHs

are picked from the available sensors

[1], [2], [3]. In this situation, setting

equal-sized clusters becomes essential

for extending the network lifetime since

it avoids the consumption of the energy

of a subset of CHs at high rate and early

making them useless. While CHs do

information gathering, it is essential to

have same number of sensor node in the

clusters therefore the related

information details get ready

approximately at the same time for more

processing at the BS or at the next layer

in the network [4].

2) Fault Tolerance:

In the future, WSNs will be useful in

tough situations and therefore sensor

nodes are generally defined to

incremente risk of fault and physical

failure. CHs damage tolerance is

commonly fundamental in these

applications to prevent the fault of main

sensors’ information. One of the useful

methods is to re-cluster the network

from a CH fault. Although, resource

load on sensor nodes is not only re-

clustering method, it is usually most

disturbing to the current operation. For

that reason, fault tolerance methods will

be more suitable. To decrease a CH

failure the most distinguished scheme

attended in the research is assigning

backup. A backup selection and the role

this extra CH will play while normal

network operation ranges. The sensors

can be used by neighboring CHs in the

failing cluster since CHs have long

radio range [1], [2], [3]. In addition to

load balancing advantage for fault

tolerance rotating the role of CHs

among sensor nodes in the cluster can

be useful [1], [2], [3].

3) Decrease Cluster Count:

The network engineer usually tends to

use the minimum number of resource-

rich nodes because they prefer to be

more costly and vulnerable than sensor

nodes. For instance, there will be

inherently some limitation on the

number of sensor nodes in case the CHs

are mobile vehicle, laptop computers or

robots [3]. The limitation can be due to

the complicated set up of this type of

sensor nodes, such as combat area or

forest. Moreover, the number of these

nodes prefers to be significantly more

than a sensor that causes them to be

detected easily. In quite a lot of WSN

applications like military

reconnaissance, substructure security

and border defense, node visibility is

greatly unwanted.

4) Increase Network Longevity:

Since there are energy limitations for

sensor nodes, a major concern is

lifetime in network especially for WSN

applications unstable situations [1], [2],

[3]. It is critical to minimize the intra-

cluster communication energy when

CHs have more resources than sensor

nodes. It should be a good solution to

place the CHs close to the sensor nodes

in same clusters [3]. In other word, the

lifetime of these kinds of CHs will be

extended by bounding their load. To

maximize the lifetime of the network

[3], clustering combination and setup

the route has been also considered.

Another viable selection to achieve the


ISBN: 978-1-941968-12-3 ©2015 SDIWC 35

network longevity is adaptive clustering

[5], [6].

Paper Organization:

This paper is divided into four sections. The

introductory material, clustering techniques as

well as the fault tolerance and load balancing is

discussed in section I. The related work is

briefly discussed in Section II. Our proposed

algorithm and scenario for the energy efficient

WSN that can be used to obtain the feature

extraction and data compression is discussed in

Section III. Finally, the results and conclusions

along with the future work are discussed in

Section IV.

II. RELATED WORK

Markov Chain:

In a Maekov Chain graph from a starting point,

we randomly choose a neighbor of that point

and walk through it. Then, we will select a

neighbor of this point randomly and move to it.

This technique of selecting the point is called

Random Walk (RW). A random walk is a

mathematical formulization of a path that

consists of a succession of random steps.

Thus a RW is a finite Markov chain, which is

time-reversible. In deed, there is no significant

difference between the mechanism of RW on

graphs and the mechanism of finite Markov

chains, another word, every Markov chain can

be considered as a RW in directed graphs, in

such a way we allocate weights to edges. In the

same way, time-reversible Markov chains can

be assumed as a RW in undirected graphs, and

symmetric Markov chains, as RW on regular

symmetric graphs [7], [8].

Clustering:

Saving energy utilization in WSNs is always a

critical issue, which is highly based on network

lifework. The Wireless Sensor Network, are

spatially distributed autonomous sensors or a

set of very small independent devices that can

sense environmental situations in their

immediate surroundings while having finite

processing, transmission capacities and energy

saving [9], [10].

In WSN the clustering is the task of grouping a

set of sensors in such a way that sensors in the

same group or cluster are more similar to each

other than those in other clusters. Clustering is

considered to be an impressive way to reserve

energy utilization and increased the network

lifework. Each cluster method has a CH and

CHs can be predefined or be chosen while

doing clustering with different algorithms. K-

Mean is a very famous and simple clustering

algorithm that selects CHs for K clusters at the

central point of each cluster [5], [6]. This assists

to minimize the intra-cluster energy utilization.

Eventually, CHs drain energy extremely more

than other sensors as they send entire cluster’s

data to the BS.

Most of the K-Mean algorithms require the

number of cluster to be specified in advance

and the clusters to be approximately the same

size. In LEACH [4], [5], [6], sensor nodes

select themselves to be CHs at random. In such

a way, the high-energy waste in communicating

with the BS wills propagation between the

nodes in the network [11], [12].

Using Compressing Sensing (CS) in gathering

data is also an impressive technique to decrease

the number of needed samples from a sparse

signal. Since the correlation among the sensor

readings in WSNs, the controlled signal will

have a sparse representation in a complete

domain like DCT or wavelet. Correspondingly,

CS has reached applications in data collection

in WSNs [13], [14].

III. PROPOSED MODEL:

Ocean covers about three quarters (71%) of the

surface of the earth, which should be constantly

monitored and be checked to observe any

climate changes or pollution of the environment

that affects human and animal habitat. The


ISBN: 978-1-941968-12-3 ©2015 SDIWC 36

present techniques are time consuming,

ineffective and costly and do not cover the wide

area of our interest. In addition, the high

resolution of measuring grid cannot easily be

obtained due to lack of sufficient information.

Our proposed techniques is based on a new

clustering techniques designed for the WSN

applied in the sea area. In this scenario, we

focused on the benefits of WSN in the sea and

explained our model that is used when the senor

nodes are distributed on the surface of the sea

by helicopter. The collected data will be sent to

nearest BS to control and check the water

quality in the sea and oceans. A water quality

controlling system is generally developed to

control water conditions and qualities including

temperature, PH, turbidity, conductivity and

Dissolved Oxygen (DO) for ocean bays, lakes,

rivers and other water bodies. An ocean sensing

and controlling system is used to control ocean

water conditions and other environmental

factors.

In this paper we explain the clustering

algorithm based on Static BS as following:

Static BS

Components:

1. 4 BSs

Send initial signals to the WSNs.

Calculate the distance between

BS and WSNs.

Select the nearest WSNs.

Apply Clustering based on the

shortest distance.

Save the selected IP Addresses

of WSN.

Based on Time Scheduling

Algorithm (TSA) for Saving

Energy, the selected WSNs go to

sleep due to lack of participation

in next round.

Get the information from the

corresponding cluster of WSNs

and compress them by applying

DWT and SVD algorithms.

2. WSNs

First, send the coordinate of each

cluster to the BS.

Once the coordinate of all

clusters are transmitted to the

BS, the CHs can transmit their

collected information.

3. Cloud

Store the information of each

Database corresponding to BS

into the cloud.

4. Agent

Monitor the information of all

Databases and update them.

Compare the received

information and apply the

Correlation algorithm.

As illustrated in Figure-1 we distribute the

WSNs randomly by the helicopter.

Obviously, the vast areas such as sea and forest

don’t have the exact shape like geometric

figures in mathematic.


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Figure1: Distribute the WSNs by Helicopter

Figure-2 shows that we have selected the

rectangle shape that covers the whole region.

Once, the rectangular shape is selected, and

then we will connect the center of heights and

widths. Therefore, we will obtain 4 smaller

rectangles in the next step of operations.

We set up 4 BSs at the center of heights and

widths for managing the WSNs of each cluster.

Figure 2: Consider the Rectangle Shape for the area

According to the TSA, first of all BS-1 wakes

up and sends the initial signal to the WSNs but

the power of signal should be set up to reach to

the center of rectangle for the energy saving

purpose. Only the WSNs of that area can

receive the signal and send back their position

coordinate to the BS-1, Figure-3.

Figure 3: Send Initial Signal by BS-1

In the next step of the BS-1 transfer operation,

we should calculate the distance of each WSN

nodes from the corresponding base station

using the (x, y) coordinates.

Once the distance measurement has done based

on the (x, y) coordinates, BS-1 will select the

nearest WSN nodes to form its cluster, Figure-

4.

Figure 4: Clustering the WSNs for BS-1

Then, in each BS the IP address of WSN nodes

will be saved and corresponding WSN nodes

will be pushed into the sleep mode, Figure-5.

In our proposed method, for the next step of the

clustering for the transfer operation only those

WSN nodes that are not selected and not

pushed to the sleep mode will reply and

participate in the clustering process. Our

method is a novel approach for the energy


ISBN: 978-1-941968-12-3 ©2015 SDIWC 38

efficiency in WSN that can be used to reduce

the mathematical calculation as well as reduce

the redundancy in transmission operations in

which improves the performance of the whole

WSNs. In addition, this method can prevent the

collision challenges exist in WSNs.

Figure 5: WSNs from BS-1 Cluster goes to sleep

Based on TSA, BS-1 and selected WSNs sleep,

BS-2 wakes up and sends the initial signal to

the WSNs, Figure-6.

Figure 6: Send Initial Signals by BS-2

As the figure-7 shows, BS-2 calculates the

distances the same as BS-1 and selects the

nearest WSN nodes for the second cluster.

Figure 7: Clustering the WSNs for BS-2

Figure-8 indicates that WSN nodes in cluster 2

goes to the sleep mode.

Figure 8: WSNs from BS-2 cluster goes to sleep

These processes should be repeated for all other

BSs as shown in Figures-9, but the calculation

operation to obtain the nearest distances should

be repeated N-1 times, where N is the number

of BS in the WSN. Since, in the last round of

operation only the nearest WSN nodes to the

last BS are in the wakeup mode and the rest are

in the sleep mode, the operations will be

repeated N-1 times.


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Figure 9: Final view of Clustering

All BSs are connected to one cloud, which is

monitored by the agent shown in Figure-10.

Thus, the BSs upload the information stored in

their Database on the cloud and agent can check

all clusters to make sure all information have

been transmitted to the cloud. If there is any

things wrong during the transmission process,

the agent will correct and update the Database.

Figure 10: Upload the information into the cloud and

sync with agent

Once the clustering process is finished, the BSs

will get information form their corresponding

sensors. This information will be compressed to

save the transmission bandwidth and increase

the lifetime of the network. We have used

DWT and SVD algorithms to compress the

information captures from the WSN nodes.

Discrete Wavelet Transform (DWT):

In this paper we have used an energy-efficient

technique to enhance the energy and lifetime of

WSN nodes based on the DWT and SVD

Algorithms. These algorithms are applied on

the captured data obtained from the WSN nodes

for the data compression and data redundancy

methods, respectively [15], [16]. Our proposed

method will decrease the transmitted and

received data volume in which it incrementes

the speed of calculations, decrease the overall

energy utilization and bandwidth to increase the

lifetime of WSN. The process of generating the

useful data with minimum volume during the

transmission time is very vital [17]. The node

sensors in WSN, which are placed inside one

cluster usually, transmit similar data with the

high rate of redundancy. Our proposed method

has considered this problem and can be used to

compress the sensed data and reduce the data

redundancy before the transmission operation is

applied, Figure-11. We also have applied the

DWT on all sensed data to decrease the size of

data by removing the high frequency

parameters that carry less information and are

not so important for the quality of the data.

Thus, those data that are passed through our

algorithms have the important features of the

sensed data and contains the best features of the

data inside the cluster group with the minimum

data volume which is suitable for WSN to

transmit the data from WSN nodes to BS [17].

Singular Valued Decomposition (SVD):

The SVD of any real or complex matrix A ,is

the decomposition of matrix A in to two

orthogonal matrices, U and V, called left and

right singular vectors, respectively, and one

diagonal matrix called the singular values of A,

where A= UƩVt . The SVD is one of the

strongest mathematical tools for matrices that

can be used to find the best approximation of

the matrix A by using the K-largest singular

values of the matrix A. To reach to our goals

for data reduction in WSN prior to the


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transmission process, we have performed the

data aggregation locally in CH and then applied

the SVD in BSs for data aggregating. By

applying SVD on each cluster we can reduce

the number of the sensed data by removing the

repetitive data and similar data [17].

Figure 11: DWT Diagram

Figure 12: One Step DWT Decomposition

Figure 13: Four Steps DWT Decomposition

Generate the Eigen Values:

|[R- λI]|=0 Eigen values : λ1, λ2, ..., λn (1)

(R- λI) Y =0 Eigen vectors : Y1, Y2, .., Yn (2)

The SVD of R, R= U Ʃ Vt

(3)

U: left singular vectors

V: right singular vectors

δ = √λ (4)

Vi = Yi / ||Yi|| (5)

U= R V Ʃ-1

(6)

(7)


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Figure 14: Input Image to Apply SVD

Figure 15: Apply SVD on RGB

Figure 16: Apply SVD on RGB

Correlation:

Once the data compression is performed, then

the BSs will upload the information saved on

the Database into the cloud storage facilities.

Thus, the agent should apply the correlation

process to remove the duplicate information

captured by the WSN nodes as shown in

equation (8) [17].

(8)

IV. CONCLUSION AND FUTURE

WORKS

In this paper we proposed an energy-efficient

data gathering in WSNs, which is based on a

multi agent clustering and DWT. We illustrated

that our scenarios for clustering provide a

significant energy savings for data gathering

projects in WSNs. Our technique can be used to

broadcast sensor nodes information through the

short paths from sensor nodes to the BS that

significantly saves energy. All power

utilizations are analyzed, formulated and

simulated. For the future works, we will

concentrate on algorithm to improve the

clustering methods and reduce the energy

consumption. For example, in some cases the


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number of sensors in one cluster is very low so

that it’s better to restructure the clustering.

REFERENCES

[1] M.T. Nguyen, K.A. Teague and N. Rahnavard “Inter-

Cluster Multi-hop Routing in Wireless Sensor Networks

employing Compressive Sensing” , Oklahoma State

University , IEEE Military Communications

Conference, 2014.

[2] M.T. Nguyen, K.A. Teague, “Compressive Sensing

Based Data Gathering in Clustered Wireless Sensor

Networks ”, Oklahoma State University , IEEE

International Conference on Distributed Computing in

Sensor Systems, 2014

[3] J.Heidemann, F.Silva, C.Intanagonwiwat,

R.Govindan, D.Estrin, D.Ganesan, “Building Efficient

Wireless Sensor Networks with Low-Level Naming”,

University of California, 2001

[4] Y.Sun, C.Cui, Sh.Ke, J.Lu, “Research on Dynamic

Clustering Routing Considering Node Load for Wireless

Sensor Networks”, North China Electric Power

University, 2013

[5] J.N. Al-Karaki, A.E. Kamal, “Routing Techniques in

Wireless Sensor Networks: A Survey” Iowa State

University, San Francisco, CA: Morgan Kaufmann

Publishers, 2011

[6] Y.Yu, V.K.Prasanna, and B.Krishnamachari,“ Energy

Minimization for Real-Time Data Gathering in Wireless

Sensor Networks ”, IEEE Transactions on Wireless

Communications VOL. 5, NO. 11, 2006

[7] N.Patwari, A.O. Hero, M.Perkins, N.S. Correal, and

R.J. O.Dea, “Relative Location Estimation in Wireless

Sensor Networks”, IEEE Transactions on Signal

Processing, VOL. 51, NO. 8, 2003

[8] D.C. Dhanapala, A. P. Jayasumana, Q.Han ,“ On

random routing in wireless sensor grids: A mathematical

model for rendezvous probability and performance

optimization ”, Colorado State University, Colorado

State University, J. Parallel Distrib. Comput. 71, 2011

[9] A.A.Abbasi, M.Younis, “A survey on clustering

algorithms for wireless sensor networks”, University of

Maryland, 2007

[10] K.C.Tun , K.Kunavut, “An Overview of Cell

Zooming Algorithms and Power Saving Capabilities in

Wireless Networks”, Assumption University, 2014

[11] N. M. Abdul Latiff, C. C. Tsimenidis, B. S. Sharif,

“Energy-Aware Clustering for Wireless Sensor Networks

Using Particle Swarm Optimization”, Newcastle

University, The 18th Annual IEEE International

Symposium on Personal, Indoor and Mobile Radio

Communications, 2007

[12] R.Krishnan, D.Starobinski, “Efficient Clustering

Algorithms for Self-Organizing Wireless Sensor

Networks”, Boston University

[13] D. J. Dechene, A. El Jardali, M. Luccini, and A.

Sauer, “A Survey of Clustering Algorithms for Wireless

Sensor Networks“,The University Of Western Ontario

[14] A. B. M. Alim Al Islam, M.S.Hossain,

V.Raghunathan, “Dynamic Clustering with Relay Nodes

(DCRN): A Clustering Technique to Maximize Stability

in Wireless Sensor Networks with Relay Nodes”, Purdue

University, Int. J. Communications, Network and System

Sciences, 2012

[15] M.V. Malakooti, N.Mansourzadeh, “A Two Level-

Security Model for Cloud Computing based on the

Biometric Features and Multi-Level Encryption”, Islamic

Azad University, The Proceedings of the International

Conference on Digital Information Processing, Data

Mining, and Wireless Communications, Dubai, UAE,

2015

[16] M.V. Malakooti, A.Aghasharif, “Prediction of

Stock Market Index based on Neural Networks, Genetic

Algorithms, and Data Mining Using SVD”, Islamic Azad

University, The Proceedings of the International

Conference on Digital Information Processing, Data

Mining, and Wireless Communications, Dubai, UAE,

2015

[17] M.V. Malakooti, N.Mansourzadeh, “A Robust

Information Security Model for Cloud Computing Based

on the Scrambling Algorithm and Multi-Level

Encryption”, Islamic Azad University, the International

Conference on Computing Technology and Information

Management, Dubai, UAE, 2014


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