Energy Efficient Techniques for Data aggregation and collection in WSN

International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
DOI : 10.5121/ijcsea.2012.2405 37
Energy Efficient Techniques for Data aggregation
and collection in WSN
Sumit Chaudhary1
, Neha Singh2
, Avinav Pathak3
and A.K Vatsa4
123
IIMT Institute of Engineering & Technology, Meerut, U.P, India
iimtsumit@gmail.com,singh.neha773@gmail.com, avinav05@gmail.com
4
Shobhit University, Meerut, U.P, India
avimanyou@rediffmail.com
ABSTRACT
A multidisciplinary research area such as wireless sensor networks (WSN) have been invoked the
monitoring of remote physical environment and are used for a wide range of applications ranging from
defense personnel to many scientific research, statistical application, disaster area and War Zone. These
networks are constraint with energy, memory and computing power enhance efficient techniques are
needed for data aggregation, data collection, query processing, decision making and routing in sensor
networks. The problem encountered in the recent past was of the more battery power consumption as
activity increases, need more efficient data aggregation and collection techniques with right decision
making capabilities. Therefore, this paper proposed the efficient and effective architecture and mechanism
of energy efficient techniques for data aggregation and collection in WSN using principles like global
weight calculation of nodes, data collection for cluster head and data aggregation techniques using data
cube aggregation.
KEYWORDS
Wireless Sensor Network (WSN), Energy Efficient, Clustering, Cluster Head Selection, Data Aggregation,
Data Collection, Data Cube.
1. INTRODUCTION
A multidisciplinary research area such as wireless sensor networks [2, 4, 6, 7, 18] where close
collaboration between users, application domain experts, hardware designers, and software
developers is needed to implement efficient systems.
Wireless sensor networks consist of small nodes with sensing, computation, and wireless
communications capabilities. Many routing, power management [17, 19, 24], and data
dissemination protocols have been specifically designed for WSNs where energy awareness is an
essential design issue. Routing protocol [5, 20] in WSNs might differ depending on the
application and network architecture.
Sensor nodes are small, inexpensive battery power wireless devices with only few memory spaces
and processing power. A sensor network is a collection of sensor nodes cumulated in an ad-hoc
fashion. The parameter being used to control the overall energy and battery power are rationalized
to provide best possible solution. Accuracy can be a matter of consideration as distance varies
from cluster to cluster but can be used to provide a variety of research application, mobile
communication and location tracking system.

38
Wireless Sensor Network [1, 2, 4, 6] generates a large amount of data that has to be aggregated at
various levels. A multidimensional aggregation approach [7, 9, 13, 14, 15] is considered for
exhibiting the node parameters for each network. Bandwidth, memory, signal strength, time,
battery power etc. have been utilized to examine the performance of a sensor network, its
efficiency can be enhanced by reducing the cost of cluster development. Sensor nodes are
becoming popular in mobile communication technology due to their fast communication speed
and better result generation in information systems.
Sensor nodes are useful in disaster, war zone and several modern technology like mobile
technology, laser technology etc where the data has to be transferred accurately and in a fraction
of time where each node is responsible for the extraction and transfer of data such that the data to
be exchanged cannot be lost on its way to the receiver.
Data Aggregation uses the parameters of nodes joining the cluster so that the data attributes are
selected and stored in an aggregated format for further evaluation and usage. Aggregation refers
to the technique that models the data and information in a dimensional construct that is easy to
store and retrieve. The data collection technique is being employed to store and collect data items
and parameters on a database server. All the related data items are stored in accessible data form.
The problem encountered in the recent past was of the battery power consumption [5, 6], more
efficient data aggregation and collection techniques with right decision making capabilities,
Therefore, this paper proposed the efficient and effective architecture and mechanism for
mentioned problem using principles like global weight calculation of nodes , data collection for
cluster head and data aggregation techniques using data cube aggregation.
This paper is organized into sections. The sections provide the information about the parts and
modules of the research undertaken by the current statements. Section-I provides the introduction.
On basis of literature survey the Section - II includes the background of the paper, Section - III
deals with the proposed work for energy efficient data collection and aggregation techniques in
WSN. The conclusion is stipulated in Section - IV. The proposed work may be extended further
with reference to the different situations are mentioned in section - V under heads of future
aspect. Finally Section - VI mentioned all references used in this paper under heads of
References.
2. BACKGROUND
A multifaceted approach in the field of WSN for research has been undertaken in recent past.
Several Parameters were used for decision making, especially in disaster and war zone with a
limited field of works. Therefore the efficiency and power consumption [5, 6] was of very much
concern and conflict in usage. Wireless sensor nodes were initially used to transfer the
information about the node with associated data and exchange the facts along with the existing
base station of the mobile user. The user had to suffer with considerable energy loss and
bandwidth consumption [17, 18, 21]. There is no efficient techniques were present in previous
ages that could enable a better power and battery efficient framework for data transfer.
Continuous research and development in the field has provided new improved solutions so that
the clustering methods can be used for efficient outputs. The flexibility, fault tolerance, high
sensing fidelity, low cost, and rapid deployment characteristics of sensor networks create many
new and exciting application areas for remote sensing [3, 5, 9, 13, 15, 23]. This wide range of
application areas will make sensor networks an integral part. However, realization of sensor
networks needs to satisfy the constraints introduced by factors such as fault tolerance, scalability,
cost, hardware, topology change, environment, and power consumption.

39
A WSN consists of a large number of sensor nodes. Each sensor node senses environmental
conditions such as temperature, pressure and light and sends the sensed data to a base station
(BS), which is a long way off in general. Since the sensor nodes are powered by limited power
batteries [5, 6] in order to prolong the life time of the network, low energy consumption is
important for sensor nodes. In general, radio communication consumes the most amount of
energy, which is proportional to the data size and proportional to the square or the fourth power
of the distance. In order to reduce the energy consumption, a clustering and data aggregation [7,
9, 13, 14, 15, 23] approach has been extensively used. In this approach, sensor nodes are divided
into clusters, and for each cluster, one representative node, which called cluster head (CH),
aggregates all the data within the cluster and sends the data to BS. Since only CH nodes need long
distance transmission, the other nodes save the energy consumption. Efficient data collection [2]
in WSN plays a key role in power conservation. It has spurred a number of researches focusing
on effective algorithms that reduce power consumption with effective in-network aggregation
techniques. Up to now, most approaches are based on the assumption that data collection [11]
involves all nodes of a network. There are large numbers of queries that in fact select only a
subset of the nodes in a WSN. Thus, the selective queries like queries that request data from a
subset of a WSN. It is also argue that selective queries are an important class of queries that can
benefit from algorithms that are tailored for partial node participation of a WSN.
3. PROPOSED WORK
The proposed work is discussed in Section 3.1 and 3.2 are as follows.
3.1. Architecture of energy efficient techniques for data aggregation and collection
in WSN
• Sensor node: Sensor node is the primary working component that performs various
activities like cluster creation, data collection, transfer data among switching centers and
so on.
• Sensors Parameters: The parameters like bandwidth, memory, time-to-live, radio signal
strength Indicator (RSSI), MRIC are identification factors for WSN architecture.
• Newly Arriving Node: Current numbers of nodes present in the cluster and newly
arriving nodes are managed by functional parameters used in cluster creation parameters.
• Cluster Creation: Collection of nodes that satisfy the parameter requirements ultimately
form a cluster.
• Cluster Head Assignment: An individual cluster head is selected by evaluating the
minimum cost of that node who will serve as the head.
• Threshold battery power: Threshold battery power is checked or evaluated against the
present status of battery of the cluster head.
• Collection of Data: Data is collected from various nodes participating in the
communication and stored in a remote location for further access.
• Query Processor: User defined queries are accepted and generated at clients end and
data is retrieved from the database for a specific query.
• Aggregation: Aggregation technique like data cube collection approach has been used
for storage of node parameter values and cluster locations (Base Station). Data cube
approach supports various phases in a graphical format that is easy to understand and
access.

40
3.2. Working Principle
The working of WSN proposed architecture model illustrated in Figure -1, which starts working
by selecting group of nodes and divided into clusters. These clusters will satisfy the intended
parameter requirements and conditions. The parameters like RSSI, TTL, MRIC, bandwidth,
battery consumption have been used to determine the number of nodes that would be considered
in a cluster. Thereafter a cluster head (CH) is selected among nodes lies inside the every cluster.
CH will be responsible for administration of all other nodes inside respective cluster and
collecting the data from the nodes inside the cluster and transferring the data to the neighboring
cluster head for further information exchange and updation. The newly arrived nodes will be
assigned as cluster head if the global cost of arrived node is minimum , otherwise other cluster
nodes will be given opportunity to participate and global cost is again recalculated. Thereafter the
data aggregation approach is presumed as the collection of data and various queries from the user
end are checked and transformed into low level schemes by a query processor. All data collected
and aggregated is stored at a storage location in database server. Finally at last the data is
aggregated by data cube approach and all the aggregated data will be transfer to the base station
for further use.
Figure 1. Architecture of Data Collection and aggregation for WSN.
3.3. Mechanism of energy efficient techniques for data aggregation and collection in
WSN
The proposed mechanism is discussed into four phases
Phase 1 - Cluster and Cluster Head creation in WSN
Step 1:- Assign node id for each node of WSN:
No of node=N
For (i=0; i<N; i++)
{
ss_id[i] =RandomNoGenerator ( ); // ss_id is Sub System ID
}

41
Step 2:- cluster creation:
ClusterCreation ( )
{
For (ss_id=0; ss_id<N; ss_id++)
{
If (Bcon >= Apr && Pm>=Tm) // Bcon = Battery consumption
// Apr =Present Cluster battery level
// Pm= Present memory
// Tm= Total cluster memory
{
Node will not be Included in Cluster
}
else if ((TTLnew>TTLclst))
// TTLnew= Time To Live new node
// TTLclst=Time To Live cluster
If ((MRICnew>MRICclst) && (RSSInew<RSSIclst) && (Bandnew>Bandclst))
/* MRICnew= Multicast Routing Information Cost
MRICclst=Multicast Routing Information Cost of
cluster
RSSInew=Reverse Signal Strength Indication of new
node
RSSIclst =Reverse Signal Strength Indication of new
node
Bandnew= Level Bandwidth new node
Bandclst= Level Bandwidth cluster
*/
{
No new node can join Cluster ( );
}
else
{
Join cluster;
}
}
Step 3:- ClusterHeadAssignment:
ClusterHeadAssignment ( )
{
Total no of node = n;
For (cluster1 to clustern)
{
For (i=0; i<n; i++)
{
/*initialize parameter for cost evaluation*/
Bcon[i] = {}; /* Battery consumption at node i*/
Apr[i] = {}; /*Present Cluster battery level at node i*/
Pm[i] = {}; /*Present memory at node i*/
Tm[i] = {}; /*Total cluster memory*/
TTLnew[i] = {}; /*Time To Live new node*/

42
TTLclst[i] = {}; /*Time To Live cluster*/
MRICnew[i] = {}; /* Multicast Routing Information Cost at node i*/
MRICclst[i] = {}; /* Multicast Routing Information Cost of cluster*/
RSSInew[i] = {}; /* Reverse Signal Strength Indication of new node*/
RSSIclst [i] = {}; /* Reverse Signal Strength Indication of new node*/
Bandnew[i] = {}; /* Level Bandwidth new node*/
Bandclst[i] = {}; /* Level Bandwidth cluster*/
/* now calculate the cost for each node */
C (i1) = (Apr[i]* Bandnew[i] /Bcon[i]*Bandclst[i])
C (i2) = (Pm[i] * TTLnew[i] /Tm[i] *TTLclst[i])
C (i3) = (MRICnew[i]*RSSInew[i]/ MRICclst[i]* RSSIclst [i])
}
Find out min. global cost for each node.
GCmin=∑ C (i1) + C (i2) + C (i3)
}
min = 0; max = 0;
{
for (i=1 to n)
{
C (i1) = (Apr[i]* Bandnew[i] /Bcon[i]*Bandclst[i])
C (i2) = (Pm[i] * TTLnew[i] /Tm[i] *TTLclst[i])
C (i3) = (MRICnew[i]*RSSInew[i]/ MRICclst[i]* RSSIclst [i])
GCmin = C (i1) + C (i2) + C (i3);
If (max< GCmin)
{
max = GCmin;
}
else if (min> GCmin)
{
min = GCmin;
}
}
Step 4:-Newly Arriving Node In WSN:
NewlyArrivingNodeInWSN ( )
{
• Calculate the following factor Bcon[i], Apr[i], Pm[i], Bandnew[i], Bandclst[i], TTLnew[i],
Tm[i], TTLclst[i], MRICnew[i], RSSInew[i], MRICclst[i], RSSIclst [i];
• Calculate C (i1), C (i2), C (i3);
• Calculate GCmin;
• If (newly arrive node GCmin < cluster head GCmin)
{
Assign new node as cluster head;
}
else
{
Join cluster ( );
}
}

43
Step 5:-Threshold For Battery Power:
ThresholdBatteryPower ( )
{
Check the battery power of cluster head
If (CH_battery power < PTHRESHOLD)
{
CH sends battery power low signal to its neighbor & recalculates the global weight for
each node and minimum global weight node assign as cluster head.
}
else
{
No requirement;
}
}
Phase 2 – Cluster head data collection:
ClusterHeadDataCollection ( )
{
• The number of nodes has to be associated with various parameter or node parameters.
• All the nodes are aggregated at cluster level.
• The parameters useful for node information are collected and stored at each cluster head
• The cost is evaluated on the basis of collected parameters.
• Minimum global cost is evaluated.
• All the cost parameters are send to the cluster head for further association
• Next the transfer is to the base station.
}

44
Figure 2. UML diagram of data collection technique
Phase 3 – Data aggregation technique based on data cube aggregation
Data Cube Aggregation: It is a multidimensional approach for data aggregation. The values are
stored in separate cell of a data cube, each phase of cube is divided into separate rows & columns
and each value & node such as consumption, bandwidth, MRIC, RSSI etc are represented at the
beginning of rows.

45
Figure 3. Data cube technique of data aggregation
4. CONCLUSIONS
The paper widely acclaims the improved technology for energy efficient techniques for data
aggregation and collection in WSN. The paper provides the accurate usage of battery and low
power consumption so that the user can send multiple messages in limited resources. The
parameters that are used manage the cluster head generation, and the node selection methods so
that the message can be easily transferred under such circumstances with right decision using
principles like global weight calculation of nodes, data collection for cluster head and data
aggregation techniques using data cube aggregation.
5. FUTURE SCOPE
The proposed architecture and mechanism is efficient and effective but the field of scalability,
heterogeneous behavior of node and base station, mobility of sensor node is fully composite with
respect to the ongoing advancement in this field. The work done related to mentioned
methodology has been effectively stipulated in mobile computing, disaster, war zone and
infrastructure-less environment etc. All the approaches are designed to be user- friendly and more
importantly is a futuristic gerard of technology.

46
REFERENCES
[1] Guan Xin, “An Energy-Efficient Clustering Technique for Wireless Sensor Networks”, IEEE
Conference, ISBN: 978-0-7695-3187-8, 25 July 2008
[2] Mohammad Hossein Anisi, Abdul Hanan Abdullah, Shukor Abd Razak, “Energy-Efficient Data
Collection in Wireless Sensor Networks”, Wireless Sensor Network, pp.329-333, October 2011.
[3] Ademola P. Abidoye, Nureni A. Azeez, Ademola O. Adesina, Kehinde K. Agbele, “A Novel
Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks”, pp.307-312, September
2011.
[4] Xin Guan, Lin Guan and Xingang Wang, “A Novel Energy Efficient Clustering Technique Based on
Virtual Hexagon for Wireless Sensor Networks”, Volume 7, Issn 1349-4198, pp. 1891-1904, April,
2011.
[5] Shio Kumar Singh, M P Singh, D K Singh, “A Survey of Energy-Efficient Hierarchical Cluster-Based
Routing in Wireless Sensor Network’s”, 570 Volume: 02, Issue: 02, pp: 570-580 (2010).
[6] Shio Kumar Singh, M P Singh, and D K Singh, “Energy Efficient Homogenous Clustering Algorithm
for Wireless Sensor Networks”, International Journal of Wireless & Mobile Networks (Ijwmn), Vol.2,
No.3, August 2010.
[7] D. Kumar, T.C. Aseri, R.B. Pate, “Energy Efficient Clustering and Data Aggregation Protocol for
Heterogeneous Wireless Sensor Networks”, ISSN 1841-9836, E-Issn 1841-9844 Vol. No. 1, pp. 113-
124, (March 2011).
[8] Carlos F. Garcia Hernandez, Pablo H. Ibargüengoytia-González, Joaquín Garcia Hernandez, and Jesus
A. Perez Diaz, “Wireless Sensor Networks and Applications: A Survey”, Ijcsns International Journal
of Computer Science and Network Security, Vol.7 No.3, pp. 264-273, March 2007.
[9] Kiran Maraiya, Kamal Kant, Nitin Gupta, “Architectural Based Data Aggregation Techniques in
Wireless Sensor Network: A Comparative Study”, International Journal on Computer Science and
Engineering (Ijcse), ISSN: 0975-3397, Vol. 3 No. 3, Pp. 1131-1138, Mar 2011.
[10] Noritaka Shigei, Hiromi Miyajima, Hiroki Morishita, Michiharu Maeda, “Centralized and Distributed
Clustering Methods for Energy Efficient Wireless Sensor Networks”, Proceedings of the International
Multiconference of Engineers and Computer Scientists 2009 Vol Iimecs 2009, pp. 18 - 20 March,
2009.
[11] Lars Kulik, Egemen Tanin, and Muhammad Umer, “Efficient Data Collection and Selective Queries
in Sensor Networks”, S. Nittel, Gsn 2006, Lncs 4540, pp. 25–44, 2008. _C Springer-Verlag Berlin
Heidelberg, pp. 25-44, 2008.
[12] Jaydip Sen., “Secure and Energy-Efficient Data Aggregation in Wireless Sensor Networks”, IEEE
Explorer, Volume: 2, Issue: 4, pp: 384–391, Symposium On(2003).
[13] K. Akkaya, M. Demirbas, and R. S. Aygun, “The Impact of Data Aggregation on the Performance of
Wireless Sensor Networks”, Wiley Wireless Communications and Mobile Computing (Wcmc)
Journal, Vol. 8, pp. 171-193, 2008.
[14] H. C¸ Am, S. Ozdemir, P. Nair, and D. Muthuavinashiappan, and H.O. Sanli, “Energy-Efficient and
Secure Pattern Based Data Aggregation for Wireless Sensor Networks”, Special Issue of Computer
Communications on Sensor Networks, Pp. 446-455, Feb. 2006.
[15] R. Mudumbai, D. R. Brown, U. Madhow, and H. V. Poor, “Distributed Transmit Beamforming:
Challenges and Recent Progress,” Vol. 47, pp. 102– 110, February 2009.
[16] J. Feng, C.-W. Chang, S. Sayilir, Y.-H. Lu, B. Jung, D. Peroulis, and Y. Hu, “Energy-Efficient
Transmission for Beamforming in Wireless Sensor Networks,” 2010 7th Annual IEEE
Communications Society Conference, pp. 1 –9, June 2010.
[17] E. Liu, Q. Zhang, and K. Leung, “Residual Energy-Aware Cooperative Transmission (React) in
Wireless Networks,” In Wireless and Optical Communications Conference (Wocc), pp. 1–6, May
2010.
[18] M. Ahmed and S. Vorobyov, “Collaborative Beamforming for Wireless Sensor Networks with
Gaussian Distributed Sensor Nodes,” Wireless Communications, IEEE Transactions, Vol. 8, No. 2,
pp. 638 –643, Feb.2009.
[19] K. Zariﬁ, A. Ghrayeb, and S. Affes, “Distributed Beamforming for Wireless Sensor Networks with
Improved Graph Connectivity And Energy Efficiency,” Signal Processing, IEEE Transactions, Vol.
58, No. 3, pp. 1904–1921, March 2010.
[20] M. Z. Siam, M. Krunz, and O. Younis, “Energy-Efficient Clustering Routing for Cooperative Mimo
Operation in Sensor Networks,” In Infocom, pp. 621–629, 2009.

47
[21] J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, and D. Ganesan, “Building
Efficient Wireless Sensor Networks with Low-Level Naming”, In Proceedings of the Eighteenth Acm
Symposium, pp: 146-159 October 2001
[22] C. Intanagonwiwat, D. Estrin, R. Govindan, and J. Heidemann, “Impact of Network Density on Data
Aggregation in Wireless Sensor Networks”, In Icdcs-22, November 2001.
[23] Mohamed Watfa , William Daher And Hisham Al Azar, “A Sensor Network Data Aggregation
Technique”, International Journal of Computer Theory and Engineering, Vol. 1, No. 1, pp. 1793-
8201, April 2009.
[24] Mohammad Mehrani, Ali Shaeidi, “A Novel Energy Efficient, Distributed, Clustering Based Network
Coverage Method for Enormous WSN”, Global Journal of Computer Science and Technology
Volume 11 Issue 4 Version 1.0, pp. 59-65, March 2011.
BIBLIOGRAPHY OF AUTHORS
Sumit Chaudhary is presently working as Assistant Professor (CSE) at IIMT Institute of
Engineering & Technology, Meerut, (U.P.), INDIA. He received his M.Tech (Computer
Engineering) degree from Shobhit University, Meerut, (U.P) and B.Tech (C.S) degree
with Hons. from SCRIET, C.C.S University Campus, Meerut, (U.P). His area of research
includes Wireless Sensor Networks (WSN), MANET (Mobile Ad-Hoc network) and
Neural Network.
Neha Singh is presently working as Assistant Professor (CSE) at IIMT Institute of
Engineering & Technology, Meerut, (U.P.), INDIA. She received her M.Tech (Computer
Engineering) degree from Shobhit University, Meerut, (U.P) and B.Tech (C.S) degree
with Hons. from SCRIET, C.C.S University Campus, Meerut, (U.P). ). Her area of
research includes Wireless Sensor Networks (WSN) and MANET (Mobile Ad-Hoc
network).
Avinav pathak is presently working as Assistant Professor (CSE) at IIMT Institute of
Engineering & Technology, Meerut, (U.P.), INDIA. He is pursuing his M.Tech
(Computer Engineering) from IIMT Engineering College, Meerut, (U.P) and B.Tech
(C.S) degree from Vidya College of Engineering, Meerut, (U.P.).
Avimanyou Kumar Vatsa is working as Assistant Professor and Coordinator - CSE at
Shobhit University, Meerut, (U.P.), INDIA. He obtained his M-Tech (Computer
Engineering) with Hons. from Shobhit University and B-Tech(I.T.) from V.B.S.
Purvanchal University, Jaunpur (U.P.). He has worked as software engineer in software
industry. He has been in teaching from more than one decade. During this short period of
time, he has been supervised several dissertation of M.Tech. students. He is on the
editorial board and reviewers of several international and national journals in networks
and security field. He has been member of several academic and administrative bodies.
During his teaching he has been coordinated many Technical fests and National Conferences at Institute
and University Level. He has attended several seminars, workshops and conferences at various levels. His
many papers are published in various national, international journals and conferences. His area of research
includes MANET (Mobile Ad-Hoc network), Network Security, Congestion Control and VOIP-SIP (Voice
over IP).

Energy Efficient Techniques for Data aggregation and collection in WSN

Recommended

More Related Content

What's hot (18)

Similar to Energy Efficient Techniques for Data aggregation and collection in WSN (20)

Recently uploaded (20)

Energy Efficient Techniques for Data aggregation and collection in WSN