data aggregation to extend life of wsn

i

DATA AGGREGATION TO EXTEND

LIFE OF WIRELESS SENSOR

NETWORK

By

Rathod Gaurang Dhirubhai

Guided by

Prof. Niteen Patel

(Associate Professor)

A Thesis submitted to

Gujarat Technological University

In Partial Fulfillment of Requirement for

The Degree of Master of Engineering

In Electronics and communication

MAY 2014

Department of Electronics & Communication Engineering

Sarvajanik College of Engineering & Technology

Dr. R.K. Desai Marg,

Athwalines, Surat - 395001, India.

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Prof. Niteen Patel

Associate Professor

Electronics & Communication Department

Sarvajanik College of Engineering &

Technology, Surat

Dr. Vaishali Mungurwadi

Principal,

Faculty of Engineering


Technology, Surat

Certificate

This is to certify that research work embodied in this thesis entitled Data

Aggregation to Extend Life of Wireless Sensor Network was carried out by Mr.

Gaurang Dhirubhai Rathod (120420704005) at Sarvajanik College of Engineering

and Technology (042) for partial fulfillment of M.E. degree to be awarded by Gujarat

Technological University. This research work has been carried out under my supervision

and is to the satisfaction of department.

Date:

Place: Sarvajanik College of Engineering & Technology, Surat

Seal of Institute

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Signature of Guide:

Name of Guide: Prof. Niteen Patel

Institute Code: 042

Dr. Vaishali Mungurwadi

Principal, Faculty of Engineering


Technology, Surat

Compliance Certificate

This is certify that research work embodied in this thesis entitled Data Aggregation to

Extend Life of Wireless Sensor Network, was carried out by Mr. Gaurang

Dhirubhai Rathod (120420704005) studying at Sarvajanik College of Engineering

and Technology(042) for partial fulfillment of M.E. degree to be awarded by Gujarat

Technological University. He has complied with the comments given by the Dissertation

phase I as well as Mid Semester Thesis Reviewer to my satisfaction.

Date:

Place: Sarvajanik College of Engineering and Technology, Surat

Signature of Student:

Name of Student: Rathod Gaurang

Enrollment No: 120420704005

Seal of Institute

iv

Thesis Approval Certificate

This is to certify that research work embodied in this entitled Data Aggregation to

Extend Life of Wireless Sensor Network was carried out by Mr. Gaurang Rathod

(120420704005) at Sarvajanik College of Engineering & Technology is approved for

the degree of Master of Engineering in Electronics & Communications by Gujarat

Technological University.

Date

Place:

Examiners Sign and Name:

.. ..

( ) ( )

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Declaration of Originality

We hereby certify that we are the sole authors of this thesis and that neither any part of

this thesis nor the whole of the thesis has been submitted for a degree to any other

University or Institution.

We certify that, to the best of our knowledge, the current thesis does not infringe upon

anyones copyright nor violate any proprietary rights and that any ideas, techniques,

quotations or any other material from the work of other people included in our thesis,

published or otherwise, are fully acknowledged in accordance with the standard

referencing practices. Furthermore, to the extent that we have included copyrighted

material that surpasses the boundary of fair dealing within the meaning of the Indian

Copyright (Amendment) Act 2012, we certify that we have obtained a written permission

from the copyright owner(s) to include such material(s) in the current thesis and have

included copies of such copyright clearances to our appendix.

We declare that this is a true copy of thesis, including any final revisions, as approved by

thesis review committee.

We have checked write up of the present thesis using anti-plagiarism database and it is in

allowable limit. Even though later on in case of any complaint pertaining of plagiarism,

we are responsible for the same and we understand that as per UGC norms, University

can even revoke Master of Engineering degree conferred to the student submitting this

thesis.

Date:

Place: Sarvajanik College of Engineering and Technology, Surat

Signature of Student :

Name of Student : Rathod Gaurang

Enrollment No : 120420704005

Signature of Guide :

Name of Guide : Prof. Niteen Patel

Institute Code : 042

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Acknowledgement

First and foremost, I would like to express my sincere gratitude to my guide, Prof. Niteen

Patel, Head of Electronics and Communication Department for immense help, guidance,

stimulating suggestion, and encouragement all the time with this thesis work. He always

provides a motivating and enthusiastic atmosphere to work; it is a great pleasure to do thesis

under his supervision.

I am equally grateful to Pritesh Saxena, Assistant Professor of Electronics and

Communication Department for helping me in sorting out the procedural work and his

precious guidance.

I also thank my friends and colleagues who provided help and valuable suggestion. And last

but not the least I wish to thank my parents for their encouragement and moral support.

Rathod Gaurang

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Table of Contents

Certificate ........................................................................................................................... ii

Compliance Certificate .................................................................................................... iii

Thesis Approval Certificate ............................................................................................. iv

Declaration of Originality .................................................................................................. v

Acknowledgement ............................................................................................................. vi

Table of Contents ............................................................................................................. vii

List of Figures ..................................................................................................................... x

List of Tables ..................................................................................................................... xi

Abstract ............................................................................................................................. xii

1. Introduction ................................................................................................................ 1

1.1 Introduction .............................................................................................................. 1

1.2 Motivation ................................................................................................................ 1

1.3 Objective .................................................................................................................. 2

1.4 Thesis Organization ................................................................................................. 2

2. Introduction of Wireless Sensor Network .................................................................. 3

2.1 Introduction ............................................................................................................. 3

2.2 Sensor Node ............................................................................................................. 5

2.3 Challenges in the End Device (Node) ...................................................................... 6

2.3.1 Limited Memory .............................................................................................. 6

2.3.2 Limited Energy Resource ................................................................................. 6

2.3.3 Limited CPU Performance ............................................................................... 6

2.3.4 Tamper-Resistant Hardware............................................................................. 7

viii

2.4 Challenges in the Network ....................................................................................... 7

2.4.1 Hostile & Remote Environment ...................................................................... 7

2.4.2 Random Topology ........................................................................................... 7

2.4.3 Latency ............................................................................................................. 7

2.5 Wireless Sensor Network Application ..................................................................... 8

2.5.1 Event Detection ................................................................................................ 8

2.5.2 Periodic Reporting ........................................................................................... 8

2.5.3 Base Station Querying ..................................................................................... 8

2.5.4 Tracking ........................................................................................................... 8

3. Data Aggregation: An Overview ............................................................................... 9

3.1 Data Aggregation ..................................................................................................... 9

3.2 Factors Affected by Data Aggregation .................................................................. 11

3.3 Data Aggregation Techniques ................................................................................ 12

3.3.1 Flat Networks ................................................................................................. 13

3.3.2 Hierarchical Networks ................................................................................... 13

3.3.3 Structure Free Network .................................................................................. 19

3.4 Comparison between Hierarchical Networks and Flat network ............................ 19

3.5 Advantages and Disadvantages of Data Aggregation in WSN .............................. 20

4. Data Aggregation by Precision Allocation ............................................................. 21

4.1 Introduction of Aggregation for Continuous Data Measuring ............................... 21

4.2 Aggregation and Lifetime of Node ........................................................................ 22

4.3 Basics of Error Bound for Nodes ........................................................................... 22

4.4 Types of Aggregation Based on Precision Allocation ........................................... 23

4.5 Error Bound and Communication between Node and Sink ................................... 24

4.6 Optimal Precision Allocation ................................................................................. 24

4.7 Candidate Based Precision Allocation ................................................................... 25

4.8 Algorithm for Optimal Precision Allocation ......................................................... 26

ix

4.9 Adaptive Precision Allocation ............................................................................... 26

5. Experimental Work A ............................................................................................. 29

5.1 Nodes Data (temperature) Generation .................................................................. 29

5.2 Error Bound of Node ............................................................................................. 29

5.3 Adjustment in Error Bound of Node ...................................................................... 30

5.4 Assumptions and Simulation Environment ........................................................... 31

5.4.1 Error Bounds of Nodes .................................................................................. 32

5.4.2 Error Bound Changing Parameters ................................................................ 33

5.4.3 Residual Energy and Communication Frequency of Node............................ 34

5.5 Network with Multiple Monitoring Sensors .......................................................... 35

6. Experimental Work B.............................................................................................. 37

6.1 Simulation Environment ........................................................................................ 37

6.2 Steps for Creating Script in NS2 ............................................................................ 37

6.3 Simulation Parameters and Assumptions............................................................... 37

6.4 Description of Simulation ...................................................................................... 38

6.4.1 Same Error Bound Case ................................................................................. 39

6.4.2 Random Different Error Bound Case ............................................................ 42

6.4.3 Error Bound Based on Node Position (Energy) Case .................................... 43

6.4.4 Packet to Delivery Ratio ................................................................................ 45

Conclusion ........................................................................................................................ 46

References ......................................................................................................................... 47

Appendix: A Review Card ........................................................................................... 49

Appendix: B-Compliance Report of Review Card ....................................................... 50

x

List of Figures

Figure 2-1 A Typical Wireless Sensor Network Architecture [1]

........................................ 3

Figure 2-2 Architecture of Sensor Node [1]

.......................................................................... 5

Figure 3-1 Without Data Aggregation With Six Transmission [3]

..................................... 10

Figure 3-2 With Data Aggregation With Four Transmission [3]

........................................ 10

Figure 3-3 Effect of Data aggregation [3]

........................................................................... 11

Figure 3-4 Taxonomy of Data Aggregation [3]

.................................................................. 13

Figure 3-5 Cluster Based Sensor Network [4]

.................................................................... 14

Figure 3-6 Chain Based Sensor Network [4]

...................................................................... 15

Figure 3-7 Minimum Spanning Tree Based Routing [4]

.................................................... 16

Figure 3-8 Grid Based Data Aggregation [4]

...................................................................... 17

Figure 3-9 In-network Data Aggregation Scheme [4]

......................................................... 18

Figure 5-1 Temperature Data Profile ................................................................................. 29

Figure 5-2 Network Topology ........................................................................................... 31

Figure 5-3 Initial Error Bound for Node ............................................................................ 32

Figure 5-4 Error Bound of Node at Simulation End .......................................................... 32

Figure 5-5 Highest Residual Energy Node at Every Adjustment Period End ................... 33

Figure 5-6 Lowest Residual Energy Node at Every Adjustment Period End .................... 33

Figure 5-7 Value of Delta at Every Adjustment Period End ............................................. 34

Figure 5-8 No. of Times Node Send Data to Base Station ................................................ 35

Figure 5-9 Residual Energy of Node at Simulation End ................................................... 35

Figure 6-1 Simulation Topology ........................................................................................ 39

Figure 6-2 Hand Shaking Packets Transmit by Nodes ...................................................... 40

Figure 6-3 Residual Energy of all Node vs Time .............................................................. 40

Figure 6-4 Residual Energy of all Nodes vs Time ............................................................. 42

Figure 6-5 Residual Energy of all Nodes vs Time ............................................................. 44

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List of Tables

Table 3-1 Comparison between Hierarchical and Flat Networks ...................................... 19

Table 5-1 Network Parameters .......................................................................................... 31

Table 6-1 Network Parameters ........................................................................................... 38

Table 6-2 Remaining Energy of Node at Simulation End ................................................. 41



xii

DATA AGGREGATION TO EXTEND LIFE OF WSN

Submitted By

Rathod Gaurang Dhirubhai

Supervised By

Prof. Niteen Patel

Head of Department,

Electronics and Communication Engineering,

Sarvajanik College of Engineering and Technology,

Surat -395001, India.

Abstract

The fast advancement of hardware technology has enabled the development of tiny and

powerful sensor nodes, which are capable of sensing, computation and wireless

communication. This revolutionizes the deployment of wireless sensor network for

monitoring some area and collecting regarding information. However, limited energy

constraint presents a major challenge such vision to become reality. Data communication

between nodes consumes a large portion of the total energy consumption of the WSNs.

Consequently, Wireless sensor nodes are very small in size and have limited processing

capability with very low battery power. This restriction of low battery power makes the

sensor network prone to failure.

Data aggregation may be effective technique because it reduces the number of packets to

be sent to sink by aggregating the similar packets. Data aggregation has been put forward

as an essential technique to achieve power efficiency in sensor networks. The main goal

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

network lifetime is enhanced.

The data aggregation technique of precision allocation helps to balance the energy

consumption of network. By optimum precision allocation given to node, helps to control

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the frequency of communication between node and base station. This way, effectively it

reduces less communication between sources and sink, which helps to reduce the energy

consumption.

In experiment work, assigning same precision, random precision and precision based on

distance and residual energy of node to all nodes in network and summarize energy

consumption of node. By periodically adjusting the precision of node extend the life time

of network compared to without aggregation and random precision allocation method.

This technique suits to problem of continues data measuring, like temperature, humidity,

water level, etc.

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1. Introduction

1.1 Introduction

A wireless sensor network is a collection of wireless sensor nodes having limited resource

constrain and that may be mobile or stationary. Sensor nodes are located on a

dynamically changing environment. WSNs inherit many characteristics/features of

wireless ad hoc networks such as the ability for infrastructure-less setup, ability of the

nodes to self-organize and self-configure without the involvement of a centralized

network manager. These features help to set up WSNs in situations where there is no

existing network setup or fixed infrastructure network. On the other hand small size, low

power and the ability of wireless communication makes WSNs the ideal solution for

numerous applications such as remote environmental monitoring, medical healthcare

monitoring, agriculture monitoring, military surveillance, etc.

1.2 Motivation

Wireless sensor networks are expected to find wide applicability and increasing

deployment in the near future. WSN have tremendous potential because they expand

human ability to monitor and interact remotely with the physical world. This is the

upcoming field and because of its benefits, it is recent area of research. Wireless sensor

network is consists of thousands of densely deployed sensor nodes which can be used for

a number of applications. Sensor nodes are tiny devices which are composed of a sensing

unit, a radio, a processor & a limited battery power. A network of thousands of sensor

nodes could be setup for many applications such as environmental monitoring, health

monitoring, disaster management, industrial areas, military application and many more.

As world is growing up, human have some requirements to measure/monitor some of the

parameters in the remote area. At this movement WSN helps a lot to human being.

In Sensor Network, the energy is mainly consumed for three purposes: data transmission,

signal processing, and hardware operation. It is said that 70% of energy consumption is

due to data transmission. So for maximizing the network lifetime, the process of data

transmission should be optimized. The data transmission can be optimized by using

efficient routing protocols and effective ways of data aggregation.

2

Routing protocols have their own ways to save energy of nodes in the network by

providing or creating an optimal route from sensor nodes to base station or sink. Data

aggregation plays an important role in energy conservation of sensor network. Data

aggregation methods are used not only for finding an optimal path from source to

destination but also to eliminate the redundancy of data. Also multiple sensors may see

the same phenomenon, from different view and if this data can be reconciled into a more

meaningful form as it passes through the network, it becomes more useful to an

application. One more benefit of data aggregation is that if data is processed as it is

passed through the network, it may be compressed thus occupying less bandwidth. This

also reduces the amount of transmission power expended by nodes. Hence Data

aggregation can be considered as a very challenging problem in wireless sensor network.

1.3 Objective

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. By data aggregation mechanism, we can save

energy of network and this way extend the life time of sensor network.

By precision allocation, the energy consumption of node can be balanced. Energy of node

can be saved by assigning different precision to each node. And by that precision, we

control the frequency of communication between node and sink and reduce data

transmission. And this way save the energy of node and extend the life time of network.

1.4 Thesis Organization

With the end of chapter 1, the whole thesis is organized as follows: Chapter 2 gives a

detailed study of wireless sensor network. Chapter 3 gives a detailed overview of data

aggregation. This chapter also presents the literature survey that had been done. Chapter 4

introduces and describes the precision allocation method for data aggregation. Chapter 5

presents the performance analysis of the experiment work which is done in MATLAB.

This chapter gives idea about error bound and how energy of network is balanced.

Chapter 6 explains experiment work done in NS2 tool. This chapter is more elaborates on

energy of nodes. It also provides the comparison results.

3

2. Introduction of Wireless Sensor Network

2.1 Introduction

The recent advances and the convergence of micro electro-mechanical systems

technology, integrated circuit technologies, microprocessor hardware and

nanotechnology, wireless communications, Ad-hoc networking routing protocols,

distributed signal processing, and embedded systems have made the concept of Wireless

Sensor Networks (WSNs). Sensor network nodes are limited with respect to energy

supply, restricted computational capacity and communication bandwidth. WSN [1]

are

usually infrastructure less networks that rely on each sensor to function as part of the

network.

Figure 2-1 A Typical Wireless Sensor Network Architecture [1]

Advances in microelectronics have enabled the development of exceptionally tiny sensor

nodes that have the ability of measuring ambient conditions such as temperature,

pressure, humidity, light intensity, and motion. The sensed data can then be transmitted

through an on-board radio transmitter to a single or multiple base stations (BSs) where it

can be further processed. The tiny size and inexpensive cost of such emerging sensor

nodes has encouraged practitioners to explore using them collaboratively in a network

formed in ad-hoc manner. Such networked sensor system not only is cost -effective but

4

also can provide fast and accurate information gathering in remote and risky areas. Figure

2.1 depicts typical Wireless Sensor Network architecture. The BS acts as a gateway for

linking the sensors to multiple command nodes.

The basic goals [1]

of a WSN are to

Determine the value of physical variables at a given location.

Detect the occurrence of events of interest, and estimate parameters of the detected

event or events

Classify a detected object

Track an object.

WSNs will be beneficial to detect the pre-cursors of these disasters, early warn the

population, evacuate them, and save their life. However, these disasters are largely

unpredictable and occur within very short spans of time. Therefore technology has to be

developed to capture relevant signals with minimum monitoring delay. Wireless sensors

are one of the cutting edge technologies that can quickly respond to rapid changes of data

and send the sensed data to a data analysis centre in areas where cabling is

inappropriate [3]

.

The Important Requirements of a WSN are

Use of a large number of sensors

Attachment of stationary sensors

Low energy consumption.

.Self organization capability

Collaborative signal processing.

Querying ability

Less delay

5

2.2 Sensor Node

The architecture of sensor node is shown in Figure 2.2.

Figure 2-2 Architecture of Sensor Node [1]

The end device in WSN (sensor node) is composed of four basic units

1. Sensing Unit

It consists of an array of sensors that can measure the physical characteristics of its

environment, like temperature, light, vibration, and others. Each sensor has the ability to

sense environmental characteristics via the sensing unit and then use the Analog to

Digital Converter (ADC) to convert the sensed analog data into digital.

2. Processing Unit

It is, in most cases, composed of an internal memory to store data and application

programs, and a microcontroller to process the data. The microcontroller can be

considered as a highly constrained computer that contains the memory and interfaces

required to create simple applications. This unit should be able to work with a limited

resource of energy and process efficiently the digital data delivered by the sensing unit.

3. Power Unit

It provides the energy required by all the sensor components, and such energy may come

from either a battery or from renewable sources.

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4. Transceiver Unit

It is able to send and receive messages through a wireless channel. In other words, it gives

the sensor the ability to talk to other sensor nodes and form an Ad Hoc Network.

2.3 Challenges in the End Device (Node)

All security approaches require a certain amount of resources for the implementation,

including data memory, code space, and energy to power the sensor during the run of the

approach. However, currently these resources are very limited in a tiny wireless sensor

node. The hardware specifications for three types of sensor node, namely MICA2 [16]

,

FLECK [16]

, and MICAZ [16]

and highlights the resource constraints in the end device of

WSNs The challenges in the sensors hardware are discussed as follows:

2.3.1 Limited Memory

A sensor node is a tiny device with only a small amount of memory and storage space for

the code. In order to build an effective security mechanism, it is necessary to limit the

code size of the security algorithm. For example, one common sensor type (MICA2) has

4K RAM, 128K program memory, and 512K flash storage.

2.3.2 Limited Energy Resource

The energy resource is the biggest challenge in WSNs. It is assumed that once sensor

nodes are deployed in a WSN, their batteries cannot be easily replaced due to the high

operating costs of being deployed in remote areas. Some current versions of sensor nodes

such as MICA2 are powered by 2AA batteries. Therefore, the battery charge taken with

them to the field must be conserved to prolong the life of the individual sensor node and

the entire sensor network. For example, when implementing a cryptographic function or

protocol in a sensor node, the energy impact of the proposed solution should be

considered.

2.3.3 Limited CPU Performance

The CPU used in MICA2 sensors, for example, is the16 bit, 8 MHz Texas Instruments

MSP430 microcontrollers [16]

. Embedded processors are generally not as powerful as

7

those in nodes of a wired network. As such, complex cryptographic algorithms should be

avoided in WSNs.

2.3.4 Tamper-Resistant Hardware

The most obvious tamper-resistance strategies are hardware-based ones, which involve

extra cost to implement special complex hardware circuits in the electronic device. To run

these circuits, extra energy should be ensured. Due to the targeted low cost and the

limited power resource existing in sensor nodes, the hardware-based tamper protection

solutions are very limited.

2.4 Challenges in the Network

Sensor nodes are usually scattered randomly in the field to perform certain tasks. There is

usually no infrastructure support for sensor networks. Sensor nodes self-organize to

form a network. However, some network challenges exist. These challenges are discussed

as follows:

2.4.1 Hostile & Remote Environment

Depending on the function of a particular sensor network, the sensor nodes may be left

unattended for long periods of time. Most WSNs are deployed in remote or hostile

environments such as battlefields. Therefore, sensor nodes without tamper-resistant

hardware cannot be protected from physical attacks since the deployment area accessible

to anyone. An adversary could capture a sensor node or even introduce his own malicious

nodes inside the network.

2.4.2 Random Topology

WSN is often deployed in random distribution since it is mostly used in remote or hostile

environments. Consequently, there is no chance to know its topology beforehand. Also,

the topology after the deployment keeps changing because some sensors disappear due to

drained resources, or for instance by being damaged, or faulty.

2.4.3 Latency

The communication range of most sensor nodes is limited in order to conserve energy.

The MICA2, FLECK, and MICAZ sensor nodes have radio coverage area up to

8

152m, 500m, and 75m, respectively. To move a packet from one end of the network to

another, a multi-hop routing approach is needed. So it consume time for transmission

from node to sink.

2.5 Wireless Sensor Network Application

WSN applications [2]

are classified into four classes:

2.5.1 Event Detection

The objective of sensor networks in this application class is to detect rare events, such as

forest fires or intrusions, and to promptly communicate a report of such as event the sink.

2.5.2 Periodic Reporting

The objective of the sensor networks in this type of application is to send periodic

updates to the sink. Thus, there is regularity in terms of data gathering phases, and

there is a steady flow of data from the sensor nodes to the sink. In-network Data

Aggregation is useful in such applications because measurement of neighboring nodes

are likely to be correlated, and could be used to reduce the amount of data that needs to

be communicated to the sink. This in turn reduces communication energy expenditure of

the nodes, and prolongs the lifetime of the network.

2.5.3 Base Station Querying

In several application classes, the sink is not interested in data updates from all the nodes

in the network. The sink may want updates from different regions at different times.

Thus, requiring all the nodes to send their data to the sink at all the times increases the

energy consumption on communication as well as on computation. In such cases, the

sink selectively queries a set of sensor nodes located in the region of interest.

2.5.4 Tracking

Tracking applications are interested in detecting, localizing and tracking targets, and

conveying the relevant information to the sink, in a timely fashion. They combine some

of the characteristics of the three application classes discussed earlier. Landslide detection

system using a WSN is the first in India, one of the first in the world of its kind. It is

also one of the first landslide field deployments backed up by laboratory setup.

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3. Data Aggregation: An Overview

3.1 Data Aggregation

Data Aggregation is the good technique to save energy of sensor nodes. Usually in a

sensor network thousand of sensor nodes are deployed for area monitoring. Most of them

sense the environment parameters and send the data to the base station. Base station

combines all the information for the desired output. If data aggregate before reaching the

base station the potentially decrease the number of packets in the network so less number

of packet send to base station and that can save the energy of sensor nodes. In typical

wireless sensor networks, sensor nodes are usually resource-constrained and battery-

limited. In order to save resources and energy, data must be aggregated to avoid

overwhelming amounts of traffic in the network. Data aggregation [3]

is the process of one

or several sensors then collects the detection result from other sensor. The collected data

must be processed by sensor to reduce transmission.

Wireless sensor networks (WSNs) consist of several sensor nodes and one or more base

station (BS) or sink. Sensor nodes have limited processing capability and low power

battery. The wireless sensor network has consisted three types of nodes: simple regular

sensor nodes, aggregator node and querier node. Regular sensor nodes sense data packet

from the environment and send to the aggregator nodes basically these aggregator nodes

collect data from multiple sensor nodes of the network, aggregates the data packet using a

some aggregation function like sum, average, count, max min and then sends aggregates

result to upper aggregator node or the querier node who generate the query. It can be the

base station or sometimes an external user who has permission to interact with the

network.

Data transmissions between sensor nodes, aggregators and the querier consume lot of

energy in wireless sensor network. Sensor nodes sense the physical environment and send

the data in the form of signals to the base station. Each of these scattered sensor nodes

has the capabilities to collect data and route data back to the sink. Data are routed back to

the sink by a multi hop infrastructure less architecture through the sink.

10

Figure 3-1 Without Data Aggregation With Six Transmission [3]

Figure 3-2 With Data Aggregation With Four Transmission [3]

The effect of the data aggregation is shown in figure 3.3. With the data aggregation

mechanism there is less no. of packets is wasted as well as the chance of the collisions is

also less. It may adversely affect other performance metrics such as retransmission,

energy consumption and throughput.

11

Figure 3-3 Effect of Data aggregation [3]

3.2 Factors Affected by Data Aggregation

Data aggregation attempts to collect the most critical data from the sensors and make it

available to the sink in an energy efficient manner with minimum data latency. Data

latency is important in many applications such as environment monitoring [2]

, where the

freshness of data is also an important factor. It is critical to develop energy-efficient data-

aggregation algorithms so that network lifetime is enhanced. There are several factors

which determine the energy efficiency of a sensor network, such as network architecture,

the data-aggregation mechanism, and the underlying routing protocol. The influence of

these factors on the energy efficiency of the network in the context of data aggregation is

described below.

Energy Efficiency: The functionality of the sensor network should be extended as long

as possible. In an ideal data-aggregation scheme, each sensor should have expended the

same amount of energy in each data gathering round. A data-aggregation scheme is

energy efficient if it maximizes the functionality of the network. If we assume that all

sensors are equally important, we should minimize the energy consumption of each

sensor. This idea is captured by the network lifetime which quantifies the energy

efficiency of the network.

12

Network lifetime, data accuracy, and latency are some of the important performance

measures of data-aggregation algorithms. The definitions of these measures are highly

dependent on the desired application.

Network Lifetime: Network lifetime is defined as the number of data-aggregation rounds

until a percent of sensors die where a percent is specified by the system designer. For

instance, in applications where the time that all nodes operate together is vital, lifetime is

defined as the number of rounds until the first sensor is drained of its energy. The main

idea is to perform data aggregation such that there is uniform energy drainage in the

network. In addition, energy efficiency and network lifetime are synonymous in that

improving energy efficiency enhances the lifetime of the network.

Data Accuracy: The definition of data accuracy [4]

depends on the specific application

for which the sensor network is designed. For instance, in a target localization problem,

the estimate of the target location at the sink determines the data accuracy.

Latency: Latency is defined as the delay involved in data transmission, routing, and data

aggregation. It can be measured as the time delay between the data packets received at the

sink and the data generated at the source nodes.

3.3 Data Aggregation Techniques

Data gathering is defined as the systematic collection of sensed data from multiple

sensors to be eventually transmitted to the base station for processing. Since sensor nodes

are energy constrained, it is inefficient for all the sensors to transmit the data directly to

the base station. Data generated from neighboring sensors is often redundant and highly

correlated. In addition, the amount of data generated in large sensor networks is usually

enormous for the base station to process. Hence, methods for combining data into high-

quality information at the sensors or intermediate nodes which can reduce the number of

packets transmitted to the base station resulting in conservation of energy and bandwidth.

This can be accomplished by aggregation. Data aggregation can be categorized on the

basis of network topology, network flow, quality of services and many more. Data

aggregation technique as shown in Figure 3.4.Data Aggregation technique into parts:

structure based and structure free. Structure based Data Aggregation can be further

divided into four parts flat network based, cluster based, tree based and grid based.

13

Figure 3-4 Taxonomy of Data Aggregation [3]

3.3.1 Flat Networks

In flat networks, each sensor node plays the same role. Node is equipped with

approximately the same battery power. In such networks, data aggregation is

accomplished by data centric routing where the sink usually transmits a query message to

the sensors. Sensors which have data matching the query send response messages back to

the sink. The choice of a particular communication protocol depends on the specific

application at hand.

3.3.2 Hierarchical Networks

A flat network can result in excessive communication and computation burdens at the

sink node, resulting in a faster depletion of its battery power. The death of the sink node

breaks down the functionality of the network. Hence, in view of scalability and energy

efficiency, several hierarchical data-aggregation approaches have been proposed.

Hierarchical data aggregation involves data fusion at special nodes, which reduces the

number of messages transmitted to the sink. This improves the energy efficiency of the

network.

3.3.2.1. Data Aggregation in Cluster-Based Networks

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

the data directly to the sink. In such scenarios, sensors can transmit data to a local

aggregator or cluster head which aggregates data from all the sensors in its cluster and

transmits the concise digest to the sink. This results in significant energy savings for the

energy-constrained sensors. Figure 3.5 shows a cluster-based sensor network

14

organization. The cluster heads can communicate with the sink directly via long range

transmissions or multi hopping through other cluster heads.

Figure 3-5 Cluster Based Sensor Network [4]

For example, The LEACH protocol is distributed and sensor nodes organize themselves

into clusters for data fusion. A designated node (cluster head) in each cluster transmits the

fused data from several sensors in its cluster to the sink. This reduces the amount of

information that is transmitted to the sink. The data fusion is performed periodically at the

cluster heads. LEACH is suited for applications which involve constant monitoring and

periodic data reporting.

3.3.2.2. Chain-Based Data Aggregation

In cluster-based sensor networks, sensors transmit data to the cluster head where data

aggregation is performed. However, if the cluster head is far away from the sensors, they

might expend excessive energy in communication. Further improvements in energy

efficiency can be obtained if sensors transmit only to close neighbors. The key idea

behind chain-based data aggregation is that each sensor transmits only to its closest

neighbor.

15

Figure 3-6 Chain Based Sensor Network [4]

For example, in Power-Efficient Data-Gathering Protocol for Sensor Information Systems

(PEGASIS) protocol, nodes are organized into a linear chain for data aggregation. The

nodes can form a chain by employing a greedy algorithm or the sink can determine the

chain in a centralized manner. Greedy chain formation assumes that all nodes have global

knowledge of network. The farthest node from the sink initiates chain formation and, at

each step, the closest neighbor of a node is selected as its successor in the chain. In each

data-gathering round, a node receives data from one of its neighbors, fuses the data with

its own, and transmits the fused data to its other neighbor along the chain. Eventually, the

leader node which is similar to cluster head transmits the aggregated data to the sink.

Figure 3.6 shows the chain-based data-aggregation procedure in PEGASIS.

3.3.2.3. Tree-Based Data Aggregation

In a tree-based network, sensor nodes are organized into a tree where data aggregation is

performed at intermediate nodes along the tree and a concise representation of the data is

transmitted to the root node. Tree-based data aggregation is suitable for applications

which involve in-network data aggregation. An example application is radiation-level

monitoring in a nuclear plant where the maximum value provides the most useful

information for the safety of the plant. One of the main aspects of tree-based networks is

the construction of an energy efficient data-aggregation tree. Figure 3.7 shows tree based

data aggregation.

16

Figure 3-7 Minimum Spanning Tree Based Routing [4]

For example, Energy Aware Distributed Heuristic (EADAT) to construct and maintain a

data-aggregation tree in sensor networks. The algorithm is initiated by the sink which

broadcasts a control message. The sink assumes the role of the root node in the

aggregation tree. The control message have five fields indicating the sensor ID, its parent,

its residual power, the status (leaf, non leaf node, or undefined state) and the number of

hops from the sink. After receiving the control message for the first time, a sensor v sets

up its timer to Tv. Tv counts down when the channel is idle. During this process, the sensor

v chooses the node with the higher residual power and shorter path to the sink as its

parent. This information is known to node v through the control message. When the timer

times out, the node v increases its hop count by one and broadcast the control message. If

a node u receives a message indicating that its parent node is node v, then u marks itself

as a non leaf node. Otherwise the node marks itself as a leaf node.

17

The process continues until each node broadcasts once and the result is an aggregation

tree rooted at the sink. The main advantage of this algorithm is that sensors with higher

residual power have a higher chance to become a non leaf tree node. To maintain the tree,

a residual power threshold Pth is associated with each sensor. When the residual power of

a sensor falls below Pth, it periodically broadcasts help messages for Td time units and

shuts down its radio. A child node, upon receiving a help message, switches to a new

parent. Otherwise it enters into a danger state. If a danger node receives a hello message

from a neighboring node v with shorter distance to the sink, it invites v to join the tree.

3.3.2.4. Grid-Based Data Aggregation

There are two data-aggregation schemes which are based on dividing the region

monitored by a sensor network into several grids. They are: grid-based data aggregation

and in network data aggregation. In grid-based data aggregation, a set of sensors is

assigned as data aggregators in fixed regions of the sensor network. The sensors in a

particular grid transmit the data directly to the data aggregator of that grid. Hence, the

sensors within a grid do not communicate with each other.

Figure 3-8 Grid Based Data Aggregation [4]

18

In grid-based data aggregation, the data aggregator is fixed in each grid and it aggregates

the data from all the sensors within the grid. This is similar to cluster-based data

aggregation in which the cluster heads are fixed. Grid based data aggregation is suitable

for mobile environments such as military surveillance and weather forecasting and adapts

to dynamic changes in the network and event mobility.

Figure 3-9 In-network Data Aggregation Scheme [4]

In in-network aggregation, the sensor with the most critical information aggregates the

data packets and sends the fused data to the sink. Each sensor transmits its signal strength

to its neighbors. If the neighbor has higher signal strength, the sender stops transmitting

packets. After receiving packets from all the neighbors, the node that has the highest

signal strength becomes the data aggregator. The in-network aggregation scheme is best

19

suited for environments where events are highly localized. This type of aggregation is

shown in fig 3.9.

3.3.3 Structure Free Network

In structure free data aggregation any type of structure cannot be maintained. This method

is very useful in event based application where event region changes very frequently and

if structure based approach is used then the structure can be maintained again and again.

In structure free environment because structure cannot be maintained so to reconstruct of

the structure at the time of node failure or the changing of event region is not required

There are two main challenges in performing structure free data aggregation. First, as

there is no pre constructed structure, routing decisions for the efficient aggregation of

packets need to be made on-the-fly. Second, as nodes do not explicitly know their

upstream nodes, they cannot explicitly wait on data from any particular node before

forwarding their own data. The benefit of this approach is that structure cannot be

maintained all the time whereas in structured environment reconstruct of structure is

necessary at the time of when some nodes fail due to energy failure.

3.4 Comparison between Hierarchical Networks and Flat network

Table 3-1 Comparison between Hierarchical and Flat Networks

Hierarchical Networks Flat Network

Data aggregation performed by cluster

heads or a leader node.

Data aggregation is performed by

different nodes along the multi-hop path.

Overhead involved in cluster or chain

formation throughout the network.

Data aggregation routes are formed only

in regions that have data for transmission.

Even if one cluster head fails, the network

may still be operational.

The failure of sink node may result in the

breakdown of entire network.

Lower latency is involved since sensor

nodes perform short-range transmissions.

Higher latency is in data transmission to

the sink via a multi hop path.

20

Routing structure is simple but not

necessarily optimal.

Optimal routing can be guaranteed with

additional overhead.

Node heterogeneity can be exploited by

assigning high energy nodes as cluster

heads.

Does not utilize node heterogeneity for

improving energy efficiency.

3.5 Advantages and Disadvantages of Data Aggregation in WSN

Advantages

With the help of data aggregation process the robustness and accuracy of information can

be enhanced which is obtained by entire network, certain redundancy exists in the data

collected from sensor nodes thus data fusion processing [5]

is needed to reduce the

redundant information. Another advantage is those reduces the traffic load and conserve

energy of the sensors.

Disadvantages

The cluster 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. If a cluster

head is compromised, then the base station (sink) cannot be sure the correctness of the

aggregate data that has been send to it. Another drawback [5]

is existing systems are

several copies of the aggregate result may be sent to the base station (sink) by

uncompromised nodes .It increase the power consumed at these nodes.

21

4. Data Aggregation by Precision Allocation

4.1 Introduction of Aggregation for Continuous Data Measuring

While the base station can have continuous power supply, the sensor nodes are usually

battery powered. The batteries are inconvenient and sometimes even impossible to

replace. When a sensor node runs out of energy, its coverage is lost. The mission of a

sensor application would not be able to continue if the coverage loss is remarkable.

Therefore, the practical value of a sensor network is determined by the time duration

before it fails to carry out the mission due to insufficient number of alive sensor

nodes. This duration is referred to as the network lifetime. It is both mission-critical and

economically desirable to manage sensor data in an energy efficient way to extend the

lifetime of sensor networks.

The data captured by the sensor nodes are often converted into an aggregate form

requested by the applications (e.g., average temperature reading). Primarily designed for

monitoring purposes, many sensor applications require continuous aggregation of sensor

data. Exact data aggregation requires substantial energy consumption because each sensor

node has to report every reading to the base station. In wireless sensor networks,

communication is a dominant source of energy consumption. To save energy, data

semantics can be relaxed to allow approximate data aggregation with precision

guarantees[6]

. The precision can, for example, be specified in the form of quantitative error

bounds: average temperature reading of all sensor nodes within an error bound of 1 C. In

this way, the sensor nodes do not have to report all readings to the base station. Only the

updates necessary to guarantee the desired level of precision need is sent to the base

station.

It is, however, a challenging task to optimize network life time under approximate data

aggregation because the sensor nodes are inherently heterogeneous in energy

consumption. First, when the data captured by different sensor nodes change at different

magnitudes and frequencies, the sensor nodes may report data at different rates. Second,

the wireless communication cost depends on the transmission distance. Due to the

geographically distributed nature of sensor networks, the sensor nodes are likely to

differ significantly in the energy cost of sending a message to the base station. Even if all

22

sensor nodes report data at the same rate, their energy consumption can be highly

unbalanced, thereby reducing network lifetime. In addition to reporting local sensor

readings, the intermediate nodes in a multi-hop network are also responsible for relaying

the data originated from other nodes to the base station.

4.2 Aggregation and Lifetime of Node

Three factors affecting the lifetime of sensor nodes in the context of approximate data

aggregation [7]

:

1) The changing pattern of sensor readings;

2) The residual energy of the sensor nodes;

3) The communication cost between the sensor nodes and the base station.

In this chapter, a candidate-based method for precision allocation and prove its optimality

for single-hop networks is discussed. Based on this method, an adaptive scheme is

proposed to dynamically adjust the error bounds allocated to the sensor nodes. The

adjustment period is also dynamically set to control the communication overhead.

4.3 Basics of Error Bound for Nodes

Here consider data aggregation with precision guarantees in a network of n sensor nodes.

The sensor nodes are geographically distributed in an operational area. They periodically

sample the local phenomena such as temperature and humidity. Without loss of

generality, the sampling period is assumed to be 1 time unit.

The base station collects data from the sensor nodes and feeds them to an application. The

application specifies the precision constraint of data aggregation by an upper bound E

(called the error bound) on the quantitative difference between an approximate result and

the exact result. That is, on receiving an aggregate result from the sensor network, the

application would like to be assured that the exact aggregate result lies in the interval.

In approximate data aggregation, not all sensor readings have to be sent to the base

station. To reduce communication cost, the designated error bound on aggregate data can

be partitioned and allocated to individual sensor nodes (call it precision allocation). Each

sensor node updates a new reading with the base station only when the new reading

23

significantly deviates from the last update to the base station and violates the allocated

error bound. To guarantee the designated precision of aggregate data, the error bounds

allocated to individual sensor nodes have to satisfy certain feasibility constraints.

Different aggregation functions impose different constraints.

4.4 Types of Aggregation Based on Precision Allocation

Three commonly used types of aggregations[8]

are as below

1. SUM

2. COUNT

3. AVERAGE

For SUM and COUNT aggregations, to guarantee an error bound on aggregate data, the

total error bound allocated to the sensor nodes cannot exceed E.

1

,n

i

i

e E

................................................................. (4.1)

Where, ei is the error bound allocated to node i and n is the number of sensor nodes

For AVERAGE aggregation, the total error bound allocated to the sensor nodes cannot

exceed n.E.

1

1,

n

i

i

e En

(4.2)

Eligible precision allocation under the feasibility constraint is not unique. For example, in

a network of 10 temperature sensor nodes, if the given error bound on AVERAGE

aggregation is 1 C, we can allocate an error bound of 1 C to each sensor node. Alternatively,

we can also allocate an error bound of 5.5 C to a selected node and an error bound 0.5 C to

each of the remaining nodes. This offers the flexibility to adjust the energy consumption of

individual sensor nodes by careful precision allocation. In general, to collect the readings

of a sensor node at higher precision (i.e., smaller error bound), the sensor node needs to

send data updates to the base station more frequently, which introduces higher energy

consumption.

24

4.5 Error Bound and Communication between Node and Sink

Let denote the energy consumed by sensor node i to send and receive a data update by si

and vi respectively. They can take different forms to cater for a wide range of factors. In

the simplest case, if all sensor nodes use a default radio communication range, sis are the

same for all nodes.

More sophisticatedly, if the sensor nodes know the locations of the receivers, they can

adapt the power level to the transmission distance. The sensor nodes with longer

transmission distances would be associated with higher si.

In addition, reliability can also be modeled in the energy cost. The sensor nodes incident

to less reliable links are entitled to higher sis and vis due to possible retransmissions. Let

simply assume that each sensor node i knows si and vi .

4.6 Optimal Precision Allocation

Let e1, e2, e3, e3en 0 be the error bounds [7] currently allocated to sensor nodes 1,

2,...n respectively. The quantitative relationship between the rate of data updates sent by a

sensor node and its allocated error bound depends on the changing pattern of sensor

readings. Without loss of generality, consider the update rate of each sensor node i as a

function of the allocated error bound ei. is essentially the rate at which node i

s reading changes by more than ei. Intuitively, is a non-increasing function with

respect to ei.

Since the sensor nodes in a single-hop network are not involved in relaying data from

other sensor nodes to the base station, the energy consumption rate of node i is simply

( ).i i iu e s (4.3)

where si refers to the energy cost for node to send a data update to the base station.

Suppose the residual energy of node i is Pi

( ).

i

i i i

p

u e s.. (4.4)

Therefore, the network lifetime is given by

25

min

1( ).

i

i i i

pi n

u e s

(4.5)

The objective of precision allocation is to find a set of error bounds e1, e2, , en that maximize the network lifetime under the constraint

1

n

i

i

e E

. (4.6)

Now analyze the optimal precision allocation. For simplicity, assume functions ui(.)s are continuous and denote the inverse function of ui(.) by ui

-1(.).

Since ui(.) is non-increasing, the minimum life time of sensor node I is given by

(0).

ii

i i

pl

u s

.... (4.7)

Now for node i having error bound ei, the lifetime of node is given as below,

( ).

ii

i i i

pl

u e s

. (4.8)

An optimal precision allocation (error bound) for node is given by

1

.

ii

i

pe u

l s

(4.9)

4.7 Candidate Based Precision Allocation

In practice, the exact forms of ui-1

(.)s (i.e., the changing patterns of sensor readings) may

not be known a priori and they may even change dynamically. The key idea is to let each

sensor node estimate [9]

and report to the base station the normalized energy consumption

rates for a number of candidate error bounds based on historical sensor readings. The base

station optimizes precision allocation based on these candidates to extend network

lifetime.

Since the general relationships between error bounds and update rates are not known, we

restrict the error bound allocated to each sensor node to one of its candidates. Such

allocations are called candidate precision allocations and the one that maximizes network

lifetime is called the optimal candidate precision allocation.

Assume that each sensor node chooses m candidates[10]

. For each node i , let

,1 ,2 ,...i i i me e e be the list of candidate error bounds, and ,1 ,2 ,, ,...,i i i mr r r be the

26

corresponding normalized energy consumption rates. It follows that,1 ,2 ,...i i i mr r r .

Suppose the smallest candidate error bounds for the sensor nodes do not add up to the

designated bound on data aggregation, i.e., 1,1 2,1 ,1... ne e e E .

4.8 Algorithm for Optimal Precision Allocation

Input:

E: error bound of data aggregation

,* ,*,i ie r : Candidate error bounds and normalized energy consumption rates

Output

, ii xe : error bound of each node in optimal allocation

1. for 1i to n do

2. 1;ix

3. end for

4. while min

1 i n ix m do

5. argj max

1 i n , ii x

r ;

6. if , 1 ,i ij x i xi j

e e E

then

7. break; 8. end if

9. 1;j jx x

10. end while

Initially, the error bound of each sensor node is set to its smallest candidate (steps 13). In

each iteration of steps 410, the error bound of the node having the highest energy

consumption rate is replaced with its next smallest candidate. The iteration stops if a new

replacement would make the total error bound of the sensor nodes exceeds the designated

bound on data aggregation (steps 67).

4.9 Adaptive Precision Allocation

Now present an adaptive precision allocation scheme that works by adjusting the error

bounds of the sensor nodes periodically. The interval between two successive adjustments

is called an adjustment period. At the beginning of an adjustment period, each sensor

node selects a list of candidate error bounds,1 ,2 ,, ,...,i i i me e e .The node keeps track of the

27

update counts under these error bounds as it captures new readings. At the end of the

adjustment period, node normalizes the counts by the length of period to obtain the data

update rate ,i ju for each ,i je . Node i then compute the normalized energy consumption rate

,i jr for each ,i je

by

,

,

.,

i j i

i j

i

u sr

p

(4.10)

Where pi is the present residual energy of node i. Node sends a candidate report message

including the,i je s and ,i jr s to the base station. On receiving the messages from all sensor

nodes, the base station computes the optimal precision allocation.

In case,,

1

n

i xi

i

e E

, the leftover error bound ,1

n

i xi

i

E e

is simply allocated to the node

with the highest normalized energy consumption rate since doing so would only extend

network lifetime. Finally, the base station sends a precision allocation message to the

sensor nodes including the new error bounds for their adjustments.

The closer the candidates to the current error bound, the smaller the difference between

neighboring candidates [11]

. The motivation is to adjust the error bounds at coarse

granularity when they are far away from the optimum, and adjust them at fine granularity

when they are close to the optimum.

Let ei be the current error bound of sensor node i. Then, the candidate error bounds of

node range from (1/2)ei to (3/2)ei. Given the number of candidates m=2k+1 , the

candidate error bounds are selected as

1 3 2 1 2 1 5 3, ,..., , , ,..., , .

2 4 2 2 4 2

k k

i i i i i i ik ke e e e e e e

(4.11)

Note that the network lifetime is determined by the lifetime [12]

of the most energy-

consuming node. Thus, to control the energy overhead of adjustments, we propose to cap

the energy overhead at the most energy-consuming node by a given portion of its

energy budget. This is done by dynamically adapting the adjustment period at each

adjustment. Specifically, each sensor node i count the number of data updates [13]

sent to

28

the base station in the adjustment periods. At an adjustment, node estimates its energy

consumption rate by

. iN s

L (4.12)

where is N the update count in the past adjustment period, si is the energy cost for

sending, and L is the duration of the past adjustment period.

Note that at an adjustment, each sensor node needs to send a candidate report message to

and receive a precision allocation message from the base station. Thus, the energy cost at

node due to an adjustment is si+vi, where si and vi are the sending and receiving costs

respectively. To limit it at a portion of the energy consumed by node i, the duration of

the next adjustment period Li should be set such that

.. ,i i i

s v N s

Li L

. (4.13)

i.e.,

( )

. .

i i

i

L s vLi

N s

(4.14)

Each sensor node i computes Li and includes it in the candidate report message sent to the

base station at the end of an adjustment period. Among all Lis received, the base station

selects the lowest one L as the next adjustment period so as to cap the adjustment

overhead at a portion of the energy consumed at the most consuming node. L is then

included in the precision allocation message sent by the base station to all sensor nodes.

29

5. Experimental Work A

5.1 Nodes Data (temperature) Generation

Change in temperature is not an abrupt process. In general, temperature changes

gradually. In simulation, assume initially all nodes sense same temperature say 28 C and

temperature changes as time goes on. The change in new sense temperature compared to

previous sensed temperature is between -2C and 2C. Fig. 5.1 shows temperature profile

for a node 1.

Figure 5-1 Temperature Data Profile

5.2 Error Bound of Node

As temperature changes gradually, nodes need not to send all sense temperature to base

station. This is controlled by error bound of node. Error bound decides which data to be

send to base station. Data which significantly deviates from previous sense reading are

necessary to send base station and this deviation is controlled by error bound of node.

Suppose for a node, error bound is 1C then deviation more than of 1C from previous

sense reading is sent to base station otherwise no need to send data. Data which closely

near to previous sense reading is never send to base station.

30

Initially total error bound (E) of network is assigned. Total error bound is divided to all

nodes. Assignment of error bounds to all nodes is such that summation of error bounds (e)

of all nodes gives total error bound of network. Initially all node have same error bound

which is decided by total error bound of network (E). If there are 10 nodes in network,

total error bound of network is 10C then error bound is 1C per node. So temperature

change is more than 1C then node send data to base station.

5.3 Adjustment in Error Bound of Node

Initially all node assigned same error bound. So nodes communicate with base station if

change in temperature is same. Due to this energy consumption of node is not nearly

equal. Energy consumption of node which is very far away from base station or near to

the environment where temperature changes frequently due to non avoidable disturbances

is very large.

To overcome above problem, error bound of node is changed at fixed time interval. This

time interval is known as adjustment interval. New error bound for node is calculated

based on residual energy of node.

For example, error bounds (eb1 and eb2) of two nodes having maximum energy

compared to other nodes are decreased by delta. Delta is similar to step size parameter.

= 1+2

2 .(5.1)

New error bound (eb1) for node is simply calculated by subtracting delta from its old

error bound. Similar calculation is applied to eb2 also.

1 = 1 (5.2)

2 = 2 ....(5.3)

where eb1 and eb2 are updated error bounds.

Error bounds of two nodes having lowest residual energy at end of adjustment interval are

also updated. For these nodes instead of substitution of delta to error bound addition

operation is done. So overall network error bound remain constant.

Consider eb4 and eb5 are the error bounds of node which have minimum residual energy

at the end of adjustment period then updated error bound is calculated as below equations.

31

4 = 4 + .(5.4)

5 = 5 + .(5.5)

where eb4 and eb5 are the updated error bounds.

5.4 Assumptions and Simulation Environment

For realization of the simple wireless sensor environment, the base station is fixed and

located far from the sensors at (500m, 500m). All nodes of network and base station are

static.

Table 5-1 Network Parameters

Description Value

Network Area 500m x 500m

Number of nodes 25

Initial energy 100 J

Data packet size 200 bytes

Electronics energy 50 nJ/bit

Free space energy 10 pJ/bit/m2

Figure 5-2 Network Topology

32

Fig. 5.2 shows deployment of nodes in sensor network of 500m x 500m area. Base station

is located at upper right corner (500,500).

5.4.1 Error Bounds of Nodes

Fig. 5.3 shows initially error bounds of nodes which is same and equal to nearly 1C.

After every adjustment period, error bound of nodes having lowest and highest energy is

updated. Error bound of node at the end of simulation is shown in fig. 5.4.

Figure 5-3 Initial Error Bound for Node

Figure 5-4 Error Bound of Node at Simulation End

33

5.4.2 Error Bound Changing Parameters

After every adjustment period error bound is updated based on residual energy of node.

Change in error bound is done only with two nodes which have highest energy and other

two nodes which have lowest energy. So other nodes error bounds are not affected at

every adjustment period. And base station need not send error bound of all nodes after

every adjustment period.

Figure 5-5 Highest Residual Energy Node at Every Adjustment Period End

Figure 5-6 Lowest Residual Energy Node at Every Adjustment Period End

34

Error bound of node which has highest residual energy is reduced by minus delta from

error bound of that node. Fig. 5.5 shows node from which delta is minus for error bound

update at every adjustment period end. Fig. 5.6 shows node which has lowest residual

energy at every adjustment period end. Error bound of this node is change by adding delta

to its error bound. The value of delta which is calculated at every adjustment period end is

shown in fig. 5.7.

Figure 5-7 Value of Delta at Every Adjustment Period End

5.4.3 Residual Energy and Communication Frequency of Node

Fig. 5.8 shows how many times node send data to base station. Due to different error

bound assign to all nodes, all nodes send different amount of data to base station. Fig. 5.8

shows though all nodes communicate with base station not equal times. Fig. 5.9 shows by

updating error bound of nodes after every adjustment period; energy consumption of

network is balanced. Network life time is defined by the life of first dying node. So by

balanced energy consumption, all nodes residual energy at the end of simulation is very

nearer which is shown in fig. 5.9. After adjustment period error bound of node which

have minimum residual energy is increased by delta and error bound of node which has

maximum energy is decreased by delta. The values of delta after every adjustment period

is as in fig. 5.7

35

Figure 5-8 No. of Times Node Send Data to Base Station

Figure 5-9 Residual Energy of Node at Simulation End

5.5 Network with Multiple Monitoring Sensors

Many times sensor network consists of nodes which sense more than one type of

parameters like temperature, humidity, etc. For such a network also, energy consumption

of node will be more without data aggregation. Though energy effective routing, energy

consumption of network can be reduced but using data aggregation, in a way due to less

data transmission system computation at base station side can be reduced. The concept of

error bound is also applicable to this case.

36

Suppose network monitors two parameters say X and Y. Then give total error bound (Ex)

for parameter X sensing and total error bound (Ey) for parameter Y sensing. Computation

for updated error bound values for such case is in line of single parameter sensing. For

two parameters, two error bound values (one for X and other for Y) is allocated to

particular sensing node. One can apply same method for error bound allocation and error

bound adjustment to node for X and Y parameters.

37

6. Experimental Work B

6.1 Simulation Environment

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

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

Qualnet, TOSSIM and so on. Since wireless sensor networks are special type of wireless

networks, most of the simulators available are not enough supported for simulating a

wireless sensor network scenario. The literature shows that the simulators which are

mostly used for wireless sensor network are NS-2 [15]

, J-Sim, GlomoSim, OPNET, and

TOSSIM. Even MATLAB is also used.

6.2 Steps for Creating Script in NS2

Step 1: Simulation parameters setup

Step 2: Initialization of a network

a) Create a ns simulator

b) Setup topography object

c) Open the NS trace file

d) Open the NAM trace file

Step 3: Mobile node parameter setup

Step 4: Configuration of a nodes like (x, y) dimensions

Step 5: Create a TCP or UDP agent and connect in to source nodes.

Step 6: Create TCP Sink and NULL agent and connect it to the destination node.

Step 7: Call End Procedure

6.3 Simulation Parameters and Assumptions

For simulation, following assumptions are considered.

1. All sensor nodes are homogeneous in physical characteristics such as initial energy,

antenna gain, etc.

38

2. All nodes are stationary.

3. Base station is also stationary and has infinite energy.

Table 6-1 Network Parameters

Parameters Values

Channel Type Wireless 802.11

Propagation Type Two Ray Ground

MAC protocol MAC 802.11

Queue Type Drop tail

Antenna Omni Antenna

Number of nodes 25

Queue Length 50

Routing protocol AODV

Traffic type CBR

Packet size 200 bytes

Initial energy 2 Joules

Network Area 500 m x 500 m

6.4 Description of Simulation

In simulation, base station is located at co ordinate (500m, 500m). Assume it has infinite

energy. All 25 nodes are placed randomly in the sensor network area 500m x 500m. All

nodes send data to base station. Simulation starting time is 0 second and ending time is

100 seconds i.e. communication between node and base station start at 0 second and end

at 100 second. Simulation runs for 100 seconds.

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How many times node sends data to base station is controlled by the error bound. In other

word, number of times node communicates with base station is function of error bound.

And data rate of node is decides how many times node communicates with base station.

So data rate is also analogous to error bound of node.

There are three different cases taken for simulation.

1. Equal data rate for all nodes which is analogous to same error bound allocation.

2. Different random data rate for all nodes which is analogous to different error bound

allocation.

3. Data rate according to nodes position (energy) which is analogous to energy saving

error bound allocation.

6.4.1 Same Error Bound Case

Fig. 6.1 shows simulation topology in which 25 nodes placed randomly in 500m x 500m

area and base station at co ordinate (500m, 500m). All nodes communicate with base

station same number of times. After every 2 seconds node sense data and send it to base

station. Fig. 6.2 shows advertise packets send by all nodes for hand shaking purpose in

AODV protocol. AODV routing protocol is very fair enough for successfully

transmission of data from sensor nodes to the base station without data packets drop.

Figure 6-1 Simulation Topology

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Figure 6-2 Hand Shaking Packets Transmit by Nodes

6.4.1.1. Residual Energy of Node

In fig. 6.3, residual energy of each node is shown. Due to same precision allocation to

each node, the rate of energy consumption of each node is not same because of all nodes

are placed at different location. The energy consumption is not balance for this case as

node represented by red in below fig. 6.3 consumes more energy compared to other

nodes.

Figure 6-3 Residual Energy of all Node vs Time

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Life time of network is defined by first dying node in network then due to node having

more energy consumption rate die first and decrease the life time of network. So energy

consumption of all node must be balance or nearly equal for increasing the life of wireless

sensor network.

By decreasing the data transmission of low residual energy of node, the energy

consumption can be reduced. And life time of network can be increase. But due to same

data rate which is analogous to same error bound all node communicate with base station

same times irrespective to their residual energy.

6.4.1.2. Remaining Energy of Node at Simulation End

The variation of remaining energy of node is very large because of not balance energy

consumption rate of node.

Table 6-2 Remaining Energy of Node at Simulation End

Node Energy in Joule Node Energy in Joule

0 1.5216 12 1.4927

1 1.5066 13 1.5207

2 1.5212 14 1.4755

3 1.4236 15 1.5209

4 1.5216 16 1.3288

5 1.5077 17 1.3287

6 1.5215 18 1.5213

7 1.5218 19 1.5219

8 1.5206 20 1.5080

9 1.5210 21 1.5215

10 1.5211 22 1.5204

11 1.4175 23 1.6813

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6.4.2 Random Different Error Bound Case

In this case also 25 nodes placed randomly in 500m x 500m area and base station at co-

ordinate (500m, 500m). All nodes communicate with base station different number of

times which is analogous to random error bound of node.

In this case, data rate of all nodes are not same. All nodes are allocated random data rate

irrespective to their location with respect to base station and their residual energy. Due to

this reasons this case has less life time compared to case 3 in which data rate of node is

define based on location of node with respect to base station.

6.4.2.1. Residual Energy of Each Node

In case of different precision (error bound) allocation, the rate of energy consumption of

node is not same for all nodes. Energy consumption rate is balanced compared to same

error bound allocation as shown in fig. 6.4.

Figure 6-4 Residual Energy of all Nodes vs Time


The variation of remaining energy of node at the end of simulation is less compared to

same error bound case and more compared to case 3(error bound based on nodes

location)

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0 1.6736 13 1.6857

1 1.6383 14 1.6608

2 1.6823 15 1.6441

3 1.6827 16 1.6849

5 1.6346 17 1.6714

6 1.6686 18 1.7172

7 1.6786 19 1.6004

8 1.6847 20 1.6808

9 1.6450 21 1.6819

10 1.6851 22 1.6600

11 1.6812 23 1.6722

12 1.6838 24 1.6813

6.4.3 Error Bound Based on Node Position (Energy) Case

All nodes placed randomly in 500m x 500m area and base station is located at co-ordinate

(500m, 500m). In this case error bound is defined by considering distance between node

and base station. The node which is far away from base station compared to other node

has more error bound compared to other near node to base station.

As error bound is analogous to data rate of node, node which is far away from the base

station have low data rate compared to node which is near to base station. In other word,

node which is far away communicates with base station less compared to other nodes.

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6.4.3.1. Residual Energy of Each Node

By this way, energy consumption of node is better and balanced as shown in fig. 6.5. This

kind of allocation provides good energy utilization of node compared to other two cases.

According to this allocation life time of network significantly increase compared to

previous cases.

Figure 6-5 Residual Energy of all Nodes vs Time


The variation of remaining energy of node is very less because of balance energy

consumption rate of node compared to previous two cases.



0 1.8562 13 1.8589

1 1.8582 14 1.8424

2 1.8592 15 1.8597

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3 1.8586 16 1.8534

5 1.8455 17 1.8561

6 1.8592 18 1.8572

7 1.8480 19 1.8586

8 1.8585 20 1.8527

9 1.8563 21 1.8580

10 1.8588 22 1.8554

11 1.8576 23 1.8505

12 1.8592 24 1.8562

6.4.4 Packet to Delivery Ratio

In same error bound case, all nodes send data to base station at fixed intervals which is

same for all nodes. While in case of random error bound assignment, data rate is different

for all nodes so, less numbers of data is sent to base station. For error bond based on

location of node case, data rate of node which is far away from base station is less

compared to other nodes.

Case Sent Packets Received Packets Ratio

Same Error Bound 1250 12

data aggregation to extend life of wsn

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