chapter 3 hierarchical energy tree based routing...
TRANSCRIPT
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CHAPTER 3
HIERARCHICAL ENERGY TREE BASED ROUTING
ALGORITHM FOR WIRELESS SENSOR NETWORKS
A novel routing algorithm called HETRA is proposed in this
research. This chapter discusses the proposed Routing Algorithm HETRA in
detail and analyses how this algorithm could be energy efficient by improving
the overall NLT. The proposed routing algorithm uses a HET, which is
constructed using the available energy in each node of the network. The aim
is to have a routing algorithm with an improved energy efficiency and
prolonged NLT with proper congestion control. A novel congestion control
algorithm called exponential congestion control algorithm is also proposed
and is discussed in Chapter 4. The combination of the proposed routing
algorithm and congestion control algorithm is simulated and experimented
using ns2 and the performance of the combination is compared with other
combinations of Routing algorithms such as AODV, DSDV and DSR and
congestion control algorithms such as TCP/Tahoe, Reno, New Reno, MIMD
and PIPD. This performance is evaluated using the simulated WSN with fixed
node distribution patterns.
3.1 WIRELESS SENSOR NETWORKS
WSN involves more and more into our life. The importance is
increased due to the massive development on the hardware and
communication abilities. This increases demand from the clients (companies,
governments and organizations) to provide a better or new solution for their
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applications. The target solution will save resources, human life, cut down the
cost, get better management and provide new services. But still, the solution
has not been reached completely. For all of these reasons, WSN is one of the
hottest research areas and needs an enormous research effort.
3.2 ROUTING ALGORITHMS FOR WIRELESS SENSOR
NETWORKS
Due to the recent technological advances, manufacturing of small
and low cost sensors has become technically and economically feasible. The
sensing electronics measure ambient conditions related to the environment
surrounding the sensor and transform them into an electric signal. Processing
such a signal reveals some properties about objects located and/or events
happening in the vicinity of the sensor. A large number of these disposable
sensors can be networked in many applications that require unattended
operations. A Wireless Sensor Network (WSN) contains hundreds or
thousands of these sensor nodes. These sensors have the ability to
communicate either among each other or directly to an external base-station.
A greater number of sensors allow for sensing over larger geographical
regions with greater accuracy. Basically, each node comprises sensing,
processing, transmission, mobilizer, position finding system and power units
(some of these components are optional like the mobilizer). Sensor nodes are
usually scattered in a sensor field, which is an area where the sensor nodes are
deployed. The deployment of these sensor nodes plays a vital role in the
overall performance of the WSN.
Sensor nodes coordinate among themselves to produce high-quality
information about the physical environment. Each sensor node bases its
decision on its mission, the information it currently has, and its knowledge of
its computing, communication, and energy resources. Each of these scattered
sensor nodes has the capability to collect and route data either to other sensors
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or to an external BS(s). A BS may be a fixed node or a mobile node capable
of connecting the sensor network to an existing communication networks.
Networking unattended sensor nodes may have a profound effect on the
efficiency of many military and civil applications such as target field imaging,
intrusion detection, weather monitoring, security and tactical surveillance,
distributed computing, detecting ambient conditions such as temperature,
movement, sound, light, or the presence of certain objects, inventory control,
and disaster management. Deployment of a sensor network in these
applications can be in a random fashion (e.g., dropped from an airplane) or
can be planted manually (e.g., fire alarm sensors in a facility). For example, in
a disaster management application, a large number of sensors can be dropped
from a helicopter. Networking these sensors can assist rescue operations by
locating survivors, identifying risky areas and making the rescue team more
aware of the overall situation in the disaster area. But the applications like fire
alarm sensors in a building may be manually placed. The placement of these
sensor nodes may follow a certain pattern in order to have a better coverage of
the entire sensor field with minimum number of sensor nodes.
In the past few years, an intensive research that addresses the
potential of collaboration among sensors in data gathering and processing and
in the coordination and management of the sensing activity with random and
fixed node placement are conducted. However, sensor nodes are constrained
in energy supply and bandwidth. Thus, innovative techniques that eliminate
energy inefficiencies that would shorten the lifetime of the network are highly
required. Such constraints combined with a typical deployment of a large
number of sensor nodes in some node distribution patterns pose many
challenges to the design and management of WSNs and necessitate energy-
awareness at all layers of the networking protocol stack. For example, at the
network layer, it is highly desirable to find methods for energy-efficient route
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discovery and relaying of data from the sensor nodes to the BS so that lifetime
of the network is maximized.
Routing in WSNs is very challenging due to the inherent
characteristics that distinguish these networks from other wireless networks
like MANETs or cellular networks. First, due to the relatively large number of
sensor nodes, it is not possible to build a global addressing scheme for the
deployment of a large number of sensor nodes as the overhead of
Identification (ID) maintenance is high. Thus, traditional IP-based protocols
may not be applied to WSNs. Furthermore, sensor nodes that are deployed in
an ad hoc manner need to be self-organizing as the ad hoc deployment of
these nodes require the system to form connections and cope with the
resultant nodal distribution especially that the operation of the sensor
networks is unattended. Sometimes in WSNs, getting the data is more
important than knowing the IDs of which nodes sent the data. Second, in
contrast to typical communication networks, almost all applications of sensor
networks require the flow of sensed data from multiple sources to a particular
BS. This, however, does not prevent the flow of data to be in other forms
(e.g., multicast or peer to peer). Third, sensor nodes are tightly constrained in
terms of energy, processing, and storage capacities. Thus, they require careful
resource management. Fourth, in most applications, nodes in WSNs are
generally stationary after deployment except for, may be, a few mobile nodes.
Nodes in other traditional wireless networks are free to move, which results in
unpredictable and frequent topological changes. However, in some
applications, some sensor nodes may be allowed to move and change their
location (although with very low mobility).
Fourth, sensor networks are application specific, i.e., design
requirements of a sensor network change with application. For example, the
challenging problem of low-latency precision tactical surveillance is different
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from that required for a periodic weather-monitoring task. Fifth, position
awareness of sensor nodes is important since data collection is normally based
on the location. Currently, it is not feasible to use GPS hardware for this
purpose. Finally, data collected by many sensors in WSNs is typically based
on common phenomena, hence there is a high probability of this data having
some redundancy. Such redundancy needs to be exploited by the routing
protocols to improve energy and bandwidth utilization. Usually, WSNs are
data-centric networks in the sense that data is requested based on certain
attributes, i.e., attribute-based addressing. An attribute-based address is
composed of a set of attribute-value pair query. For example, if the query is
something like [temperature > 60F], then sensor nodes that sense temperature
> 60F only need to respond and report their readings.
Due to such differences, many new algorithms are being proposed
for the routing problem in WSNs. These routing mechanisms must take into
consideration the inherent features of WSNs along with the application and
architecture requirements. The task of finding and maintaining routes in
WSNs is nontrivial since energy restrictions and sudden changes in node
status (e.g., failure) cause frequent and unpredictable topological changes. To
minimize energy consumption, many routing techniques are being proposed
for WSNs that employ some well-known routing tactics as well as tactics
special to WSNs, e.g., data aggregation and in-network processing, clustering,
different node role assignment, node placement patterns and data-centric
methods are employed. Almost all of the routing protocols can be classified
according to the network structure as flat, hierarchical, or location-based.
Furthermore, these protocols can be classified into multipath-based, query-
based, negotiation-based, QoS-based, and coherent-based depending on the
protocol operation. In flat networks, all nodes play the same role while
hierarchical protocols aim at clustering the nodes so that cluster heads can do
some aggregation and reduction of data in order to save energy. Location-
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based protocols utilize the position information to relay the data to the desired
region rather than to the whole network. The last category includes routing
approaches that are based on the protocol operation, which vary according to
the approach used in the protocol.
Distributed WSN consist of a large number of small, low-cost and
low-power nodes that coordinate with one another for environmental sensing.
The sensor nodes are severely restricted in power, memory and computational
resources.
The nodes can be densely deployed in close proximity to the
phenomenon to be observed. They can be deployed in hostile environments
where the nodes may not be physically accessible and are subject to
tampering. Nodes can be added to and deleted from the network at any time,
resulting in unpredictable changes to the topology of the network. This
research work presents a routing protocol for sensor networks to improve the
energy efficiency and hence the NLT.
Network lifetime is a novel performance metric which is derived in
need to evaluate the networks that are composed of nodes with non-
replenishable energy sources. WSNs are the primary examples of such
networks, in which the main concern is elongating the network lifetime.
Optimal WSN design is highly dependent on the application
scenario context. Correct quantification of the application specific network
lifetime is a must to further optimize the design. The focus is also given on
proposing some better schemes of node placement to increase the energy
efficiency and hence the NLT.
To realistically and correctly quantify the lifetime, we propose a
utility based lifetime measurement parameter called DANLT Product. With
this parameter, a more representative lifetime metric which maps the complete
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network behavior into a numeric value is obtained. This is in contrast with
metrics which focus solely on certain milestones of the network functionality
to quantify the lifetime which include the first node death and the last node
death.
3.3 DESIGN ISSUES OF ROUTING PROTOCOLS
Initially WSN was mainly motivated by military applications. Later
on, the civilian application domain of WSN has been considered, such as
environmental and species monitoring, production and healthcare, smart home
etc. These WSNs may consist of heterogeneous and mobile sensor nodes, the
network topology, may be as simple as a star topology; the scale and density
of a network varies depending on the application. To meet this general trend
towards diversification, the following important design issues of the sensor
network have to be considered.
3.3.1 Fault Tolerance
Some sensor nodes may fail or be blocked due to lack of power,
have physical damage or environmental interference. The failure of sensor
nodes should not affect the overall task of the sensor network. This is the
reliability or fault tolerance issue. Fault tolerance is the ability to sustain
sensor network functionalities without any interruption due to sensor node
failures. In fixed node distribution patterns proposed in chapter 6, the NLT
could be extended as the nodes are evenly utilized.
3.3.2 Scalability
The number of sensor nodes deployed in the sensing area may be in
the order of hundreds, thousands or more and routing schemes must be
scalable enough to respond to the events.
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3.3.3 Production Costs
Since the sensor networks consist of a large number of sensor
nodes, the cost of a single node is very important to justify the overall cost of
the networks and hence the cost of each sensor node has to be kept low.
3.3.4 Operating Environment
A sensor network can be set up in the interior of a large machinery,
at the bottom of an ocean, in a biologically or chemically contaminated field,
in a battle field beyond the enemy lines, in a home or a large building, in a
large warehouse, attached to animals, attached to fast moving vehicles, in
forest areas for habitat monitoring etc (Mainwaring et al 2002). Hence, the
environment is also an important design parameter for the WSN.
3.3.5 Energy Efficiency
Since the transmission power of a wireless radio is proportional to
the distance squared or even higher order in the presence of obstacles, multi-
hop routing will consume less energy than direct communication. This RF
power is the dominant energy among all the energy consumed by a sensor
node. However, multi-hop routing introduces significant overhead for
topology management and medium access control (Wu et al 2000). Direct
routing would perform well enough if all the nodes were very close to the
sink. Sensor nodes are equipped with limited power source (<0.5 Ah 1.2V).
Node lifetime and hence, the NLT is strongly dependent on its battery
lifetime.
3.3.6 Data Aggregation/Fusion
Since sensor nodes might generate significant redundant data,
similar packets from multiple nodes can be aggregated so that the number of
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transmissions would be reduced. Data aggregation is the combination of data
from different sources by using functions such as suppression (eliminating
duplicates), min, max and average. As computation would be less energy
consuming than communication, substantial energy savings can be obtained
through data aggregation. This technique has been used to achieve energy
efficiency and traffic optimization in a number of routing protocols.
3.3.7 Quality of Service (QoS)
The QoS means the quality service required by the application. It
could be the length of life time, the data reliable, energy efficiency, and
location-awareness and collaborative-processing. These factors will affect the
design or selection of routing protocols for a particular application. In some
applications, (e.g. some military applications) the data should be delivered
within a certain period of time from the moment it is sensed.
3.3.8 Node Placement Patterns
Node placement pattern is application dependent and affects the
performance of the routing protocol. The placement of nodes is either
deterministic / fixed node distribution pattern or self-organizing. In
deterministic / fixed node distribution patterns, the sensors are manually
placed and data is routed through pre-determined paths. However in
self-organizing systems, the sensor nodes are scattered randomly creating an
infrastructure in an Ad-hoc manner. In that infrastructure, the position of the
sink or the cluster head is also crucial in terms of energy efficiency and
performance. When the distribution of nodes is not uniform, optimal
positioning of cluster head becomes a pressing issue to enable energy efficient
network operation.
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This research work proposes a routing algorithm that takes care of
remaining energy in each sensor node. This algorithm is presented in the next
section. This algorithm is further augmented by the congestion control
algorithm for reducing the number packet drops and hence the number of
retransmissions. This reduction in retransmissions will save much energy and
hence will extend the overall NLT. The proposed algorithm TCP/Exp is
presented and analysed in chapter 4. Further, the fixed node distribution
patterns which could improve the network performance are discussed in
chapter 5.
3.4 HIERARCHICAL ENERGY TREE BASED ROUTING
ALGORITHM FOR WIRELESS SENSOR NETWORKS
The HETRA extends the set of parameters used for taking a routing
decision with the inclusion of the energy remaining in the nodes. Energy is an
absolute value measured in watt-hour. In addition to the energy used as
routing metric, they are also used to place the nodes in a hierarchical tree of
three levels of nodes. This is used to categorize the links connecting these
nodes as unidirectional or bidirectional. Nodes include such parameters as
periodic location update packets, which in our implementation corresponds to
an overhead of two octets added to the header.
In order to increase path robustness with efficient energy
utilization, the proposed routing metric favors relatively stable paths. To this
aim, we use the distance from the destination and energy remaining in each
node. The distance component chooses forwarding nodes located closer to the
destination, but considering that the next hop must be within the transmission
range and preferably not too close to its perimeter. Energy that remains in
each node after the transmission of a packet is the other metric used for
routing. Higher the remaining energy, more probable is the participation of
the node in forwarding the packet.
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3.4.1 HET
HET is constructed by placing each node of the sensor network in
one of the two levels. For each node, based on the energy update information
received from neighbors, a level is fixed in the HET. One HET is constructed
for the entire network and a copy this is sent to each node during update
operation. The destination node is kept as the root node (level 0) of the tree.
All the remaining nodes are divided into two categories based on their
remaining energy. A threshold of 25% of the initial energy is used to evaluate
the energy level in HET. If the node has energy above the threshold, they are
placed in level 1. The remaining nodes are placed in level 2 as shown in
Figure 3.1.
Figure 3.1 Hierarchical Energy Tree
Nodes in level 1 are assumed with bidirectional links and nodes in
level2 are assumed with unidirectional links. The level 2 nodes are attached to
the nodes at level 1 if they are the neighbors. So HET after construction will
contain the information of the energy level, link characteristics and
neighborhood details. The HET so constructed is used along with the routing
metric to determine the routing. The HET construction algorithm is given
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below. The construction of HET is explained with an example in the next
section.
Input : Number of Sensor Nodes n;
Initial Energy Level Ei;
Destination Node D(p);
position of node p;
Node identification id;
List of nodes L{SNi (id,E,p)};
HET_construct()
{
HET_initiate(L0{}, L1{}, L2{});
for (every t sec)
{
Energy_request (L{SNi (id,E,p)});
HET_update ( L1{}, L2{});
}
}
HET_initiate(L0{}, L1{}, L2{})
{
Add D(p) L0{};
for i= 1 to n
{
Add SNi L1{};
Set E(SNi) = Ei;
Link SNi D(p);
}
Set L2{} NULL;
}
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HET_update (L0{}, L1{}, L2{})
{
for each node in L1{}
{
if (E(SNL1) < ET
then {
X = Neighbour(SNL1);
Link SNL1 X;
L1{} L1{} – SNL1;
L2{} L2{} + SNL1;
}
}
3.4.2 Using HET
HETRA is a reactive energy-driven routing protocol and only those
nodes having sufficient energy (level 1 nodes in HET) requests information
for making routing decisions. A node broadcasts a beacon-request packet to
neighbors seeking location information. In response, a neighbor node sends
back a beacon including its location via either a broadcast packet or a unicast
packet as specified in the request packet. Bidirectional or unidirectional
neighbors are discovered by the energy that remains in the nodes. Level 2
nodes have energy less than a threshold and hence they are assigned with
unidirectional links. Level 1 nodes have energy more than the threshold and
hence they are assigned with bidirectional links.
The Level 2 nodes have very small energy. They are restricted to
participate in receiving only. These nodes consume less energy for receiving.
When they are made to involve for transmission also, the energy will get
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reduced faster and will result into complete drain out of the entire energy.
This may result into the network split. The network split will lead to the
complete die out of the network. Hence preserving some energy with the
nodes will avoid network split. These Level 2 nodes may be used for both
transmission and reception during emergency situation like earth quake etc.
As the earth quake may damage many of the Level 1 nodes, the Level 2
nodes may be used for transmission and reception. Thus the Level 2 nodes
increase the efficiency of the network, by providing better connectivity for
transmission of sensed data needed for avoiding more damages or emergency
situations.
A forwarding node is always trying to select the neighbor closest to
the destination as the next hop. The neighbor information is obtained from the
HET. Forwarding fails when reaching a node which has no neighbors closer
to the destination. Then the packet is forwarded using the tree constructed
based on neighbor information of bidirectional links.
An example WSN is assumed to explain the construction and
updating of the Hierarchical Energy Tree. The WSN assumed is shown in
Figure 3.2. The Network consists of 14 sensor nodes, 1 base station node and
a phenomenon node. The Sensor nodes are intended for sensing and
transmitting the sensed data to the data collecting centre. Here the base station
node is the data collecting centre. The phenomenon node is used to simulate
the physical phenomenon to be sensed and reported. This phenomenon node
is made to move across the entire sensing field. This movement simulates the
situation that the physical phenomenon is occurring at different parts of the
sensing field. The sensor nodes are initially assumed with energy of
0.5 Joules.
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Figure 3.2 Example of WSN
After a data is sensed by the sensor node, it is transmitted towards
the base station node through the intermediate nodes. The data packet will
reach the final destination i.e. the base station node after passing through the
intermediate nodes. The nodes which are involved in forwarding the data
packets will consume the energy available with the sensor nodes for receiving
and retransmitting the data packets towards the base station node. Initially the
HET is constructed with the base station node as the root node and all the
nodes of the WSN as the nodes in Level 1 nodes.
In order to form the HET and update the HET after every
transmissions and receptions, the remaining energy in each node is obtained
by getting the information about the remaining energy using the route
discovery packets send from the base station node to all the nodes. The
wireless links used to transmit the route discovery packet is shown in
Figure 3.3 HET is updated through reply packets and the remaining energy
information.
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Figure 3.3 Wireless links used to transmit the route discovery packet
All the sensor nodes shown in Figure 3.3 are assumed with an
initial energy of 0.5 Joules. It is also assumed that the energy required for
sensing and transmission of each data packet is 0.025 Joules. The energy
required for receiving and internal processing is very small compared to the
energy required for transmission. Figure 3.4 shows the initial energy
configuration of a HET formed for the WSN shown in Figure 3.3. As the
initial energy of all the nodes before starting the process of sensing and
transmission is the same, i.e. 0.5 Joules, all the nodes are assigned to the
Level 1 in HET and are attached to the base station node as shown in
Figure 3.4.
Suppose the node 1 senses the data and transmits it through nodes 2
and 3 to the base station node, their energy level is reduced by 0.025 Joules
(as all the 3 nodes are involved in transmission) and becomes 0.475 Joules.
This energy level is still above the threshold level of 0.125 Joules, the
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nodes 1, 2 and 3 are assigned to the same level 1 and attached to the base
station node. If the network senses a large number of data and transmits data
packets, then more number of sensor nodes are involved in transmission and
correspondingly their energy level gets reduced.
The energy level information of these nodes are updated at the base
station node by the reply packets. If more number of data packets are
transmitted through node 3, then its energy level will be reduced below the
threshold energy level of 0.125 Joules. At this stage, node 3 is assigned to
level 2. As the node 3 is neighbor to node 2, node 3 is attached to node 2 in
the updated configuration of HET. Similarly, if node 8 and node 13 are
involved in more number of data transmissions, their energy level gets
reduced below the threshold level. AS node 8 is neighbor to node 9 and node
13 is neighbor to node 14, they are attached to node 9 and 13 respectively and
assigned to level 2. This updated energy level configuration is shown in
Figure 3.5.
Figure 3.4 Initial energy configuration of a HET
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Figure 3.5 Updated HET after some transmission
Further, sensed data packet transmissions may reduce the energy
remaining in each node. This will change the status of the nodes and change
some more nodes from level 1 to level 2. The updated HET may be as shown
in Figure 3.6.
Figure 3.6 Updated HET after some transmission
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These figures are referred by the sensor nodes to decide whether the
nodes are used for receiving only (Level 2 nodes) or for both receiving and
transmitting (Level 1 nodes).
3.5 IMPLEMENTATION OF HETRA IN ns2
HETRA is implemented and added as a novel wireless sensor
network routing protocol in ns2. Since ns2 is an open source, any addition
can be incorporated to ns2 easily. The procedure to add new routing protocols
is detailed in ns manual (Simulator 2000). Three new programs that
incorporate the structure of hetra are added to the ns2. These are hetra.cc,
hetra.h and hetra.tcl. The program hetra.h includes basic header files needed
for constructing a node with all basic attributes, to make the node a mobile
node with the necessary attributes. These header files are already defined in
ns2.
The program hetra.cc adds additional features such as constructing
the hierarchical tree using the remaining energy of each node. The routing
used is fixed routing, but the hierarchical structure is constructed every 2 sec
in the simulation performed. But in practical applications, the refreshing time
for changing the hierarchical tree construction may be in the order of minutes.
This is due to the nature of application as the changes in sensed data may vary
slowly and hence the change in energy consumed is also low.
The Program hetra.tcl provides the necessary mobility features
needed for the mobile ad hoc nodes. This program also decides the
characteristics of transmitter and receiver. This in addition to the routing
provided by the hetra.cc defines the energy consumption characteristics of the
HETRA nodes.
After including these files, the ns2 is reinstalled so that these files
get compiled and included in the basic routing algorithms already defined in
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ns2. The routing algorithm may be called by the tcl program very similar to
DSDV, AODV and DSR. HETRA is the name to be used to call the new
routing algorithm.
3.6 COMPARISON OF HETRA WITH DSDV, DSR, AODV
MANETS and sensor networks are two classes of wireless ad hoc
networks with resource constraints. MANETS typically consist of devices that
have high capabilities, mobile and operate in coalitions. Sensor networks are
typically deployed in specific geographical regions for tracking, monitoring
and sensing. Both these wireless networks are characterized by their ad hoc
nature that lack pre-deployed infrastructure for computing and
communication.
A great variety of routing protocols have been developed especially
for WSN. On the other hand ad-hoc routing protocols are also being
developed in a slightly different context under the general heading MANET.
Even though there are different restrictions and requirements for these two
types of networks, ad-hoc routing protocols actually developed for MANETs
could be adapted to WSNs or at least deserve to be discussed in that context.
AODV, DSDV and DSR are the protocols that are taken into consideration
for adoption in WSN. Hence, in evaluating the performance of the HETRA
routing algorithm, an analysis is performed by comparing the performance of
HETRA with the algorithms AODV, DSDV and DSR.
3.7 SIMPLE SIMULATION OF WSN WITH HETRA USING ns2
The HETRA routing algorithm once implemented in ns2 may be
used by any network simulation as the routing algorithm for the
transmissions. The Wireless sensor network may be simulated by writing the
tcl program and execute in ns2. The simulated WSN may be assumed to
consist of a number of sensor nodes, a base station node and a physical
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phenomenon node. This may be placed in a sensor field covering a particular
area.
Now to check the HETRA routing algorithm, a WSN is simulated
with about 55 nodes. These nodes are placed in a random placement pattern
covering the area of 250 500 m. The simulated network is shown in
Figure 3.7. The figure is nam generated display of the simulation performed
in ns2.
The simulation is now performed by assuming the occurrence of
physical phenomenon at any point in the sensor field of 250 500 m. This
phenomenon node is now attached with a TCP source and an ftp agent is
attached to this source for continuous transmission of data packets. Hence,
when this phenomenon node moves over the sensor field area, the data
packets transmitted from this phenomenon node is assumed as the sensed
data. This sensed data is now transmitted towards the base station node.
Figure 3.7 Example of WSN simulated with HETRA using ns2
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The simulation program is written to find the number of data
packets received at the base station node. The throughput of the transmission
is also evaluated. The simulation is repeated for different simulation times.
The simulation is repeated for the simulation times of 5, 10, 15, 20, 25 and
30 seconds. The corresponding data packets received is evaluated. These
values are depicted in Figure 3.8. This figure indicates that the packets are
properly routed to the base station node by HETRA and the number of data
packets received increases with the increase in simulation time. This figure
infer that the routing algorithm behaves very similar to other routing
algorithms in increasing the number of data packets with the increase in
simulation time.
Number of Data Packets Delivered
0
1000
2000
3000
4000
5000
6000
7000
5 10 15 20 25 30
Simulation Time in seconds
Nu
mb
er
of
Data
Packets
Figure 3.8 Number of data packets delivered in different Simulation
Times
The simulated WSN as discussed above is also analysed for the
NLT. The trace file format is shown in Figure 2.16. Since awk script is very
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efficient in matching data file which has the form of line records and is very
convenient in extracting numeric variables from strings, it is also easy to do
the computation as C programming language does. So, awk script is used to
analyse the trace file and compute the performance evaluation of the proposed
congestion control and routing algorithms. Hence, from the pseudocode, it is
easy to see that the awk script get the trace file line by line. The following
statement works as a filter when it is tried to get a line from the trace file. It
can easily draw out the packet one wants to count, such as TCP packets,
AODV routing control packets, DSR routing control packets, and so on.
set awkCode {
BEGIN { print "" >> "aodv1"; }
{if ($7 == "tcp" && $1 == "s" || $1 == "r")
{time = $2;
energy = $14*1;}
print time, energy >> "aodv1";}}
exec awk $awkCode aodv.tr
Hence, by using the tcl programs and the other supporting programs
as mentioned above, NLT of the WSN may be determined for the simulated
network topology. The energy versus time algorithm for one such simulation
with an initial energy of 1.5 Joules for the sensor node is shown in
Figure 3.9. This figure indicates that the energy level of the nodes of WSN
gets reduced from 1.5 Joules to 0 Joules in a time of about 21 seconds. The
lifetime of the WSN using HETRA with an initial energy of 1.5 Joules is
therefore 21 seconds. The value of this NLT with initial energy of 0.25, 0.5,
0.75, 1.0, 1.25 and 1.5 Joules is depicted in Figure 3.10. The figure shows that
the NLT is directly proportional to the initial energy level of the sensor nodes.
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Figure 3.9 Energy versus time with initial energy of 1.5 Joules
Network Life Time with Different Initial Energy
0
5
10
15
20
25
0.25 0.5 0.75 1 1.25 1.5
Initial Energy in Joules
Netw
ork
Lif
e T
ime i
n s
eco
nd
s
Figure 3.10 NLT with different initial energy
The design and implementation of the HETRA is detailed in this
chapter. A simple simulation is detailed and the results show that the HETRA
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behaves similar to other algorithms as far as the sensing and transmission of
data is concerned. This will be compared with the classical algorithms and the
results discussed in Chapter 6. The next chapter discusses the proposed
congestion control algorithm TCp/Exp, its implementation details and the
basic characteristics.