DATA AGGREGATION IN WIRELESS SENSOR

NETWORKS

A Seminar Report

Submitted by

P.PRAVEEN KUMAR

(09751A0581)

in partial fulfillment of the requirements for the award of the degreeOf

BACHELOR OF TECHNOLOGY

IN

COMPUTER SCIENCE AND ENGINEERING

At

SREENIVASA INSTITUTE OF TECHNOLOGY AND

MANAGEMENT STUDIES, CHITTOOR-517127DEC-2012

1

TABLE OF CONTENTS

3 Data Aggregation: An Overview 8

3.1 In-Network Aggregation

3.2 Tree-Based Approach

3.3 Cluster-Based Approach

3.4 Multi-path Approach

S NO. TITLE PAGE NO.

1 Introduction 5

2 Goal & Problem Definition in Data Aggregation 7

4 Query Processing 15

4.1 Query Models

4.2 Query Language in Tiny DB

4.3 Queries and Aggregates

5 Simulation and Experimental Analysis 20

6 Security Challenges 25

7 Advantages and Disadvantages 29

8 Future Scope &Applications 30

9 Conclusion 31

10 References 32

2

ABSTRACT

Sensor networks are collection of sensor nodes which co-operatively send sensed data to

base station. As sensor nodes are battery driven, an efficient utilization of power is essential in

order to use networks for long duration hence it is needed to reduce data traffic inside sensor

networks, reduce amount of data that need to send to base station. The main goal of data

aggregation algorithms is to gather and aggregate data in an energy efficient manner so that

network lifetime is enhanced. Wireless sensor networks (WSN) offer an increasingly Sensor

nodes need less power for processing as compared to transmitting data. It is preferable to do in

network processing inside network and reduce packet size. One such approach is data

aggregation which attractive method of data gathering in distributed system architectures and

dynamic access via wireless connectivity.

Wireless sensor networks have limited computational power and limited memory and battery

power, this leads to increased complexity for application developers and often results in

applications that are closely coupled with network protocols. In this paper, a data aggregation

framework on wireless sensor networks is presented. The framework works as a middleware for

aggregating data measured by a number of nodes within a network.

The aim of the proposed work is to compare the performance of TAG in terms of energy

efficiency in comparison with and without data aggregation in wireless sensor networks and to

assess the suitability of the protocol in an environment where resources are limited.

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1. INTRODUCTION

Sensor Networks:

The wireless sensor network is ad-hoc network. It consists small light weighted wireless nodes

called sensor nodes, deployed in physical or environmental condition. And it measured physical

parameters such as sound, pressure, temperature, and humidity. These sensor nodes deployed in

large or thousand numbers and collaborate to form an ad hoc network capable of reporting to

data collection sink (base station). Wireless sensor network have various applications like habitat

monitoring, building monitoring, health monitoring, military survivallance and target tracking.

However wireless sensor network is a resource constraint if we talk about energy, computation,

memory and limited communication capabilities. All sensor nodes in the wireless sensor network

are interact with each other or by intermediate sensor nodes.

With advance in technology, sensor networks composed of small and cost effective sensing

devices equipped with wireless radio transceiver for environment monitoring have become

feasible. The key advantage of using these small devices to monitor the environment is that it

does not require infrastructure such as electric mains for power supply and wired lines for

Internet connections to collect data, nor need human interaction while deploying. These sensor

nodes can monitor the environment by collecting information from their surroundings,and work

cooperatively to send the data to a base station, or sink, for analysis.

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Figure 1 Architecture of the Sensor network

A sensor nodes that generates data, based on its sensing mechanisms observation and transmit

sensed data packet to the base station (sink). This process basically direct transmission since the

base station may located very far away from sensor nodes needs. More energy to transmit data

over long distances so that a better technique is to have fewer nodes send data to the basestation.

These nodes called aggregator nodes and processes called data aggregation in wireless sensor

network.

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2. GOALS AND PROBLEM DEFINITION

GOAL:

The main goal of data aggregation algorithms is to gather and aggregate data in an energy

efficient manner so that network lifetime is enhanced. Wireless sensor networks (WSN) offer an

increasingly attractive method of data gathering in distributed system architectures and dynamic

access via wireless connectivity.

PROBLEM DEFINITION:

Data aggregation protocols aims at eliminating redundant data transmission and thus improve the

lifetime of energy constrained wireless sensor network. In wireless sensor network, data

transmission took place in multi-hop fashion where each node forwards its data to the neighbor

node which is nearer to sink. Since closely placed nodes may sense same data, above approach

cannot be considered as energy efficient. An improvement over the above approach would be

clustering where each node sends data to cluster-head (CH) and then cluster-head perform

aggregation on the received raw data and then send it to sink. Performing ag homogeneous

sensor network cluster-head will soon die out and again re-clustering has to be done which again

cause energy consumption.

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3. DATA AGGREGATION:AN OVERVIEW

Data aggregation is a process of aggregating the sensor data using aggregation approaches. The

general data aggregation algorithm works as shown in the below figure. The algorithm uses the

sensor data from the sensor node and then aggregates the data by using some aggregation

algorithms such as centralized approach, LEACH(low energy adaptive clustering

hierarchy),TAG(Tiny Aggregation) etc. This aggregated data is transfer to the sink node by

selecting the efficient path

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Fig:General architure of algorithm

3.1 In-Network Aggregation:

In-network aggregation is the global process of gathering and routing information through a

multi-hop network, processing data at intermediate nodes with the objective of reducing resource

consumption (in particular energy), thereby increasing network lifetime.

There are two approaches for in-network aggregation:

1) with size reduction

2) without size reduction.

With size reduction:

In-network aggregation with size reduction refers to the process of combining

and compressing the data packets received by a node from its neighbors in order to reduce the

packet length to be transmitted or forwarded towards sink

Without size reduction:

In-network aggregation without size reduction refers to the process merging data packets

received from different neighbors in to a single data packet but without processing the value of

data.

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3.2 Tree Based Approach:

The tree based approach is defining aggregation from constructing an aggregation tree. The form

of tree is minimum spanning tree, sink node consider as a root and source node consider as a

leaves. Information flowing of data start from leaves node up to root means sink(base

station).Disadvantage of this approach, as we know like wireless sensor network are not free

from failure .in case of data packet loss at any level of tree, the data will be lost not only for

single level but for whole related sub tree as well.This approach is suitable for designing optimal

aggregation techniques data centric protocol know as Tiny aggregation (TAG) approach.

Fig:Tree-based aggregation in wireless sensor networks

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The working of TAG is depending on two phases:

1) distributed phase

2) collection phase

Distributed Phase:

In distributedphase, in which aggregate queries are pushed down into the network.

Collection Phase:

A collectionphase, where the aggregate values are continually routed up from children to parents.

Recall that our query semantics partition time into epochs of duration ,and that we must produce

a single aggregate value (when not grouping) that combines the readings of all devices in the

network during that epoch.

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3.3 Cluster-Based Approach:

In energy-constrained sensor networks of large size, it is inefficient for sensors to transmit the

data directly to the sink In such scenarios, Cluster based approach is hierarchical approach. In

cluster-based approach, whole network is divided in to several clusters. Each cluster has a

cluster-head which is selected among cluster members. Cluster-heads do the role of aggregator

which aggregate data received from cluster members locally and then transmit the result to base

station (sink). Recently, several cluster-based network organization and data-aggregation

protocols have been proposed for the wireless sensor network.

Fig:Cluster Based sensor networks.Arrow indicates wireless communication links

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The cluster heads can communicate with the sink directly via long range transmissions or multi

hopping through other cluster heads.

K. Dasgupta in proposed a maximum lifetimedata aggregation (MLDA) algorithm which finds

datagathering schedule provided location of sensors node andbase-station, data packet size, and

energy of each sensornode. A data gathering schedule specifies how data packetare collected

from sensors node and transmitted to basestation for each round. A schedule can be thought of as

acollection of aggregation trees. They proposedheuristic-greedy clustering-based MLDA based

on MLDAalgorithm. In this they partitioned the network in to clusterand referred each cluster as

super-sensor. They thencompute maximum lifetime schedule for the super-sensorsand then use

this schedule to construct aggregation treesfor the sensors. W. Choi et present a two-

phaseclustering (TPC) scheme. Phase I of this scheme createsclusters with a cluster-head and

each node within thatcluster form a direct connects with cluster-head. Phase I thecluster-head

rotation is localized and is done based on the remaining energy level of the sensor nodes which

minimizetime variance of sensors and this lead to energy savingfrom unnecessary cluster-head

rotation. In phase II, eachnode within the cluster searches for a neighbor closer thancluster-head

which is called data relay point and setup up adata relay link.

Now the sensor nodes within a clustereither use direct link or data relay link to send their data

tocluster head which is an energy efficient scheme. The datarelay point aggregates data at

forwarding time to another data relay point or cluster head. In case of high networkdensity, TPC

phase II will setup unnecessary data relaylink between neighbors as closely deployed sensor

willsense same data and this lead to a waste of energy.

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3.4 Multi-Path Approach:

The drawback of tree based approach is the limited robustness of the system. To overcome this

drawback, a new approach was proposed by many researchers .in which sending partially

aggregated data to single parent node in aggregation tree, a node could send data over multiple

paths. In which each and every node can send data packets to its possibly multiple neighbor’s.

Hencedata packet flow from source node to the sink node along multiple path, lot of intermediate

node between source node to sink node so aggregation done in every intermediate node.

Using this approach we will make the system robust but some extra overhead. The example of

this approach like ring topology, where network is divided in to concentric circle with defining

level levels according to hop distance from sink.propose a new strategy have both issues : energy

efficiency and robustness. In which single path to connect each node to the base station it is

energy saving but high risk of link failure. But on the other head multipath approach would

require more nodes to participate with consequent waste of energy. Authors present a clever use

of multi-path only when there is loss of packet which is implemented by smart caching of data at

sensor nodes. Authors also argue that in many practical situation data may be gathered only from

a particular region, so they use a different approach that relies on a spanning tree and provides

alternative paths only when a malfunctioning is detected. Algorithm adopts a tree-based

approach for forwarding packets through the network. In the ideal situation when no failures

occur, this is certainly the best choice, as the minimum number of nodes is engaged in the

transmission phase. In the presence of link or node failures, the algorithm will discover

alternative paths, so as ensure the delivery of as many packets as possible within the time

constraints.

The problem with this approach is that it may cause the arising of hot spots and nodes along

preferred paths will consume their energy resources quickly, possibly causing disconnection in

the network.

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4. QUERY PROCESSING

4.1 Query Models:

COUGAR approach proposes a query layer tosupport aggregate queries. With the interface

provided, the clients can issue queries without knowing how the results are generated, processed

and returned by the sensor network to them. The query layer processes declarative queries and

generate a cost effective query plan. They follow a database approach to design a query interface

for sensor networks. The view of cost is different for sensor networks.

The major factor under consideration is the communication cost, involving the cost of routing the

queries and aggregating data over the sensor networks. TAG also proposes a query model for

supporting aggregate queries. TAG and COUGAR are tightly coupled with the underlying

aggregation schemes. Proposes a Query Agent that provides application independent query

interface and an API support to map the user specified queries to lower level semantics

corresponding to underlying routing and aggregating protocols. It supports different

communication models - anycast, unicast, multicast and broadcast. Query agent will support a

wide variety of routing and aggregation protocols selecting the best combination based on the

type of the query.

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4.2 Query Language in Tiny DB:

TinyDB’s query language is based on SQL, and we will refer to it as TinySQL. Query Language

in TinySQL supports selection, projection, determining sampling rate, group aggregation, user

defined aggregation, event trigger, lifetime query, setting storing point and simple join.

The query language of Antelope is called AQL and isused both to build and to query databases.

The typical way of using Antelope in a sensor device is first to define a database consisting of

one or more relations. Data can be modeled using the well-founded design principles for

relational databases, such as normalizing the data and planning for what kind of queries that the

system should be able to handle efficiently.

Table 1. Antelope database operations

Operation Purpose

INSERT Insert a tuple into a relation.

REMOVE Remove tuples matching a condition.

SELECT Select tuples and project attributes.

JOIN Join two relations on a condition.

CREATE RELATION Create an empty relation.

REMOVE RELATION Remove a relation and all its associated indexes.

CREATE ATTRIBUTE Add an attribute to a relation.

CREATE INDEX Create an attribute index.

REMOVE INDEX Remove an attribute index.

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1CREATE RELATION sensor;

2CREATE ATTRIBUTE id DOMAIN INT IN sensor;

3 CREATE ATTRIBUTE name DOMAIN STRING(20) IN sensor;

4 CREATE ATTRIBUTE position DOMAIN LONG IN sensor;

5 CREATE INDEX sensor.id TYPE INLINE;

6 CREATE INDEX sensor.positionTYPE MAXHEAP;

Example 4.1: A database in Antelope is created by issuing a series of catalog operations. We first

create the relation, then its attributes, and lastly its indexes.

AQL, Users define databases by using a set of catalog op-erations provided by the language. The

database can then be queried by using a set of relational operations. Several of these operations

share syntactic elements with SQL, but there is also a considerable difference caused by the

different goals of the languages. SQL targets a broad range of database systems, including high-

end systems with several orders of magnitude larger resources than sensor devices.

AQL, by contrast, is designed for systems with modest hardware resources. Hence, complex

SQL functionality such as procedural extensions, triggers, and transactions are first verifies that

all its attribute values pertain to the domain of the corresponding attribute. The abstract

representation in AQL is thereafter transformed into its physical representation, which is a

compact byte-array record of the attribute values. If the attribute is indexed, the Antelope calls

the abstract function for index insertion, which forwards the call to the actual index component

chosen for the attribute. In the final step, the tuple is passed to the storage abstraction,

which writes it to persistent storage.

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4.3 Queries and Aggregates:

The probable queries for the sensor networks can be categorized into:

1) Simple Queries:

These are non aggregate queries.

Eg. "SELECT temperature FROM sensor WHERE node = z". These are generally mapped into

broadcast or point to point queries.

2) Complex Queries:

They may contain sub queries.

Eg. "SELECT temperature FROM sensor WHERE room =(SELECT room WHERE floor =

’3’)".

3) Event Driven Queries:

These are the continuous query that returns the values periodically at specified time intervals.

Eg: “SELECT AVG (temperature) FROM sensor where node =z" .The Grammar of TinySQL

query language is as follows:

SELECT select-list [FROM sensors] WHERE predicate 294 [GROUP BY gb-list] [TRIGGER

ACTION command-name[(param)]] [EPOCH DURATION time]

Where, select−list is the attribute list of the unlimited virtual relational table, which can include

an aggregation function. Predicate is the query condition.gb−list is an attributes list.

command−name is a trigger operation. Param is the parameters of trigger. Time is the value of

time. TRIGGERACTION is the subordinate clause which defines the trigger. It determines the

operations executed when WHERE clause is satisfied. EPOCH DURATION defines the query

cycle. The meaning of the other clauses is the same as SQL.

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Following is an example of a TinyDB query.

SELECT nodeid, AVG(light), AVG(temp) FROM sensors WHERE AVG(light)=100 GROUP

BY nodeidEPOCH DURATION 5min

The meaning of the query is detecting nodeid per five minutes in which the average light is equal

to 100 and returning the nodeid and its average light and temperature. Currently, the functions of

TinyDB are very limited. Some functions supported by SQL are not supported by TinyDB.

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5. SIMULATION AND EXPERIMENTALANALYSIS

Simulation Tools:

We have plenty of simulation tools or simulators for simulating wireless networks. The

simulators which are most popular are TOSSIM, NS-2, OPNET, OMNet++,J Sim, GlomoSim,

and Qualnet and so on. TOSSIM is a discrete event simulator for TinyOS (TinyOS is a popular

sensor network operating system) sensor networks. Instead of compiling a TinyOS application

for a mote, users can compile it into the TOSSIM [20] framework, which runs on a PC. This

allows users to debug, test, and analyze algorithms in a controlled and repeatable environment.

As TOSSIM runs on a PC, users can examine their TinyOS code using debuggers and other

development tools. TOSSIM’s primary goal is to provide a high fidelity simulation of TinyOS

applications. For this reason, it focuses on simulating TinyOS and its execution, rather than

simulating real word.

Simulation run:

This simulation is run for the following with aggregation and clustering Query-1.

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QUERY-1: SELECT AVG (light) FROM SENSORS GROUP BY NODEID % 2 SAMPLE PERIOD 2048

Fig: Result window for with aggregation and clustering

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QUERY-2: SELECT MAX (temp), AVG (light) FROM SENSORS SAMPLE PERIOD 2048

Fig: Result window for with aggregation and without clustering

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QUERY-3: SELECT temp, light FROM SENSORS SAMPLE PERIOD 2048

Simulation results and comparison:

With aggregation query:

SELECT MAX (temp), AVG (light) FROM SENSORS SAMPLE PERIOD 2048

Without aggregation query:

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SELECT light FROM sensors SAMPLE PERIOD 2048

With aggregation and with clustering query:

SELECT AVG (light) FROM SENSORS GROUP BY NODEID %2 SAMPLE PERIOD

2048.

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6. SECURITY CHALLENGES

Data acquisition systems for sensor networks can be classified into two broad categories on the

basis of the data collection methodology employed for the application:

Query-based systems:

In query-based systems, the base station (the data sink) broadcasts a query to the network and the

nodes respond with the relevant information. Messages from individual nodes are potentially

aggregated enroute to the base station. Finally, the base station computes one or more aggregate

values based on the messages it has received.

In some applications, queries may be persistent in nature resulting in a continuous stream of data

being relayed to the data sink from the nodes in the network. For such applications, the query

broadcast by the base station specifies a period (referred to as an epoch); nodes in the network

send their readings to the base station after each epoch.

Event-based systems:

In event-based applications, such as perimeter surveillance and biological hazard detection,

nodes send a message to the base station station only when the target event occurs in the area of

interest. If multiple reports being relayed correspond to the same event, they can be combined by

an intermediate node on the route to the base station.

Data acquisition systems can also be categorized based on how sensor data is aggregated. In

single-aggregator approaches, aggregation is performed only at the data sink

In contrast, hierarchical aggregation approaches make use of in-network

aggregation.Hierarchical aggregation schemes can be further classified into tree-based schemes

and ring-based schemes on the basis of the topology into which nodes are organized.

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As discussed in the introduction, most existing data management and acquisition systems for

sensor networks are vulnerable to security attacks launched by malicious parties. Sensor nodes

are often deployed in unattended environments, so they are vulnerable to physical tampering.

Since current sensor nodes lack hardware support for tamper resistance, it is relatively easy for

an adversary to compromise a node without being detected.

The adversary can obtain confidential information (e.g., cryptographic keys) from the

compromised sensor and reprogram it with malicious code. Moreover, the attacker can replicate

the compromised node and deploy the replicas at various strategic locations in the network

A compromised node can be used to launch a variety of security attacks. These attacks include

jamming at the physical or link layer as well as other resource consumption attacks at higher

layers of the network software. Compromised nodes can also be used to disrupt routing protocols

and topology maintenance protocols that are critical to the operation of the network .

In this topic we focus on attacks that targetthe data acquisition protocol used by the application.

Specifically, we discuss attacks in which the compromised nodes send malicious data in response

to a query (or send false event reports in event-based systems). By using a few compromised

nodes to render suspect the data collected at the sink, an adversary can effectively compromise

the integrity and trustworthiness of the entire sensor network.

\

In event-based systems, compromised nodes can be used to send false event reports to the base

station with goal of raising false alarms and depleting the energy resources of the nodes in the

network. We refer to this attack as the false data injection attack. we discuss an approach for

filtering the false data injected by malicious nodes

.

Similarly, in query-based systems, compromised nodes can be used to inject false data into the

network with the goal of introducing a large error in the aggregate value computed at the data

sink. The aggregate computed by the sink is erroneous in the sense that it differs from the true

25

value that would have been computed if there were no false data values included in the

computation. Unlike event-based systems, however, in aggregation systems the effectiveness of a

false data injection attack depends on both the aggregate being computed, e.g., MAX or

MEDIAN, and whether sensor data is aggregated enroute to the data sink or only at the data sink,

and thus the techniques used for preventing this attack differ from the techniques used in event-

based systems.

In an aggregation system, a compromised node M can corrupt the aggregate value computed at

the sink in four ways. First, M can simply drop aggregation messages that it is supposed to relay

towards the sink. This has the effect of omitting a large fraction of sensor readings being

aggregated. Second, M can alter a message that it is relaying to the data sink. Third, M can

falsify its own sensor reading with the goal of influencing the aggregate value. Fourth, in

systems that use in-network aggregation, M can falsify the sub-aggregate which it is supposed to

compute based on the messages received from its child nodes.

The first attack in which a compromised node intentionally drops aggregation messages can

substantially deviate the final estimate of the aggregate if tree-based aggregation algorithms are

used. The deviation will be large if the compromised node is located near the root of the

aggregation hierarchy because a large fraction of sensor readings will be omitted from being

aggregated. Countermeasures against this attack include the useof multi-path routing and ring-

based topologies as well as the use of probabilistic techniques in the formation of aggregation

hierarchies

To prevent the second attack in which a compromised node alters a message being relayed to the

sink, it is necessary for each message to include a message authentication code (MAC) generated

using a key shared exclusively between the originating node and the sink. This MAC enables the

sink to check the integrity of a message, and filter out messages that have been altered. Hence,

the effect of altering a message is no different from dropping it, and countermeasures such as

multi-path routing are needed to mitigate the effect of this attack.

26

We refer to the third attack in which a sensor intentionally falsifies its own readingas the falsified

local value attack. This attack is similar to the behavior of nodes with faulty sensors, and also to

the false data injection attack in event-based systems. Potential countermeasures to this attack

include approaches used for fault tolerance such as majority voting and reputation-based

frameworks. we discuss how aggregation schemes can be designed to be resilient to this attack.

The three attacks discussed above apply to both single-aggregator and hierarchical aggregation

systems, whereas the fourth attack applies only to hierarchical aggregation systems. This attack

in which a node falsifies the aggregate value it is relaying to its parent(s) in the hierarchy is much

more difficult to address. We refer to this attack as the falsified sub-aggregate attack. we discuss

two approaches that have been proposed for resilient hierarchical aggregation that include

countermeasures for this attack.

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8. ADVANTAGES AND DISADVANTAGES

Advantages:

1)With the help of data aggregation process we can enhance the robustness and accuracy of

information which is obtained by entire network, certain redundancy exists in the data collected

from sensor nodes thus data fusion processing is needed to reduce the redundant

information.

2) Another advantage is those reduces the traffic load and conserve energy of the sensors.

Disadvantages:

1) Thecluster head means data aggregator nodes send fuse these data to the base station .this

cluster head or aggregator node may be attacked by malicious attacker.

2) If a cluster head is compromised, then the base station (sink) cannot be ensure the correctness

of the aggregate data that has been send to it.

3) Another drawback is existing systems are severalcopies of the aggregate result may be sent to

the base station (sink) by uncompromised nodes .It increase the power consumed at these nodes.

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9. FUTURE SCOPE

Future work will focuses on the using new different routing algorithms for routing the data from

the source to the sink.

Our approach should confront with the difficulties of topology construction, data routing, loss

tolerance by including several optimization techniques that further decrease message costs

andimprove tolerance to failure and loss.

In addition to implementing these techniques, we need to rethink some of these techniques to

present more efficiency to network changes and external factors which could affect our approach

such as node mobility, obstacles and other issues.

In addition as future work, we could also extend our simulator to incorporate a 3D tree

construction technique.

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10. CONCLUSION

In this work we have studied the two most important parts ofdata communication in sensor

networks- query processing, data aggregation and realized how communication in sensor

networks is different from other wireless networks. Wireless sensor networks are energy

constrained network. Since most of the energy consumed for transmitting and receiving data, the

process of data aggregation becomes an important issue and optimization is needed. Efficient

data aggregations not only provide energy conservation but also remove redundancy data

and hence provide useful data only.

The simulation result shows that when the data from source node is send to sink through

neighbors nodes in a multihopfashion by reducing transmission and receiving power, the energy

consumption is low as compared to that of sending data directly to sink that is aggregation

reduces the data transmission then the without aggregation. We have showed how aggregate

queries are efficiently executed in wireless sensor networks.

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