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"
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.
- 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
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:
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
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.
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.
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.
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.
Figure
4.6 Residual energy of source as a function of time.
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.
Figure
4.7 Throughput as function of time.
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.
Figure
4.8 Effect of network density
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.
Figure
4.9 Packet delivery ratio of the network.
No comments:
Post a Comment