Data Aggregation in Cluster-based Wireless Sensor...
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A Thesis Submitted in partial fulfillment
of the requirements for the award of the degree of
MASTER OF TECHNOLOGY in
INFORMATION TECHNOLOGY (Specialization: WIRELESS COMMUNICATION & COMPUTING)
by
Pranay Tiwari (IWC2006014)
Under the Guidance of: Prof. U. S. Tiwary
IIIT-Allahabad
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY ALLAHABAD – 211 012 (INDIA)
July, 2008
Date: ___________________
I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER
OUR SUPERVISION BY Pranay Tiwari ENTITLED Data
Aggregation in Cluster-based Wireless Sensor Network BE
ACCEPTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF TECHNOLOGY IN
INFORMATION TECHNOLOGY FOR EXAMINATION
Prof. U. S. Tiwary
(THESIS ADVISOR)
COUNTERSIGNED
Prof. U. S. Tiwary DEAN (ACADEMIC)
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY AAllllaahhaabbaadd
(Deemed University) (A Centre of Excellence in Information Technology Established by Govt. of India)
CERTIFICATE OF APPROVAL* The foregoing thesis is hereby approved as a creditable study in
the area of Information Technology carried out and presented in a
manner satisfactory to warrant its acceptance as a pre-requisite to the
degree for which it has been submitted. It is understood that by this
approval the undersigned do not necessarily endorse or approve any
statement made, opinion expressed or conclusion drawn therein but
approve the thesis only for the purpose for which it is submitted.
COMMITTEE
ON
FINAL EXAMINATION
FOR EVALUATION
OF THE THESIS
*Only in case the recommendation is concurred in
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY AAllllaahhaabbaadd
(Deemed University) (A Centre of Excellence in Information Technology Established by Govt. of India)
DECLARATION
This is to certify that this thesis work entitled “Data Aggregation in
Cluster-based Wireless Sensor Networks’’ which is submitted by me in
partial fulfillment of the requirement for the completion of M.Tech. in
Information Technology specialization in Wireless Communication and
Computing to Indian Institute Of Information Technology, Allahabad
comprises only my original work and due acknowledgement has been made
in the text to all other materials used.
Pranay Tiwari
M.Tech. (WCC)
IWC200614
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY AAllllaahhaabbaadd
(Deemed University) (A Centre of Excellence in Information Technology Established by Govt. of India)
Abstract
The rapid advancement of hardware technology has enabled the development of small,
powerful, and inexpensive 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.
We consider energy constrained wireless sensor network deployed over a region. The
main task of such a network is to gather information from node and transmit it to base
station for further processing. So the aim of any data forwarding protocol is to conserve
energy to maximize the network lifetime. Sensor nodes are capable of performing in-
network aggregation of data coming from more than one source.
In this thesis we have concentrated on energy consumption issue and aim to develop an
energy efficient data aggregation protocol. To provide energy efficiency we have
considered a cluster-based wireless sensor network. Our protocol executes on each
cluster independently and provides an energy efficient data aggregation in a cluster and
hence maximize network lifetime for whole network.
Acknowledgements
Before, I get into thick of things; I would like to add a few heartfelt words for the people
who were part of my thesis in numerous ways, people who gave unending support right
from the beginning. During this period, the faculty members and my batch mates took
keen interest and participated actively. They are very efficient and qualified in their
respective disciplines.
I express my sincere gratitude to my thesis supervisor Prof. U.S. Tiwary, Indian
Institute of Information Technology-Allahabad for all his affectionate encouragement
and guidance during the entire Thesis. His views and inputs are very helpful throughout
the process.
I would like to thank Dr. Shirshu Verma, Indian Institute of Information
Technology-Allahabad, who suggested many related points and is always very
constructive and helpful.
I would like to thank Dr. M.D. Tiwari, Hon’ble Director, Indian Institute of
Information Technology-Allahabad for the facilities and environment for research.
Not the least I would like to appreciate the support and suggestions of my dear friend
Neelam who continuously encouraged me during the progress of my thesis work and my
M.Tech friends Anuraag, Koustubh, Lalit, and Satish for everyday chatting we had on
several topics.
Lastly I would like to thank my family for their love, support and encouragement that
they have given me throughout my life, helping me to persevere in my studies. In
addition, I thank almighty god who made me a normal human being and gave me such
strength.
List of Figures
Figure 2.1 Simplified Schematic of Directed Diffusion 12
Figure 2.2 E-Span protocol 14 Figure 2.3 Example of Aggregation path over ring structure 15 Figure 2.4 Illustration of Two-phase clustering 17
Figure 2.5 Data transmission using ESPDA 18 Figure 3.1 A typical scenario of data aggregation in a cluster 23 Figure 3.2 Parent selection procedure 24 Figure 4.1 Random nodes scenario in NAM 29 Figure 4.2 Snapshot of console 32 Figure 4.3 Data transfer between node 0 and 10 through node 1 & 2 33 Figure 4.4 Energy level of nodes drop to first threshold 34 Figure 4.5 Energy level of nodes drop to second threshold 35 Figure 4.6 Residual energy of source as a function of time 36 Figure 4.7 Throughput as function of time 37 Figure 4.8 Effect of network density 38 Figure 4.9 Packet delivery ratio of the network 39 Figure A.1 The basic structure of NS-2 43
Table of Contents
CERTIFICATE OF APPROVAL DECLARATION ABSTRACT ACKNOWLEDGEMENTS LIST OF FIGURES Chapter 1 INTRODUCTION …………………………………………….. 1
Wireless Sensor Networks …………………………………….. 1 1.1.1 Sensor Network Challenges ………………………………… 2 1.1.2 Wireless Sensor Network vs. Traditional Wireless Network…. 4
1.1
1.1.3 Clustering in WSN ………………………………………….. 5 1.2 Motivation ……………………………………………………... 6 1.3 Problem Definition …………………………………………….. 7 1.4 Report Organization …………………………………………... 8
Chapter 2 DATA AGGREGATION: AN OVERVIEW ………………... 9 2.1 In-Network Aggregation ……………………………………… 9 2.2 Tree-Based Approaches ………………………………………. 11 2.3 Multi-path Approaches ……………………………………….. 14 2.4 Cluster-based Approaches …………………………………..... 16 2.5 Simulation Tools ………………………………………………. 18 2.6 Summary ………………………………………………………. 19
Chapter 3 ENERGY-AWARE BALANCED IN-NETWORK AGGREGATION………………………………………………
20
3.1 Introduction ……………………………………………............. 20
3.2 System & Energy Model ……………………………………… 21
Protocol Description …………………………………………... 22
3.3.1 Configuration Packet Flow …………………………………… 23
3.3
3.3.2 Data Packet Flow ……………………………………………... 24
3.4 Issues …………………………………………………………… 26
3.5 Summary ………………………………………………………. 26
Chapter 4 PERFORMANCE ANALYSIS ………………………………. 28
4.1 Simulation Analysis …………………………………………… 28
4.1.1 Simulation Setup ……………………………………………. 28
4.1.2 Simulation Run ………………………………………........... 33
4.1.3 Simulation Results …………………………………….......... 36
Chapter 5 CONCLUSION & FUTURE WORK ………………………... 40
Appendix A Network Simulator 2 …………………………………………............ 42
References ……………………………………………………...................................... 47
Data Aggregation in Cluster-based Wireless Sensor Networks
Chapter 1
Introduction
Before giving any outline of this thesis, we are going to introduce you to the
world of wireless sensor networks. We will briefly describe challenges and application of
wireless sensor networks. We will also provide you the motivation behind the selection of
the data aggregation issue for this thesis and also describe what exactly the data
aggregation is. Finally we will conclude this with providing outline of the thesis report.
1.1 Wireless Sensor Networks
A wireless sensor network is a wireless network consisting of tiny devices which
monitor physical or environmental conditions such as temperature, pressure, motion or
pollutants etc. at different regions. The tiny device, known as sensor node, consists of a
radio transceiver, microcontroller, power supply, and the actual sensor. Initially sensor
network were used for military applications but now they are widely used for civilian
application area including environment and habitat monitoring, healthcare application
and so on.
Normally sensor nodes are spatially distributed throughout the region which has
to be monitored; they self-organize in to a network through wireless communication, and
collaborate with each other to accomplish the common task. With the going time, sensor
nodes are becoming smaller, cheaper, and more powerful which enable us to deploy a
large-scale sensor network.
Basic features of sensor networks are self-organizing capabilities, dynamic
network topology, limited power, node failures & mobility of nodes, short-range
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broadcast communication and multi-hop routing, and large scale of deployment [12]. The
strength of wireless sensor network lies in their flexibility and scalability. The capability
of self-organize and wireless communication made them to be deployed in an ad-hoc
fashion in remote or hazardous location without the need of any existing infrastructure.
Through multi-hop communication a sensor node can communicate a far away node in
the network. This allows the addition of sensor nodes in the network to expand the
monitored area and hence proves its scalability & flexibility property.
Presently there are different types of commercially available sensor nodes.
University of California at Berkeley has developed Mica mote which is a special purpose
sensor node. Other special purpose sensor nodes available are Spec, Rene, Mica 2, Telos
etc. Some high bandwidth sensor nodes available are BTNode, Imote 1.0, Stargate,
Inryonc Cerfeube etc. [13].
1.1.1 Sensor Network Challenges
Wireless sensor network promise a wide variety of application and to realize these
application in real world, we need more efficient protocols and algorithms. Designing a
new protocol or algorithm address some challenges which are need to be clearly
understood. These challenges are summarized below:
Physical Resource Constraints: The most important constraint
imposed on sensor network is the limited battery power of sensor nodes. The
effective lifetime of a sensor node is directly determined by its power supply.
Hence lifetime of a sensor network is also determined by the power supply.
Hence the energy consumption is main design issue of a protocol. Limited
computational power and memory size is another constraint that affects the
amount of data that can be stored in individual sensor nodes. So the protocol
should be simple and light-weighted. Communication delay in sensor network
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can be high due to limited communication channel shared by all nodes within
each other’s transmission range.
Ad-hoc Deployment: Many application or most of them requires the ad-hoc
deployment of sensor nodes in the region. Sensor nodes are randomly deployed
over the region without any infrastructure which requires the system to be able to
cope up with random distribution and form connection between the nodes. As an
example, for fire detection in a forest the nodes typically would be dropped in to
the forest from a plane.
Fault-Tolerance: In a hostile environment, a sensor node may fail due to
physical damage or lack of energy (power). If some nodes fail, the protocols that
are working upon must accommodate these changes in the network. As an
example, for routing or aggregation protocol, they must find suitable paths or
aggregation point in case of these kinds of failures.
Scalability: In a region, depending upon the application, the number of sensor
nodes deployed could be in order of hundreds, thousands or more. The protocols
must scalable enough to respond and operate with such large number of sensor
nodes.
Quality of Service: Some sensor application are very time critical which
means the data should be delivered within a certain period of time from the
moment it is sensed, otherwise the data will be careless. So this could be a QOS
parameter for some applications.
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1.1.2 Wireless Sensor Networks vs. Traditional Wireless
Networks
Though many existing protocol, techniques and concepts from traditional wireless
network, such as cellular network, mobile ad-hoc network, wireless local area network
and Bluetooth, are applicable and still used in wireless sensor network, but there are also
many fundamental differences which lead to the need of new protocols & techniques.
Some of the most important characteristic differences are summarized below:
Number of nodes in wireless sensor network is much higher than any traditional
wireless network. Possibly a sensor network has to scale number of nodes to
thousands. Moreover a sensor network might need to extend the monitored area
and has to increase number of nodes from time to time. This needs a highly
scalable solution to ensure sensor network operations without any problem.
Due to large number of sensor nodes, addresses are not assigned to the sensor
nodes. Sensor networks are not address-centric; instead they are data-centric
network. Operations in sensor networks are centered on data instead of individual
sensor node. As a result sensor nodes require collaborative efforts.
Wireless sensor networks are environment-driven. While data is generated by
humans in traditional networks, the sensor network generate data when
environment changes. As a result the traffic pattern changes dramatically from
time to time.
Another characteristic unique to wireless sensor network is the correlated data
problem. Data collected by neighboring sensor nodes are often quite similar
which makes possible to the development of routing and aggregation techniques
that can reduce redundancy and improve energy efficiency. It also been observed
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that the environmental quantities changes very slow and some consecutive
readings sense temporally correlated data. This advantageous feature can be
exploited to develop an energy efficient data gathering and aggregation
techniques.
1.1.3 Clustering in WSN
It is widely accepted that the energy consumed in one bit of data transfer can be
used to perform a large number of arithmetic operations in the sensor processor [13].
Moreover in a densely deployed sensor network the physical environment would produce
very similar data in close-by sensor nodes and transmitting such data is more or less
redundant. Therefore, all these facts encourage using some kind of grouping of nodes
such that data from sensor nodes of a group can be combined or compressed together in
an intelligent way and transmit only compact data. This can not only reduce the global
data to be transmitted and localized most traffic to within each individual group, but
reduces the traffic and hence contention in a wireless sensor network. This process of
grouping of sensor nodes in a densely deployed large-scale sensor network is known as
clustering. The intelligent way to combined and compress the data belonging to a single
cluster is known as data aggregation.
There are some issues involved with the process of clustering in a wireless sensor
network. First issue is, how many clusters should be formed that could optimize some
performance parameter. Second could be how many nodes should be taken in to a single
cluster. Third important issue is the selection procedure of cluster-head in a cluster.
Another issue that has been focused in many research papers is to introduce heterogeneity
in the network. It means that user can put some more powerful nodes, in terms of energy,
in the network which can act as a cluster-head and other simple node work as cluster-
member only. Considering the above issues, many protocols have been proposed which
deals with each individual issue.
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1.2 Motivation
Wireless sensor network are new age technology which can be used at a place
which is possibly hostile & human inaccessible. Wireless sensor network is a class of
wireless network which 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.
In wireless sensor network, there are so many challenges & issues as above
already been discussed. The main challenges are how to provide maximum lifetime to
network and how to provide robustness to network. As sensor network totally rely on
battery power, the main aim for maximizing lifetime of network is to conserve battery
power or energy.
In sensor network, the energy is mainly consumed for three purposes: data
transmission, signal processing, and hardware operation. It is said in [4] 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.
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, since transmitting huge volume
of raw data is an energy intensive operation, and thus minimizing the number of data
transmission. Also multiple sensors may see the same phenomenon, albeit 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
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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 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. That neighbor node
performs aggregation function and again forwards it on. But performing data forwarding
and aggregation in this fashion from various sources to sink causes significant energy
waste as each node in the network is involved in operation. 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 aggregation function over cluster-head still causes
significant energy wastage. In case of homogeneous sensor network cluster-head will
soon die out and again re-clustering has to be done which again cause energy
consumption.
We would like to present an algorithm that performs data aggregation within a
cluster and thus reducing the load of aggregation at cluster-head to provide energy
efficiency for maximizing network lifetime.
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1.4 Report Organization
With the end of chapter 1, the whole report is organized as follows:
Chapter 2 gives a detailed overview of data aggregation. This chapter also presents the
literature survey that had been done.
Chapter 3 introduces and describes the new proposed protocol for data aggregation in
cluster-based wireless sensor networks.
Chapter 4 will present the performance analysis of the proposed protocol. It will also
provide you the comparison results.
Conclusion is given in the last chapter 5 and scope of future enhancements is also
incorporated.
Appendix – A describes the network simulator NS-2 and its usability to simulate
proposed protocol.
Materials (e.g. URLs, books, and research papers) used and studied are given in
Reference.
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Chapter 2
Data Aggregation: An Overview
Wireless sensor network have recently been the focus of many research efforts
and emerged as an important new area in wireless technology. As chapter 1 already
discuss about the problem of data aggregation, this chapter presents an overview of data
aggregation. This chapter first discusses in-network aggregation and then different
approaches that are widely used for data aggregation.
2.1 In-Network Aggregation
In a typical sensor network scenario, different node collect data from the
environment and then send it to some central node or sink which analyze and process the
data and then send it to the application. But in many cases, data produced by different
node can be jointly processed while being forwarded to the sink node. So in-network
aggregation deals with this distributed processing of data within the network.
Data aggregation techniques explore how the data is to be routed in the network
as well as the processing method that are applied on the packets received by a node. They
have a great impact on the energy consumption of nodes and thus on network efficiency
by reducing number of transmission or length of packet. Elena Fosolo et al in [7] defines
the in-network aggregation process as follows: “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.”
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There are two approaches for in-network aggregation: with size reduction and
without size reduction. In-network aggregation with size reduction refers to the process
of combining & 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. As an
example, consider the situation when a node receives two packets which have a spatial
correlated data. In this case it is worthless to send both packets. Instead of that one should
apply any function like AVG, MAX, MIN and then send a single packet. This approach
considerably reduces the amount of bits transmitted in the network and thus saving a lot
of energy but on the other hand, it also reduces the precision of value of data received. 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. As an example, two packets may contain different physical quantities (like
temperature & humidity) and they can be merged in to a single packet by keeping both
values intact but keeping a single header. This approach preserves the value of data and
thus transmit more bits in the network but still reduce the overhead by keeping single
header.
This of the two approaches to use depends on many factors like the type of
application, data rate, network characteristics and so on. There is also a trade-off between
energy consumption and precision of data for the two approaches.
Most of the work done till now on in-network aggregation mainly deals with
problem of forwarding packets from source to sink, to facilitate aggregation therein.
Actually the main idea behind were to enhance existing routing protocols such that they
can efficiently aggregate data. So till now, most of the data aggregation techniques fall
under three categories. They are tree-based approaches, multi-path approaches, and
cluster-based approaches. There also some hybrid approaches that combines any of the
three techniques above. So, all the three approaches will be described in coming sections
with giving details of some of the main techniques by different authors.
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2.2 Tree-Based Approach
The simplest way to aggregate data is to organize the nodes in a hierarchical
manner and then select some nodes as the aggregation point or aggregators. The tree-
based approach perform aggregation by constructing an aggregation tree, which could be
a minimum spanning tree, rooted at sink and source nodes are considered as leaves. Each
node has a parent node to forward its data. Flow of data starts from leaves nodes up to the
sink and therein the aggregation done by parent nodes. The way this approach operates
has some drawbacks. As we know like any wireless network the wireless sensor networks
are also not free from failures. In case of packet loss at any level of tree, the data will be
lost not only for a single level but for whole related sub-tree as well. In spite of high cost
for maintaining tree structure in dynamic networks and scarce robustness of the system,
this approach is very much suitable for designing optimal aggregation technique and
energy-efficient techniques.
S. Madden et al. in [14] proposed a data-centric protocol which is based on
aggregation tress, known as Tiny Aggregation (TAG) approach [14]. TAG works in two
phases: distribution phase and collection phase. In distribution phase, TAG organizes
nodes in to a routing tree rooted at sink. The tree formation starts with broadcasting a
message from sink specify level or distance from root. When a node receive this message
it sets its own level to be the level of message plus one and elect parent as node from
which it receives the message. After that, node rebroadcast this message with its own
level. This process continues until all nodes elect their parent. After tree formation, sink
send queries along structure to all nodes in the network. TAG uses database query
language (SQL) for selection and aggregation functions. In collection phase, data is
forwarded and aggregated from leaves nodes to root. A parent node has to wait for data
from all its child node before it can send its aggregate up the tree. Apart from the simple
aggregation function provided by SQL (eg: COUNT, MIN, MAX, SUM, and AVG),
TAG also partitions aggregates according to the duplicate sensitivity, exemplary and
summary, and monotonic properties. Though TAG periodically refresh tree structure of
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network but as most of the tree-based schemes are inefficient for dynamic network, so
TAG may be.
C. Intanagonwiwat et al. in [3] proposed a reactive data-centric protocol for
applications where sink ask some specific information by flooding, known as directed
diffusion paradigm. The main idea behind directed diffusion paradigm is to combine data
coming from different source and en-route them by eliminating redundancy, minimizing
the number of data transmission; thus maximizing network lifetime. Directed diffusion
consists of several elements: interests, data messages, gradients, and reinforcements.
Figure 2.1 Simplified schematic for directed diffusion. (a) Interest propagation. (b) Initial
gradients setup. (c) Data delivery along reinforced path [3].
The base station (BS) requests data by broadcasting an interest message which
contains a description of a sensing task. This interest message propagates through the
network hop-by-hop and each node also broadcast interest message to its neighbor. As
interest message propagates throughout the network, gradients are setup by every node
within the network. The gradient direction is set toward the neighboring node from which
the interest is received. This process continues until gradients are setup from source node
to base station. Loops are not checked at this stage but removed at later stage. After this
path of information flow are formed and then best path are reinforced to prevent further
flooding according to a local rule. Data aggregation took place on the way of different
paths from different sources to base station or sink. The base station periodically refresh
& resend the interest message as soon as it start to receives data from sources to provide
reliability. The problem with directed diffusion is that it may not be applied to
applications (e.g. environmental monitoring) that require continuous data delivery to base
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station. This is because query driven on demand data model may not help in this regard.
Also matching data to queries might require some extra overhead at the sensor nodes.
Mobility of sink nodes can also degrade the performance as path from sources to sinks
cannot be updated until next interest message is flooded throughout the network. To cope
up with above issue if introduce frequent flooding then also too much overhead of
bandwidth and battery power will be introduced. Furthermore, exploratory data follow all
possible paths in the network following gradients which lead to unnecessary
communications overhead.
M. Lee et al. in [2] proposed a new low-control-overhead data dissemination
scheme, which they called as pseudo-distance data dissemination (PDDD), for efficiently
disseminating data from all sensor nodes to mobile sink. Some assumption have been
made, they are: (1) all source nodes maintain routes to mobile sink node, (2) no
periodically messaging for topological changes due to mobile sink node, (3) all link are
bi-directional and no control messages are lost, (4) mobile sink nodes have unlimited
battery power, so no need to care about battery efficiency of sink node, and (5) network
partitioning is not considered. Data dissemination process is influenced by directed
diffusion [3]. Though mobile sink periodically broadcast interest message, sensor nodes
do not send exploratory data and do not wait reinforcement message because each sensor
node already has routes to the sink node. After getting interest message, adjacent nodes
set a parent-child relationship using pseudo-distance of each node and finally a partial
ordered graph (POG) has been build. Optimal data dissemination is achieved in terms of
path length by forwarding packets to a parent node until topology is unchanged. Then
each sensor node is assigned a level for a corresponding sink node with pseudo-distance.
In order to overcome the shortcoming of POG, author used totally ordered graph (TOG)
in place of POG. The problem identified in this approach is that due to mobility of sink
node all sensor nodes have to maintain routes and for any change in topology nodes have
to again change route accordingly which led to energy waste.
Marc Lee et al. in [8] proposed an energy-aware spanning tree algorithm for data
aggregation, referred as E-Span. E-Span is a distributed protocol in which source node
that has highest residual energy is chosen as root. Other source nodes choose their parent
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based on residual energy and distance to the root. The protocol uses configuration
message to exchange information of node i.e., residual energy and distance to the root.
Each node performs single-hop broadcast operation to send packets. Single-hop broadcast
refers to the operation of sending a packet to all single-hop neighbors [8].
Figure 2.2 E-Span protocol [8].
2.3 Multi-path Approach
One of the main drawbacks of tree-based approach is the scarce robustness of the
system. To overcome this drawback, a new approach was proposed by many researchers.
Instead of sending partially aggregated data to a single parent node in aggregation tree, a
node could send data over multiple paths. The idea behind is that each node can send the
data to its possibly multiple neighbors by exploiting the wireless medium characteristic.
Hence data will flow from sources to sink along multiple paths and aggregation can be
performed by each intermediate node. Clearly schemes using this approach will make the
system robust but with some extra overhead. One of the aggregation structures that fit
well with this approach is ring topology, where network is divided in to concentric circles
with defining levels according to the hop distance from sink.
S. Nath et al. in [15] presented a data aggregation technique using multi-path
approach, known as synopsis diffusion. Synopsis diffusion works in two phases:
distribution of queries and data retrieval phase. During distribution of queries phase, a
node sends a query in the network. The network nodes then form a set of rings around the
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querying node. The node which is i hop away from querying node is considered is ring
Ri. In the second phase, aggregation starts from outermost ring and propagate level by
level towards the sink. Here a source node can have multiple paths towards sink.
Figure 2.3 Examples of aggregation paths over a ring structure [7].
L. Gatani et al. in [5] describe a new strategy for data gathering in wireless sensor
network that consider both issues: energy efficiency and robustness. Authors first say that
single path to connect each node to the base station is simple & energy-saving approach
but expose a high risk of disconnection due to node/link failures. But multi-path 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
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Data Aggregation in Cluster-based Wireless Sensor Networks
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.
2.4 Cluster-Based Approach
We talked about hierarchical organization of the network in tree-based approach.
Another scheme to organize the network in hierarchical manner is cluster-based
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 sink. The advantages and disadvantages of the cluster-based
approaches is very much similar to tree-based approaches.
K. Dasgupta et al. in [16] proposed a maximum lifetime data aggregation
(MLDA) algorithm which finds data gathering schedule provided location of sensors and
base-station, data packet size, and energy of each sensor. A data gathering schedule
specifies how data packet are collected from sensors and transmitted to base station for
each round. A schedule can be thought of as a collection of aggregation trees. In [6], they
proposed heuristic-greedy clustering-based MLDA based on MLDA algorithm. In this
they partitioned the network in to cluster and referred each cluster as super-sensor. They
then compute maximum lifetime schedule for the super-sensors and then use this
schedule to construct aggregation trees for the sensors.
W. Choi et al. in [1] present a two-phase clustering (TPC) scheme. Phase I of this
scheme creates clusters with a cluster-head and each node within that cluster form a
direct link with cluster-head. Phase I of this scheme is similar to various scheme used for
clustering but differ in one way that the cluster-head rotation is localized and is done
based on the remaining energy level of the sensor nodes which minimize time variance of
sensors and this lead to energy saving from unnecessary cluster-head rotation. In phase II,
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Data Aggregation in Cluster-based Wireless Sensor Networks
each node within the cluster searches for a neighbor closer than cluster-head which is
called data relay point and setup up a data relay link. Now the sensor nodes within a
cluster either use direct link or data relay link to send their data to cluster head which is
an energy efficient scheme. The data relay point aggregates data at forwarding time to
another data relay point or cluster-head. In case of high network density, TPC phase II
will setup unnecessary data relay link between neighbors as closely deployed sensor will
sense same data and this lead to a waste of energy.
Figure 2.4 Illustration of Two Phase Clustering [1].
H. Cam et al. in [4] present energy efficient and secure pattern based data
aggregation protocol which is designed for clustered environment. In conventional
method data is aggregated at cluster-head and cluster-head eliminate redundancy by
checking the content of data. This protocol says that instead of sending raw data to
cluster-head, the cluster members send corresponding pattern codes to cluster-head for
data aggregation. If multiple nodes send the same pattern code then only one of them is
finally selected for sending actual data to cluster-head. For pattern matching, authors
present a pattern comparison algorithm.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Figure 2.5 Data transmission using ESPDA [4].
2.5 Simulation Tools
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, J-Sim, GlomoSim, OPNET,
TOSSIM, PROWLER and even MATLAB is also used.
NS-2 is the most popular and powerful simulator. NS-2 is an object-oriented
discrete time event simulator and its modular design made it to be extensible. The detail
of NS-2 is provided in appendix A. To simulate sensor network, there had been made
attempt to put some add-ons. The most appreciable extension to NS-2 for wireless sensor
networks was developed in 2004 by Ian Downward of Naval Research Laboratory (NRL)
[18]. In this extension, they had created phenomenon packet which trigger event for
sensor nodes. They also designed sensor agent, sensor application and other application
to support wireless sensor network. But there has been no improvement or work done
since 2006. This is the reason why all research is going by using NS-2 without any
extension support. Other simulators like J-Sim, GlomoSim, OPNET, OMNet++ are
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Data Aggregation in Cluster-based Wireless Sensor Networks
exceptionally used by researchers. Another simulators which is MATLAB based, known
as PROWLER, is sometimes used for simulating routing protocol or some MAC issues in
wireless sensor network. MATLAB is also used for simulating some physical layer
issues. But still there is no efficient simulator which is purely dedicated to wireless sensor
network. More details for comparative study on simulator for wireless sensor network
could be found in [19].
2.6 Summary
In-network data aggregation is the process of gathering data from all nodes and
processes them at intermediate node while forwarding towards sink.
Tree-based approaches construct a minimum spanning aggregation tree rooted at
sink and covering all nodes in the network.
In multi-path approaches a node choose more than one parent for forwarding data
so as to have multiple paths towards sink to make the system robust.
Cluster-based approaches organize the network in to several clusters each with a
cluster-head which responsible for aggregating data locally before forwarding
towards sink.
Many simulators are used by researchers but NS-2 is the most efficient and
widely used.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Chapter 3
Energy-aware Balanced In-Network Aggregation
After the detailed description of wireless sensor networks, problem of data
aggregation for maximizing network lifetime, and existing aggregation techniques in
chapter 1 and 2, we now present our proposed protocol for solving data aggregation
problem. This chapter first gives you an introduction of our protocol “Energy-aware
Balanced In-Network Aggregation (E-BINA)” and then briefly describes the system and
energy model that has been considered for designed protocol. After this a detailed
description of the algorithm will be presented. As there are some issues involved with
every protocol, so with our proposed protocol also. These issues will be presented in brief
and finally we summarize this chapter.
3.1 Introduction
The three broad categories of data aggregation techniques that we have described
in previous chapter are: tree-based approach, multi-path approach, and cluster-based
approach. Focusing on cluster-based approach, we found that the existing protocols
assume a sensor network which is divided in to several clusters. Depending upon the
protocol operation, each cluster-head receives the data packets from some cluster-
member or from all cluster-member nodes directly and then cluster-head perform
aggregation operation.
Taking the advantageous features of tree-based approach, we have designed our
protocol which takes the merits of both cluster-based and tree-based approach. E-BINA
assumes a cluster-based wireless sensor network and applies tree-based approach inside
each cluster. When a cluster is formed and cluster-head selected, it consider cluster-head
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Data Aggregation in Cluster-based Wireless Sensor Networks
as root and construct an aggregation tree over cluster-member nodes. The process of
aggregation tree construction requires the sensor nodes to reduce their transmission
power as sensor nodes now have to send their data packets to the neighbor node which is
selected as parent. Energy consumed in wireless transmission is directly proportional to
the square of the distance between nodes in communication [13]. Since cluster-member
node now sends their data packets to the neighbor node instead of cluster-head, the
transmission distance is reduced and hence the energy consumption of the sensor node.
Likewise, overall energy consumption of sensor nodes in a cluster is reduced and so for
the whole sensor network. Hence overall network lifetime will be increased.
Energy-aware Balanced In-Network Aggregation (E-BINA) protocol is energy-
aware as it has taken the residual energy of sensor node in to consideration while
constructing the aggregation tree. The protocol also balances the network load by
selecting different parent for a node according to the energy level remain in the sensor
node during the aggregation tree construction process. Each parent node performs
aggregation of data packet that it receives from its child nodes and hence the protocol
justifies the in-network aggregation concept.
3.2 System & Energy Model
Consider a homogeneous network of n sensor nodes and a base station or sink
node distributed over a region. The location of the sensors and the base station are fixed
and known priori. Each sensor produces some information as it monitors its vicinity. We
assume that the whole network is divided in to several clusters; each cluster has a cluster-
head (CH). The clustering and the selection of cluster-head (CH) can be done by using
any existing protocol like LEACH, such that cluster-head (CH) is maximum k-hop away
from any node in cluster. We also assume that after the formation of cluster the
transmission power of all nodes is adjusted in such a way that they can perform single
hop broadcast. Single hop broadcast refers to the operation of sending a packet to all
single-hop neighbors [8].
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Data Aggregation in Cluster-based Wireless Sensor Networks
Our energy model for the sensors is based on the first order radio model
described in [17]. A sensor consumes Eelec = 50nJ/bit to run the transmitter or receiver
circuitry and Eamp = 100pJ/bit/m2 for the transmitter amplifier. Thus, the energy
consumed by a sensor i in receiving a l-bit data packet is given by,
ERxi = Eelec . l (1)
while the energy consumed in transmitting a data packet to sensor j is given by,
ETxi,j = Eelec . l + Eamp . di,j2 . l (2)
where di,j is the distance between nodes i and j.
3.3 Protocol Description
In a cluster-based wireless sensor network, our algorithm is designed to provide
energy-aware in-network data aggregation in a cluster. Each cluster uses this algorithm
independently. In a cluster, the nodes can be categorized as: one cluster-head (CH) and
other cluster member node.
Function of cluster-head (CH)
1. Receive a query from base station.
2. Cluster-head (CH) sends configuration packets to all single-hop neighbors.
3. Receive data packets from all single hop neighbors.
4. Finally aggregate the data packets received and route it to base station.
Function of cluster member
1. Receive configuration packets from neighbor nodes.
2. Update and forward configuration packets to all single-hop neighbors.
3. Receive data packets from neighbor nodes.
4. Aggregate all data packets by applying redundancy factor and send it to selected
parent node.
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Data Aggregation in Cluster-based Wireless Sensor Networks
The algorithm works in two phases: Configuration packet flow and Data packet flow that
are described below.
Initial cluster position Flow of configuration packets Flow of data packets
through selected parent
Figure 3.1 A typical scenario of data aggregation in a cluster.
3.3.1 Configuration Packet Flow
Initially cluster-head broadcast configuration packet to all its neighbors.
Configuration packet contains the following fields:
Node Id location of node that each node know in prior
Hop Distance distance from cluster-head in terms of hop count (set zero for CH)
Residual Energy current energy in node
Each node upon receiving the broadcast configuration packet that is originated
from cluster-head adds the sender of the packet in the list of its possible parents with its
node id, hop distance, residual energy. After this the node again broadcast the
configuration packet to all its neighbors by updating node id to its own id, incrementing
hop distance by one and its own residual energy. This process continues until all the
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Data Aggregation in Cluster-based Wireless Sensor Networks
nodes in cluster receive configuration packet. All nodes that broadcast the configuration
packet do so by predefined and common signal strength that is know to all the nodes.
3.3.2 Data Packet Flow
When all nodes receives configuration packets, each node now select the parent to
which it can forward the data packet. The parent selection procedure is shown in fig. 3.2.
Each node looks in to the list of all its possible parents. The neighbor node which
has least hop distance, ie closest to cluster-head, is selected as parent by a node. In case
when two neighbor nodes have the least but equal hop distance, the node checks the
residual energy of two neighbor nodes. The neighbor node that has greater residual
energy is now selected as parent. In both the cases, node also calculate the difference of
residual energy of two neighbor nodes, which have least hop distance, and checks
whether this difference is less than the threshold or not. If it is then the node selects the
parent as usual. But if it is not then the node selects other neighbor node as its parent.
Define: Er[i]: residual energy of node i dh[i]: distance from CH in terms of hop count of node i Ed
ij: difference of residual energy of two nodes i & j te: threshold of residual energy difference of two nodes i & j for balancing load nid: id of a node S: set of configuration packets received by node i ParentSelection(nid)
1 select two nodes j & k such that dh[j] & dh[k] is minimal in S 2 if (dh[j] < dh[k] AND |Ed
jk| < te) 3 then return nid of node j 4 else if (dh[j]==dh[k] AND |Ed
jk| < te) 5 then return nid of node with max(Er[j] , Er[k]) 6 endif 7 else return nid of node k 8 endif
Figure 3.2 Parent selection procedure.
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Data Aggregation in Cluster-based Wireless Sensor Networks
This allows a node that has more available resources to be selected as a parent node. This
also balances the consumption of energy of nodes in the cluster and leads to die out of
nodes nearly at same time.
After selecting the parent node, each node now forwards its data to its parent.
When a parent node receives multiple data packets from its neighbor nodes, it performs
aggregation operation by eliminating redundancy in the data. Each parent node checks
the equation below:
| VNi – VNj | < K (3)
where, VNi data value of node i
VNj data value of node j
K redundancy factor
If this equation satisfies, the parent node perform aggregation by applying any
aggregation functions like MIN, MAX, and AVG on the values of data packet and send
only one packet while discarding other packets. But if this equation do not satisfies, the
parent performs aggregation by simply concatenating two data packet in to one keeping
value of both packets intact.
The selection of value for redundancy factor (K) has a trade-off between precision
and energy consumption. If the application wants more precision, it should select a low
value for redundancy factor otherwise a high value. Selecting high value for K means
sending only one value thus less number of bits needs to be transmitted and hence low
energy consumption.
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Data Aggregation in Cluster-based Wireless Sensor Networks
3.4 Issues
E-BINA significantly reduces the energy consumption of all nodes in the cluster
by reducing the transmission power of all nodes. The one issue that arises in our designed
protocol is that after the formation of cluster and selection of cluster-head, all nodes have
to reduce their transmission power. All nodes have to reduce their transmission power in
such a way that they could only reach their single-hop distance neighbors. This operation
requires some kind of synchronization among all nodes. The nodes have to program
before to perform the above task. For this, the programming task needs little extra effort.
Now when cluster-head received all data packets and aggregated them, it has to now
increase its transmission power so that it can transmit the final aggregated data up in the
cluster-head hierarchy towards the sink. Another issue that remains with any tree based
approach is robustness of the system. In case of failure of any intermediate node in the
tree hierarchy during operation will lead to the loss of data.
Though E-BINA requires all nodes to adjust their transmission power after the
deployment and requires extra effort for programming before, it conserves a significant
amount of energy. So in the presence of the above issue, E-BINA outperforms when we
try to maximize the network lifetime.
3.5 Summary
E-BINA takes the advantageous features of cluster-based and tree-based
approaches.
Instead of sending data directly to cluster-head, nodes form an aggregation tree in
each cluster.
In aggregation tree, the selection of parent is based on two factors: hop distance
and residual energy of node.
Energy model is based on first order radio model.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Protocol requires all nodes to adjust their transmission power.
Protocol defines two types of packets: configuration packet and data packet.
Each node eliminates redundancy in the data by satisfying equation (3).
In-network aggregation is performed during data flow towards cluster-head.
Issue: Adjustment of transmission power after deployment requires extra
programming effort.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Chapter 4
Performance Analysis
In previous chapter we have explained our proposed protocol for data aggregation
in cluster-based wireless sensor networks, called E-BINA. Now in this chapter we are
going to analyses the performance of our protocol. We will show how our protocol
outperforms in terms of energy efficiency in comparison to conventional protocol for data
aggregation in cluster-based wireless sensor networks by presenting simulation analysis
with different simulation parameters taken and results obtained.
4.1 Simulation Analysis
Though many simulation tools are available for wireless sensor networks as
discussed in chapter 2, we have chosen Network Simualtor-2 (NS-2) [20], in particular
NS-2.29.3, as our tool to simulate the proposed protocol.
4.1.1 Simulation Setup
A square field of 160m X 160m is taken where 11 nodes are randomly deployed.
One node is designated as cluster-head (CH) and one node is designated data
source.
Command:
set val(sc) "/root/Desktop/mov1"
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Data Aggregation in Cluster-based Wireless Sensor Networks
set val(cp) ""
#setdest is found in "/root/ns-allinone-2.29/ns-2.29/indep-
utils/cmu-#scen-gen/setdest"
exec ./setdest -n 10 -M 0.1 -p 21 -x 160 -y 160 -t 20 > mov1 &
puts "loadin RANDom sceanrio"
source $val(sc)
The snapshot of node scenario in NAM is shown below. The three colors of node
show the energy levels of nodes. The initial color of nodes is green. When energy drop to
first threshold level the color turns to yellow and when drop to second threshold level the
color turns to red. After this level a node is dead and color is red.
Figure 4.1 Random nodes scenario in NAM.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Energy model is ON. Transmit power, Receive power, Idle power, Sleep power,
Transition power, and Initial Energy of nodes is set accordingly. Also
transmission range is set by controlling the transmit power and receiving
threshold of antenna of nodes. All other parameters are taken default values.
Command:
set val(energymodel) EnergyModel ;# energy model
is on
set val(initialenergy) 1 ;# Initial energy
in Joules
set val(sleeppower) 0.0 ;# sleep power in
Watt
set val(tp) 0.002 ;# transition
power
consumption(Watt)
in state
transition from
sleep to idle
(active)
set val(tt) 0.005 ;# transition
time(second) use
instate
transition from
sleep to idle
(active)
set val(ip) 0.035 ;# idle power
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Data Aggregation in Cluster-based Wireless Sensor Networks
Transmit power, Receive power, transmit power & receiving threshold of antenna
of nodes are set with different values to use different transmission range of nodes and
show the comparison between E-BINA and conventional protocol.
Case 1: E-BINA
set val(rxPower) 0.395 ;# receive
power in Watt
set val(txPower) 0.66 ;# transmit
power in Watt
Phy/WirelessPhy set Pt_ 8.5872e-4 ;# 40m
Phy/WirelessPhy set RXThresh_ 3.66152e-10
Case 2: Conventional protocol
set val(rxPower) 1.0 ;# receieve
power in Watt
set val(txPower) 2.0 ;# transmit
power in Watt
Phy/WirelessPhy set Pt_ 7.214e-3 ;# 100m
Phy/WirelessPhy set RXThresh_ 3.65209e-10
Other values that could be used are:
#Phy/WirelessPhy set Pt_ 1.33826e-3 ;# Transmission
range 50m,
#Phy/WirelessPhy set Pt_ 0.281838 ;# 250m
The value of RXThresh_ is obtained by executing threshold.cc defined in "/root/ns-
allinone-2.29/ns-2.29/indep-utils/propogation”. Snapshot is given below:
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Data Aggregation in Cluster-based Wireless Sensor Networks
Figure 4.2 Snapshot of console
Some other parameters used are:
Channel Type Wireless channel
Propagation Model Two Ray Ground
MAC Type 802.11
Network Interface Type Phy/WirelessPhy
Interface Queue Type Queue/DropTail/PriQueue
Antenna Model Antenna/OmniAntenna
Routing Protocol AODV
Simulation Time 20 sec
Parameters set for data transfer are:
Cluster-head node 10 with UDP agent attached
Source node node 0 with UDP agent attached
Traffic Type CBR with a rate of 5 packets / second
Packet Size 136 bytes
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Data Aggregation in Cluster-based Wireless Sensor Networks
4.1.2 Simulation Run
Case 1: E-BINA
For E-BINA we set transmission range of 40m such that a node sends its data to its
single-hop neighbor and data is forwarded in a multi-hop fashion. Figure 4.3 shows the
data transfer between node 0 and node 10. Node 1 and 2 are relay nodes. Since the
transmission range is set to 40m, node 0 can only send its data to node 1 and so other
nodes.
Figure 4.3 Data transfer between node 0 and 10 through node 1 & 2.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Figure 4.4 shows that after some time energy level of node 2, 1, 0, and 10 dropped to first
threshold level and hence the color of nodes turns to yellow.
Figure 4.4 Energy level of nodes drop to first threshold.
Figure 4.5 shows that after some more time energy level of node 1, 2, 0, and 10 dropped
to second threshold level and hence the color of nodes turns to red.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Figure 4.5 Energy level of nodes drop to second threshold.
Case 2: Conventional Protocol
In conventional method, all nodes in a cluster send their data directly to cluster-head. For
this reason we set transmission range of nodes to be 100m so that source node 0 can send
data directly to node 10. To able to have a large transmission range the transmitting and
receiving power of nodes are more than double of as in case 1.
The data transfer start between node 0 and node 10 directly. As happened in last case,
again after some time energy level of node 0 and node 10 decreased to first threshold
level and color of nodes change from green to yellow and then energy level of both nodes
go down to second threshold level and nodes turn to red.
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Data Aggregation in Cluster-based Wireless Sensor Networks
4.1.3 Simulation Results
A. Conserving Energy
We determine residual energy of the source node, which is defined as the remaining
energy of a node and considered that as the metric to prove energy efficiency of our
proposed protocol. We used this metric to show the impact of transmission power on
energy reduction. Figure 4.9 shows the significant reduction in energy consumption by
using E-BINA when compared with conventional protocol. This shows the benefit of
sending data in a multi-hop fashion towards cluster-head.
E-BINA Conventional protocol
Figure 4.6 Residual energy of source as a function of time.
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Data Aggregation in Cluster-based Wireless Sensor Networks
B. Throughput
We have also measured the throughput of the receiving node i.e. cluster-head node 10 in
our scenario for both the cases. Throughput of a node is defined as the average rate of
successful message delivery over a communication channel. Figure 4.10 show that E-
BINA achieves high throughput in comparison with conventional protocol.
E-BINA Conventional protocol
Figure 4.7 Throughput as function of time.
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Data Aggregation in Cluster-based Wireless Sensor Networks
C. Network Density
By considering the changes in the network density, we also study the relationship
between the network lifetime and network density. In our experiment we have considered
the change in the residual energy of source node i.e. node 0 in the end of simulation. The
density of network is calculated via equation [9]:
λ = NπR2 / A2
Where, N is sensor number,
R is sensor range,
A is sensor area.
By keeping network area constant and increasing the number of nodes, we have increased
network density. Due to increase in the network density, the hop count between source
node and sink node also increases. When hop count increases node now transmit data to
nearer node with less transmit power and hence consume less energy. Figure 4.8 shows
the increase in the residual energy when we increase the hop count. We have taken N as
11, 21, 31, 41, and 51.
Effect of Network Density
00.050.1
0.150.2
0.250.3
0.350.4
3 4 5 6 7
Hop Count
Resi
dual
Ene
rgy
Figure 4.8 Effect of network density
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Data Aggregation in Cluster-based Wireless Sensor Networks
D. Packet Delivery Ratio
Besides examining the network lifetime extension roughly via energy saving, we also
evaluate the network efficiency influenced by E-BINA. Here, we measure the efficiency
in term of data delivery ratio, which is defined as the number of received packets divided
by the number of sent packets for a certain time period. From our simulation results
illustrated in figure 4.9, we find that this ratio does not change much while the network is
alive. It shows the stable performance of our protocol. When the network energy is
running out, the data delivery ratio collapses rapidly. This phenomenon probably can be
taken as a sign of the network death.
PDR
00.10.20.30.40.50.60.70.80.9
1
5 10 15 20
simulation time
pack
et d
eliv
ery
ratio
Figure 4.9 Packet delivery ratio of the network.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Chapter 5
Conclusion & Future Work
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 aggregation
protocols not only provide energy conservation but also remove redundancy in the data
and hence provide useful data only. There exist several protocols for data aggregation
which uses different approaches to provide energy efficiency. In cluster-based
approaches, nodes send their data directly to cluster-head and cluster-head then aggregate
and forward the data towards sink. We exploited this approach and proposed a new
protocol called Energy-aware Balanced In-Network Aggregation (E-BINA).
E-BINA uses the advantageous features of cluster-based and tree-based
approaches. E-BINA requires a wireless sensor network which is divided in to several
clusters, each having a cluster-head. Each cluster then uses E-BINA independently and
avoids aggregation only at cluster-head by constructing an aggregation tree rooted at
cluster-head. During the construction of aggregation tree, each cluster member node
chooses its parent node among its neighbors based on the information of residual energy
and hop distance from cluster-head. After the construction of aggregation tree, when a
parent node receives data from its different child neighbor nodes, it eliminates the
redundancy in the data received from different nodes and then forward. The difference
between E-BINA and other cluster-based approach lie in the reduction of transmission
power of node as in E-BINA a node send data to its neighbor node instead of sending to
cluster-head. This is the main performance improvement factor of our protocol.
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Data Aggregation in Cluster-based Wireless Sensor Networks
The simulation result shows that when the data from source node is send to
cluster-head through neighbors nodes in a multi-hop fashion by reducing transmission
and receiving power, the energy consumption is low as compared to that of sending data
directly to cluster-head.
Future work will focus on the implementation of E-BINA in NS-2 as a separate
module so that it could be tested more accurately. As we have already tested the effect of
reduction of transmission power on the energy consumption and we got positive result.
After implementing in NS-2, we will measure the whole network lifetime, packet
delivery ratio and effect of network density. Also the effect of redundancy factor on
energy consumption and overall performance of our protocol will be measured.
Enhancing E-BINA by introducing an effective compression technique for data is also the
part of future work.
Apart from working on E-BINA, future work also includes the extension of NS-2
for wireless sensor networks. Though some extensions are available but none of them
work properly and efficiently. Developing a framework for wireless sensor network in
NS-2 helps us to test our protocol more accurately; other protocol as well. It will provide
user a more realistic model for simulating a scenario.
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Data Aggregation in Cluster-based Wireless Sensor Networks
Appendix A
The Network Simulator version 2 (NS-2) is a deterministic discrete event network
simulator, initiated at the Lawrence Berkeley National Laboratory (LBNL) through the
DARPA funded Virtual InterNetwork Testbed (VINT) project. The VINT project is
collaboration between the Information Sciences Institute (ISI) at the University of
Southern California (USC), Xerox's Palo Alto Research Center (Xerox PARC),
University of California at Berkeley (UCB) and LBNL [20].
NS-2 was initially created in 1989 as an alternative to the REAL Network
Simulator. Since then there is significant growth in uses and width of NS project.
Although there are several different network simulators available today, ns-2 is one of the
most common. NS-2 differs from most of the others by being open source software,
supplying the source code for free to anyone that wants it. Whereas most commercial
network simulators will offer support and a guarantee but keeping the moneymaking
source code for themselves. The release of the source code helps users to create their own
functions and subprograms, but also makes it easier to implement them into the ns-2
environment. One of the main benefits for the ns project group releasing the source code
is that independent researchers can help in the development of ns-2. It is fairly common
that a researcher contributes with the code of a non-implemented protocol or algorithm,
after constructing it for his studies.
It should be noted that NS-2 is a research progressive effort and not a kind of
commercial software release. The difference is that there are very few people in the ns
project group compared to ordinary software, leading to difficulties in supporting all the
users. That problem has lead to the solution of having a huge mailing list
(http://mailman.isi.edu/mailman/listinfo/ns-users) for anyone interested, as well as a
complete archive of all the mails ever been sent to this mailing list. The mailing list is
based on the idea of user helping user, taking the load of the ns project group. The
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Data Aggregation in Cluster-based Wireless Sensor Networks
mailing list and the archives are a huge help for all users of ns-2, no matter old or new,
since usually someone else has had the same problem before.
Another important thing to note is that NS-2 is an ongoing progressive project and
hence can not be considered as a complete product. This is the reason why it is free of
cost and only offers the mailing lists as support. The people that are in charge of the
project heavily rely on the users to find bugs and faults and reporting these when
discovered. This also leaves the validating of results to the user, but the user is not alone
so help is just an email away. The most commonly used protocols are so well
implemented and checked so the main concerns are the new implementations. New
implementations usually start out as a research assignment not linked to the ns project
group. Since the project group does not have a full company helping them in verification
and implementation they have no possibility to do everything themselves thus
encouraging any help they can get.
A 1 The NS-2 structure
NS-2 is made up of hundreds of smaller programs, separated to help the user sort
through and find what he or she is looking for. Every separate protocol, as well as
variations of the same, sometimes has separate files. Though some are simple, but still
dependent on the parental class [20].
Figure A.1 The basic structure of NS-2
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Data Aggregation in Cluster-based Wireless Sensor Networks
A 1.1 C++
C++ is the predominant programming language in ns-2. It is the language used for
all the small programs that make up the ns-2 hierarchy. C++, being one of the most
common programming languages and specially designed for object- oriented coding, was
therefore a logical choice what language to be used. This helps when the user wants to
either understand the code or do some alterations to the code. There are several books
about C++ and hundreds, if not thousands, of pages on the Internet about C++
simplifying the search for help or answers concerning the ns-2 code.
A 1.2 OTcl
Object Tcl (OTcl) is object-oriented version of the command and syntax driven
programming language Tool Command Language (Tcl). This is the second of the two
programming languages that NS-2 uses. The front-end interpreter in NS-2 is OTcl which
link the script type language of Tcl to the C++ backbone of NS-2. Together these two
different languages create a script controlled C++ environment. This helps when creating
a simulation, simply writing a script that will be carried out when running the simulation.
These scripts will be the formula for a simulation and is needed for setting the
specifications of the simulation itself. Without a script properly defining a network
topology as well as the data-rows, both type and location, nothing will happen. For a
more in depth presentation of these scripts, it is better to have a closer look at the
introduction and related chapters in the NS-2 manual.
A 1.3 Nodes
A node is exactly what it sounds like, a node in the network. A node can be either
an end connection or an intermediate point in the network. All agents and links must be
connected to a node to work. There are also different kinds of nodes based on the kind of
network that is to be simulated. The main types are node and mobile node, where node is
used in most wired networks and the mobile node for wireless networks. There are
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Data Aggregation in Cluster-based Wireless Sensor Networks
several different commands for setting the node protocols to be used, for instance what
kind of routing is to be used or if there is a desire to specify a route that differs from the
shortest one. Most of the commands for node and mobile node can be easily found in the
ns documentation. Nodes and the closely connected link creating commands, like simplex
link and duplex link, could be considered to simulate the behavior of both the Link Layer.
A 1.4 Agents
An agent is the collective name for most of the protocols you can find in the
transport layer. In the ns-2 documentation agents are defined as the endpoints where
packets are created and consumed. All the agents defined in ns-2, like tcp, udp etc., are
all connected to their parent class, simply called Agent. This is where their general
behavior is set and the offspring classes are mostly based on some alterations to the
inherent functions in the parent class. The modified functions will overwrite the old and
thereby change the performance in order to simulate the wanted protocol.
A 1.5 Applications
The applications in ns-2 are related to the Application Layer in the TCP/IP suite.
The hierarchy here works in the similar way as in the agents case. To simulate some of
the most important higher functions in network communication, the ns-2 applications are
used. Since the purpose of ns-2 is not to simulate software, the applications only
represent some different aspects of the higher functions. Only a few of the higher layer
protocols has been implemented, since some are quite similar when it comes to using the
lower functions of the TCP/IP stack. For instance there is no use adding both a SMTP
and a HTTP application since they both use TCP to transfer small amounts of data in a
similar way. The only applications incorporated in the release version of ns-2 are a
number of different traffic generators for use with UDP and telnet and FTP for using
TCP. All the applications are script controlled and when concerning the traffic
generators, you set the interval and packet-size of the traffic. FTP can be requested to
send a data packet whenever the user wants to, or to start a transfer of a file of an
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Data Aggregation in Cluster-based Wireless Sensor Networks
arbitrary size. If starting an FTP transmission and not setting a file-size the transmission
will go on until someone calls a stop.
A 1.6 NAM
The Network Animator NAM is a graphic tool to use with ns-2. It requires a nam-
tracefile recorded during the simulation and will then show a visual representation of the
simulation. This will give the user the possibility to view the traffic packet by packet as
they move along the different links in the network. NAM offers the possibility of tracing
a single packet during its travel and the possibility to move the nodes around for a user to
draw up his network topology according to his own wishes. Since the simulation has
already been performed there is no possibility for the user to change the links or any other
aspect of the simulation except the representation. The existence of an X-server allows
NAM to be able to open a graphical window. Therefore if NAM is to work, there must be
a version of X-server running.
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Data Aggregation in Cluster-based Wireless Sensor Networks
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