An Efficient Data Aggregation and Query Optimization for Energy

International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869, Volume-2, Issue-5, May 2014
An Efficient Data Aggregation and Query
Optimization for Energy Efficiency in Wireless
Sensor Network
S. Antony Alice Jeya Bharathi, Dr.K.Algarsamy
1) S. Antony Alice Jeya Bharathi , Department of
Computer Applications, Sardar Raja College of
Engineering, Alangulam, Tirunelveli, Tamilnadu,
India, 99521 98477
2) Dr.K.Alagarsamy, Department of Computer
Applications, School of Information Technology in
M.K University, Madurai, Tamilnadu, India, 99440
Abstract— Wireless Sensor Network (WSN), a dispersed
independent devices used to sense the physical or
environmental circumstances which comprises of collection of
discrete sensors for observing and classifying the collected
data. The data aggregation is a method of collecting the data
from sensor nodes and monitors the information obtained from
the sensors. Data aggregation minimizes the traffic in network
and reduces energy utilization on the sensor nodes. The
aggregated data provides energy competent to improve the
network lifetime and also minimizes the redundant
information. Common data aggregation methods utilized to
combine the information obtained from the sensor node and
combine the information using different aggregation
algorithms like LEACH (low energy adaptive clustering
hierarchy) and TAG (Tiny Aggregation) etc.
Query processing is used to extract the information from the
database and at the same time optimization process provides
the information in a fast and efficient manner to the user. The
query is provided using the set of commands that extort the
information from the database in an efficient manner. The
query is based on the database structure which is known as
database schema and subsequently filters are used by the
query processor. Moreover, query optimizations are the most
significant for the clients in order to access huge database
consisting of internet or client server process. In addition,
query optimization minimizes the system resources which are
necessary for query processing and eventually offered to
the user with accurate result. Finally, query optimizations are
presented with fast results to the users in order to access the
application for fast information processing to the user.
Index Terms— WSN, LEACH, TAG
Prediction based data aggregations, decrease unnecessary
data transmission and minimizes [4] energy utilization using
new prediction based data collection protocol for
coordinating the information sequence of the sensor node
and the sink node.
Many simple application of wireless sensor network
technology are used to observe the distant surrounding for
low occurrence in the data development. Aggregators nodes
are executed by end to end encrypted data aggregation
whereas decreases the involvement of invader to collect [8]
the secret data. The wireless sensor nodes are not necessary
to converse straightly with their adjacent high-power base
station for the confined peers. Here data aggregation is
discussed and query processing in sensor networks are
A. Data Aggregation
Data aggregations are used to reduce unnecessary data
communication and further to increase the duration of
wireless sensor networks with energy constrains. Data
aggregations are used to reduce the amount of data
communication by merging sensor nodes in networks. It is
used to secure [1] data using data aggregation protocol.
Rising fields of wireless sensor networks concentrates on
sensing, estimating and communicating into a single
insignificant device. The authority of wireless sensor
network lies between the capabilities to organize huge
amount of tiny nodes being collected and organize by itself.
Sensor data are collected from the sensor
Data aggregation algorithms are TAG,
LEACH etc.
Aggregated data
Manuscript received May 05, 2014.
Mrs.S.Antony Alice Jeya Bharathi, Currently she is working as Assistant
Professor in Sardar Raja College of Engineering, Tirunelveli, Tamilnadu.
Dr. K. Alagarsamy, Associate Professor in Department of Computer
Applications, School of Information Technology in M.K University, Madurai,
Sensor base station and PC interface
Fig 1.1.Data Aggregation Algorithm
An Efficient Data Aggregation and Query Optimization for Energy Efficiency in Wireless Sensor Network
Fig 1.1.represent the data aggregation algorithm
implementation steps to be derived in WSN.
In wireless sensor network data communication are
performed by the multi-hop approach in that every node are
transferred by the neighbor node to the nearby sink. Data
aggregation used to merge the information is always intend
with various sources and also reduce the amount of
broadcasting to save the energy consumptions
Data aggregations are always considered with the
reduction of the data transformation and save energy
consumption [5]. In data aggregation, SafeQ protocols are
used to prevent the process from malicious node and [17]
attackers are expanding data from both sensor collected
information and sink nodes. To protect privacy and security,
SafeQ utilize the new method to encode both information and
structure for data aggregations are obtained from end-to-end
encryption and achieves higher estimation cost on the sensor
nodes. Data aggregation are identify as an effective technique
to minimize energy utilization in WSN. Data aggregations
are typically considered into two types namely, Tree based
data aggregation and Grid based or in-network based data
aggregation. Fig 1.2 represents the classification types of
data aggregation.
Data Aggregation
Tree Based Data
Grid Based Data
B. Query processing and optimization
Fig 1.2.Types Data aggregation
In WSN efficient communication is achieved using data
aggregation and [16] query processing. Query processing
and optimization are essential method for the DBMS and are
used for timeframe method. Query processing refers to data
extraction from the database. The updating of data and
recovery can be achieved using several low level processes.
DBMS scheme concentrates on the low level process and
reduces the problem related to the user according to the
DBMS requirements. End to end data aggregations are used
to sense the exact process in the WSN in order to avoid the
[13] jamming in network. Three phases are involved in the
DBMS processing query consisting of Parsing and
Translation, Optimization and Evaluation. Parsing and
translation translate the query using the query processing
engine. The query optimization uses the policy for interior
data structures and conversion of it accordingly. Finally,
evaluations select the optimization engine and perform the
optimization in an efficient manner.
Wireless sensor networks uses sensor nodes to reduce
energy while transmitting data with the controlled
computational authority, limited reminiscence and battery
A. Aggregated Data in the Sensor Network
Secure data aggregations are based on protection process,
data privacy and reliability. Various data aggregation
algorithms [3] used under many protocols but not efficient
for the exact process in the networks. To conquer this process
distributed algorithm is used to minimize aggregation
latency with efficient performance. This algorithm utilized O
(K) time slots to process the aggregation work where K
denotes the relationship between the fastest and straight
connections in the network. The aggregation techniques are
categorized into two phases, namely hop-by-hop and
end-to-end encrypted data aggregation. The common
In tree based data aggregation, sensor nodes are classified
in the form of tree where aggregated data are executed at
intermediate nodes and data are transmitted to the root node.
The main features of the tree based networks are to perform
effective data aggregation tree with minimum energy
consumption. The grid-based data aggregations are also
known as in-network data aggregations used for allocating
data aggregators for sensors in secure regions. Efficient and
privacy-preserving aggregation systems (EPPA) are used for
the sensor node interaction and also to provide security. It is
utilized [6] in the sequence increases to formatting multi
dimensional information and encrypts the controlled data by
the homomorphic Paillier cryptosystem techniques.
B. Query Optimization
Query processing is the process of transmitting
high-level query into an accurate form and implemented in
low-level language. processor selects suitable process for the
query execution. Query optimizations are related to
relational database management systems for query
transmission. The advantages of query processing are,
Quick processing of query
Minimum cost per query
Best performance of the process
Effective procedures for the database engine
Take less memory
Two kinds of query optimization Logical optimizations are
used to create a series for relational database whereas
physical optimizations are used to establish the functions for
effectual query processing. Three stages are required for
query processing in the DBMS.
They are:
Parsing and translation
International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869, Volume-2, Issue-5, May 2014
Parsing and translation are accessed by the DBMS to
create query processing engine. Query optimizations are used
to transmit the query to the interior part of the data structures.
The final stage involved is the evaluation which is used to
create the optimization engine by the applicants and
performs the query processing
The most of the existence protocols, applications and
services for WSN provides data aggregation and query
processing but do not provide security. Consequently data
aggregation and query optimization should be provided in
addition to security, serve as the next line of defense for
enhanced system are indispensable for WSN with high
security necessities.
A. Data Aggregation in Wireless Sensor Network
In the network the sensors are energy controlled but it is
not suitable for the sensors as it transmits data straightly to
the network base station. Whenever data are collected from
large [2] sensor networks normally had a huge process in the
base station. To avoid this process data aggregation is
performed which merge the data collected from various
sensor nodes. By utilizing the data aggregation they extract
the exact information for the complete network.
It is also used to reduce the increased error. The gathered
information is processed by sensor to decrease
communication load before they are transferred to the BS or
Data Aggregations in [7] merge information from various
sources which minimizes the occurrence of during
communication. So, security is the important and necessary
aspect in the networks. Protected data aggregation protocols
provide proper security with the end to end encrypted data
aggregation methods which are based on the privacy
Aggregator nodes combine the information,
established from its child node for essential aggregation
utility. This collected information’s are resulted in huge level
of collected nodes or sink nodes. The detection algorithm
utilized outlier method for detection whereas the filtration
algorithm utilized the outlier sensor nodes for filtering.
The hierarchical data aggregation provides security
from attacks with high isolation in the network. Generally
the networks [9] are divided into the amount of clusters and
every cluster is organized by the aggregators which are
straightly associated with the links. They detect the set of
nodes continuously and communicate with the unique values
using verification keys. The aggregator combines and
encrypts the information with common secret key for the sink
nodes and these nodes are transferred to the secured sink
nodes. Furthermore, data aggregation approaches are
investigated using dispensation [10] technique. The energy
utilization of nodes and network effectiveness decreases the
amount of communication or packet transmission.
The major reason for data collection in WSN are to
achieve the data efficiency from the operating [11]
environment which results in minimizing the utilization of
data aggregation approaches. A data aggregation tree is
created to utilize the packet transmission which are related
with invalidate multicast construction. The major functions
of the data aggregation are [12] to decrease the energy
consumption by reducing the amount of data
communication. All the combined nodes gather the
information from their child nodes and estimate the
aggregation value, followed by the values promoted towards
the data sink node.
Security managements are highly required for the privacy
management where arises ambiguities and information
integrity is highly required to conserve the networks. DyDAP
are denoted as dynamic secure end-to-end data aggregation
for the security process in the network are considered in the
initial form of UML replica which includes significant
construction process for privacy preservation in WSNs.
In WSNs, the sensor nodes introduce fake data during
combining data and promoting data [14]. The fake data
detection approaches inserts fake data during data promotion
and does not permit any modification on the data
aggregation. However, data aggregations provide
authentication protocol DAA, to incorporate fake data
recognition with combined data and privacy.
To focus on the reliability of the combined information are
the significance process in the WSN. It includes various
methods [15] for securing the data integrity which are
resolved using hop by hop approach. The data aggregation
uses the hop by hop and end to end approaches for obtaining
information from sensor nodes. In that, hop by hop protocols
are used for reliable authentication for the sensor nodes
facilitated using the sink nodes.
B. Query Processing
Distributed Quad-Tree (DQT), used for distance
responsive for querying with the query cost for the
occurrence denoted by the distance “d” in the network. DQT
construction is local and does not require any
communication. Numerical formulations for the query
optimization [18] for the fake data recovery in the isolation
reduce the risk in the transparency processing. The isolation
risks are calculated as a data theoretic deviation among the
various client query allocation which are incorporated with
the entropy for the client sharing as a particular process. In
wireless sensor networks, many of the routing protocols are
used for energy consumptions and the information traffics
are unrestrained liberation constraints for the given data
[19]. They are dependent on the traditional ant-based
algorithm and an ant-based multi-QoS routing metric
(AntSensNet) for the effective information transmission. The
controlled optimization framework are used to [20] develop
the level of information centric storage and uncertainty in
WSNs. They required both unstructured networks and
structured networks for sequential query exploration and
hash query efficient.
In wireless sensor network a new process called
region-based query processing (REQUEST) are used to
maximize the set of nodes in the network. The region-based
queries are processed in the hierarchical aggregation
techniques which are organized by every region. The sensor
structures are investigated and focused on node hardware
requirements with limited energy constraints. Various
An Efficient Data Aggregation and Query Optimization for Energy Efficiency in Wireless Sensor Network
methods [22] are analyzed for data requirements in the
WSNs for real time storing data and query processing.
Top-k monitoring queries are valuable in various WSN
applications. A query is the kind of constant arrival [23]
which had a list of controlled nodes with maximum sensor
readings. To develop these queries, familiar approaches are
used to filter the entire sensor nodes to prevent from the
redundant communication of sensor readings. The novel
top-k monitoring techniques are known as Distributed
Adaptive Filter-based Monitoring which are used on the
distributed network.
In cooperative communications, the clustered WSNs [24]
are used to collect the relay data packets which are based on
dispersed space time block coding which accomplish
efficient analysis on the power utilization. In heterogeneous
wireless networks the energy utilization are used by
combined [25] packet size optimization. The body sensor
network (BSN) and a WiFi network techniques are used by
the data communication system. The BSN and the WiFi
network uses the packets for payload which are based on the
packet delivery ratios (PDR).
In WSN applications, executed only by the information
collection and [26] aggregation. The novel creations for the
data processing techniques are used in the WSN like data
storage, querying and difficult liberate applications. The
multi-objective optimization methods are used for self
categorizing, energy management, [27] deliberation of
application constraints, communication requirements and
energy preservation process.
The primary query optimization difficulties in WSNs [28]
are based on the BS location so that the information is
transferred to sensor node in effective energy process. The
information’s are sent straightly to the base station or
through relay nodes. Transmission protocols are used to
obtain the energy consumption for increasing the lifetime of
the network.
Energy consumptions are the major function in the WSN
which poses the transmit route along with the data
communications which are based upon the link layer, MAC
layer and routing layer [29] for the transmissions. They
shorten these layers by combining various processes using
the optimization techniques to reduce the network lifetime.
The positions of base station are the important issues to be
solved to increase the network lifetime for WSN. They had
some challenging [30] method in the multihop sensor
network to prevent certain troubles in the routing for the
coupled data. The approximation algorithm provides
mechanism for increasing the duration of the network. This
algorithm is also used to identify the finite element in the
base station position and used to decrease the cost parameter
with the efficient performance.
C. Data Aggregation and Query Optimization
Energy-efficient Trust dependent [31] data aggregation is a
wireless sensor network routing protocol that depends on the
ant colony optimization (ACO) algorithm.This technique
attain dependable and energy competent data aggregation
beside energy prediction. Energy-efficient Trust dependent
data aggregation utilizes model of practical reputation and
expectation to achieve dependability. The ACO added the
issue of energy with the ants having been penetrating the
most favorable path.
The rising accessibility of location aware movable devices
specified rise to a flood of location dependent services [32].
The illuminating accurate user locality to LBS may isolate
their individuality and violate their privacy.
The main framework is designed for unidentified query
dispensation in road networks. The location obfuscation
techniques that offered unidentified LBS way in to the users
and to facilitate competent query dispensation at the LBS
side. The wireless sensor network, data aggregation method
that decreases a huge amount of broadcast is the nearly all
sensible [33] technique. This system contains three main
drawbacks such as, it is intended for a multi appliance
The base station extorts application precise data from
aggregated cipher texts. Next, it moderates the collision of
cooperation attacks in particular application environments.
The Wireless sensor networks are networks of [34]
self-sufficient nodes used for observing an environment. The
Particle swarm optimization technique is an uncomplicated,
resourceful and computationally capable optimization
WSN is used for making possible a variety of monitoring
applications greater than an extensive environmental region.
One such query is the K Nearest Neighbor (KNN) query that
ease group of sensor data illustration dependent on specified
query locality and the number of illustration specified.
The Parallel Concentric-circle Itinerary based KNN [35]
query processing technique obtains dissimilar itineraries by
optimizing both query latency and energy expenditure.
Different types of data aggregation and query optimization
methods are analyzed and compared with the efficient data
communication in wireless sensor network. A. Analysis of
various techniques in Data aggregation
WSN had many transmission protocols to reduce the
redundant data but some of them are not efficient. To rectify
these troubles in the sensor network they used prediction
based data aggregation to minimize redundant data
communications and save the energy of the sensor nodes. A
new prediction based data collection protocols are utilized
with the double-queue method to coordinate with the
information sequences and also are processed with sensor
node and sink node to reduce the collective errors in the
networks. Data aggregation techniques are utilized to reduce
the energy consumption and increase the lifetime of the
network by decreasing the amount of packets transmission in
the network. The secure data aggregation increases the entire
energy utilization in the network. It utilized the detection
algorithm to identify the process and sends data to sensor
nodes. They offered huge outlier detection rate for the
distribution approach. MAC protocols are used for the data
validation and data reliability. The Dynamic Data
Aggregation Scheme is an end-to-end data aggregation with
isolation function and intended starting from a UML
representation that includes the most significant structure
blocks of a privacy aware WSN. The Possibility is the one of
using emerging nano technologies or mixture architectures
to concentrates the power limits.
International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869, Volume-2, Issue-5, May 2014
4.2. Query processing and Energy Efficiency in WSN
Efficient and privacy-preserving aggregation methods are
used for the efficient process in the data communications. It
is a growing series of the structure multi-dimensional
information and certain encryption techniques used in the
homomorphic Paillier cryptosystem approach. The main
drawbacks of the system of heuristic methodologies for
optimal steering of animatedly elected cluster head sensors,
through some multi-hop communication protocol. The
main framework is designed for unidentified query
dispensation in road networks. They are determined using
locality obfuscation method that make available unidentified
LBS way in to the users and allocate resourceful query
processing at the LBS elevation. The main drawback was
that the unidentified client could substantiate that the locality
attendant did not interfere with the innovative owner data.
The ant based routing protocol constructs a hierarchical
constitution on the system prior to decide appropriate
passageway to assemble various QoS necessities from
different kinds of traffic, thus maximizing system
exploitation, as improving its recital. The major problems of
this technique are mix of numerous sink nodes in addition to
node mobility.
CDAMA are the new data aggregation system
which encryption scheme. The base station extracts the data
from collective cipher texts. The client stores the record on
an un-trusted service provider Therefore; the user has to
protect their record through PH schemes because PH schemes
remain exploitable possessions than usual ciphers.
Energy-efficient cooperative communication are used for the
appropriate decipher established packets contribute in the
supportive broadcast.
Two-tiered sensor networks are utilized for the nodes
which provide the tier among the sink and sensor node for the
query processing but in the same way the attackers also
identify the sensor nodes. To overcome this problem they use
the SafeQ protocol to detect the attackers from the sink
nodes. In that the SafeQ protocols identify the bad storage
node in the network. SafeQ techniques are used to encode the
information and the sink node from the network
Several data aggregation and query processing techniques
are discussed in detail, where data aggregations are used as a
mechanism to solve storage related issues in wireless sensor
network. In order to find the efficient path for data
transmission many routing protocols are used in sensor
network. The information is processed between the sensor
and the sink node to optimize the utilization of resources
available in the network. At the same time to increase the
lifetime of the network, significant process involved in data
aggregations are focused to minimize the redundant node in
addition to the security aspect
being covered. As a result, secured data aggregations provide
security for the entire network.
Sensor networks became ubiquitous and the databases also
face with certain amount of difficulty to organize the data in
the network. The query processing in the sensor network is
provided with the in-network aggregation for every routing
layer. Many prototype used in the routing protocol are
designed for query processing to extract the data from the
database. The query processing is mainly focused on the
adaptive approach for specific framework designed for the
adaptive model. In order to provide query optimization
certain key aspects has to be recognized
Data aggregations are the major issues to be resolved to
increase the lifetime of the sensor network. In future work
data aggregations should be mainly concentrated to save the
power consumption for increasing the lifetime of the network
and provide reliable data transmit technique between source
and destination. We can execute the data aggregation with
minimum energy and security algorithm designed for data
aggregation in order to provide more secure and more energy
efficient mechanism.
An Efficient Data Aggregation and Query Optimization for Energy Efficiency in Wireless Sensor Network
Query processing techniques are analyzed and also
manage the sensor data during the failures. In future work
query processing can be applied within a Stochastic
Well-formed Petri Nets (SWN) to analyze various query
optimization process in the Sensor Network. SWN
techniques are used for the accurate results using modeling
process for the concurrency, organization and cooperation.
Query optimizations of cooperative communication
scheme have to be improved by optimizing multiple network
parameters for efficient arithmetical process. Also
cooperative communication method enlarges the multi hop
clustered network for optimal routing by separately selecting
the cluster heads in the network intended with the upper layer
routing techniques for cooperative communication using
multi-hop network.
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IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 5,
May 2010.
Mrs.S.Antony Alice Jeya Bharathi received the B.Sc
(Computer Science) from MS University, Tamilnadu,
India. She received the MCA from MS University,
Tamilnadu, India.
Currently she is working as Assistant Professor in Sardar
Raja College of Engineering, Tirunelveli, Tamilnadu.
Now she is doing her research in EFFICIENT DATA
SENSOR NETWORK in M.S University, Tirunelveli under the Department of
Computer Science .She is a member of ISTE.
Dr. K. Alagarsamy received MCA, M.Phil and Ph.D from M.K University,
Madurai. Currently he is working as Associate
Professor in Department of Computer Applications,
School of Information Technology in M.K University,
Madurai, Tamilnadu. He has published more than 50
papers in national and international conferences. His
areas of interest are software engineering and
computer network.