# Performance Evaluation of the WSN Routing Protocols Scalability

Authors: L. Alazzawi; A. Elkateeb
Journal: Journal of Computer Systems, Networks, and Communications
 (2008)

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## Abstract

Scalability is an important factor in designing an efficient routing protocol for wireless sensor networks (WSNs). A good routing protocol has to be scalable and adaptive to the changes in the network topology. Thus scalable protocol should perform well as the network grows larger or as the workload increases. In this paper, routing protocols for wireless sensor networks are simulated and their performances are evaluated to determine their capability for supporting network
scalability.


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## Body

## 1. Introduction

Sensor networks
consist of a large number of small sensor devices that have the capability to
take various measurements of their environment. For instance, such measurements
can include acoustic, magnetic, and video information. Each of these devices is
equipped with a small processor and wireless communication antenna and is
powered by a battery making it very resource constrained. These sensors are typically
scattered around a sensing field to collect information about their surroundings.Deferent routing
protocols are designed and implemented for WSNs [1]. The design of these routing protocols is influenced
by many factors where the scalability factor is considered as one of these
important factors. The sensor networks scalability is the ability to support network
expansion to include more nodes that might not be anticipated during the
initial network design stage. Therefore, the routing protocols used for
wireless sensor networks should support network scalability where such
protocols should continue to perform well as the network grows larger or as the
workload increases [2].As the routing
packets within a large-scale wireless sensor network occur on nodes that have
very limited resources of packets storage and updates routing table processing,
the routing processing has become a very challenging issue. Therefore, in order
to constrain such limitations, efficient and scalable routing protocols design
is required for routing packets in sensor networks [2]. Such routing protocols
should avoid degrading the performance of the wireless sensor networks as the
network expanding.The evaluation of the
scalability issue with wireless sensor networks is a real challenge due to the variety
of routing protocols, the large nodes number, and the wide range of sensor
networks applications. Therefore, the evaluation of the sensor networks
scalability is not practically feasible over a real network, and using network
simulator will provide a meaningful perspective into the study of the sensor
networks scalability [3]. Thus the goal of this paper is to develop a
detailed simulation framework, which can accurately model different sensor
networks routing protocols.


## 2. Routing Protocols for Scalability Evaluation

Many routing protocols have been proposed for routing data in wireless
sensor networks. These routing protocols have considered the characteristics of
sensor nodes along with the application and architecture requirements [4]. Three
different protocols have been selected in this evaluation study: the flooding
protocol (FP) [5], the beacon vector routing protocol (BVR) [6], and the
probabilistic geographic routing protocol (PGR) [7] (see Figure 1). Using these
three different protocols for evaluating scalability issue in WSNs can be
expanded in future work to support other protocols.Figure 1
Selected routing protocols for scalability evaluation.The beacon
vector routing protocol (BVR) is a hierarchical-based routing protocol which is
assigned coordinates to the nodes based on the vector of hop count distances to
a small set of beacons, and defines distance metric on these coordinates. The BVR
routes packets greedily forwarding to the next hop. That is, the closest to the
destination according to this beacon vector routing. The probabilistic
geographic routing protocol (PGR) is location-based routing that assumes each node is
aware of its geographical coordinates through some localization scheme, such as
the GPS. The flooding protocol (FP) is one of the available flat-based routing
protocols. In this protocol, each intermediate node that receives a packet
simply forwards it to all its neighbors until it reaches the destination.
Flooding protocol is selected because of the simplicity of this flat protocol
that can be used to compare other protocols scalability with this flooding
protocol [4].


## 3. The Comparison Quantitative Metrics

In order to compare FP, BVR, and
PGR protocols’ scalability, the quantitative
metrics are used to measure and evaluate the performance of the simulated
routing protocols. For all metrics, the average over multiple experiments is
determined. The set of performance metrics used for comparing the selected
routing protocols of this work is shown in Figure2. Each of these metrics
parameters can be described briefly as follows [8].Figure 2
A set of performance metrics.(a) Network Delay
This performance
metric is used to measure the average end-to-end delay of data packet
transmission. The end-to-end delay implies the average time taken between a
packet initially sent by the source, and the time for successfully receiving the message at
the destination. Measuring this delay takes into account the queuing and the
propagation delay of the packets.(b) Network Throughput
The end-to-end
network throughput measures the number of packets per second received at the
destination. It is considered here as an external measure of the effectiveness
of a protocol.(c) Success Rate
The total number of
packets received at the destinations verses the total number of packets sent
from the source.(d) Latency
The average message
latency is defined as the average amount of time between the start of
disseminating a data and its arrival at a node interested in receiving the
data. Hence, the latency measures time performance for the individual message [9].(e) Energy Consumption
The energy
consumption is the sum of used energy of all the nodes in the network, where
the used energy of a node is the sum of the energy used for communication,
including transmitting (Pt), receiving (Pr), and idling (Pi). Assuming each
transmission consumes an energy unit, the total energy consumption is
equivalent to the total number of packets sent in the network.(f) Network Lifetime
It is considered as
the time until message loss rate is above a given threshold. The more complete
definition for the lifetime of the network is “time to network
partition” [10]. Network partition occurs when there is a cut-set in the
network. It will be introduced as a new metric, which will use energy variance:(1)Networklifetime=E−(U̲+σ),whereU̲=ΣUiN.
E is the total
initial energy at each node (full battery charge), Ui is the average used
energy, N is the total number of nodes in the network, and σ is
expressed as(2)σ2=(Ui−U̲)2N. All these metrics
are calculated using their cumulative average values, that is, at time t, the
performance value is the average from 0 to t (seconds) [11].(g) Packet Generation Rate
It is the number of
packets that the sensor node transmits in one time period which is usually one
second.


## 4. Simulator

Many network simulators
are currently available such as SensorSim [12], TOSSIM [13], NS2 [14], OPNET
[15]. However, we decided to use the prowler simulator in this research work because of
our previous experience with this simulator, easy to use, and it is available
online [16].We
have used the prowler
network simulator to evaluate the protocols performance and specifically to measure
their scalability. A prowler
is an event-driven tool that simulates the nondeterministic nature of the
communication channel and the low-level communication protocol of the wireless
sensor nodes [4]. To produce replicable results while testing the application, prowler can be set to
operate in deterministic mode also. It can incorporate arbitrary number of
nodes on arbitrary and even dynamic topology. Prowler models all the important
aspects of the communication channel and the application. The tool is
implemented in MATLAB, thus it provides a fast and easy way to prototype applications,
and has nice visualization capabilities [10].The
nondeterministic nature of the radio propagation is characterized by a
probabilistic radio channel model. The applications interact through a set of events
and commands just like in actual TinyOS applications. The radio propagation
model determines the RF signal strength at a particular point in the space for
all transmitters in the system. Based on this information, the signal reception
conditions at the receivers can be evaluated and collisions can be detected. Similarly
to the real TinyOS framework, prowler applications are event based.The
simulator signals important events for the application code, such as
initialization completed, packet sent, packet received, packet collided, and clock
ticked. The application in turn can initiate actions such as set clock and send
packet. These can cause further events. Several debugging/visualization tools
are also available, including switching mote LEDs on/off, drawing lines and
arrows, and printing text messages.


### 4.1. Protocols Performance

A
set of simulations are used to evaluate the performance of the protocols: FP, BVR,
and PGR. The simulations are all performed using prowler under the default radio modelσα=45, σβ=0.02, and perror=0.05. The average radio range of
transmission was a radius of 10 m. However, the radio model in prowler was se tup to
model the transmission range as an imperfect circle. The network setup
consisted of 100 nodes dispersed in an area depicting 100m×100m. The simulations
are run on random networks model, where the nodes placements are changed
randomly in uniformly square area. The sensors are deployed in a regular grid
with random offsets.The performance of the protocols
is evaluated under two setting referred to firstly in the relation of simulation
time in seconds with defiant quantitative metrics include (success rate, latency,
and throughput. Secondly, it is reflected through the
relation between average delays, total throughput, and total energy used. All the results are driven from five runs for the
random network models for each of the three protocols. Figures3, 4, 5, and 6 illustrate the
performance of the flooding protocol. Figures 7, 8, 9, and 10 illustrate the
performance of the PGR protocol and Figures 11, 12, 13, and 14 illustrate the
performance of the BVR protocol.Figure 3
Simulation time
and success rate.Figure 4
Simulation time and latency.Figure 5
Simulation time and throughput.Figure 6
Average delays—total throughput—total energy used.Figure 7
Simulation time
and success rate.Figure 8
Simulation time and latency.Figure 9
Simulation time and throughput.Figure 10
Average delays—total throughput—total energy used.Figure 11
Simulation time and success rate.Figure 12
Simulation time and latency.Figure 13
The simulation time and throughput.Figure 14
Average delays—total throughput—total energy used.


#### 4.1.1. Flooding Protocol Performance

Figures3–6 are illustrating the flooding protocol performance.


#### 4.1.2. PGR Protocol Performance

Figures7–10 are illustrating the PGR protocol performance.


#### 4.1.3. BVR Protocol Performance

Figures11–14 are illustrating the BVR protocol performance.


### 4.2. Effects of Routing Metrics on the Scalability According to the Packet Generation Rate

The
first study of the network scalability for the selected routing protocols is by
using different packet generation rates in a prowler simulator. We randomly
place 100 sensor nodes in a100m×100m sensor field for each protocol. The
default prowler radio model is used. Each routing protocol test is performed
(with query and event locations randomly selected) which, started by one packet
and then increased the
number of packets.
In this section, performances of the selected routing protocols with increased
workload are evaluated as follows.Figure15 compares the energy used for the flooding, BVR, and PGR protocols with
respect to packet generation rate. The results show that the energy used
experienced by BVR protocol is lower than the other protocols.Figure 15
Energy test.The
message latency can be an important parameter for the scalability of the
protocols. The intrinsic relative low latency characteristic can be used as a
tradeoff to extend the network performance in accordance with the protocol
constraints. The BVR protocol gave the lowest latency among the PGR and flooding
protocols with increasing the
packet generation rate as shown in Figures16.Figure 16
Latency test.With
increasing the packet generation rate, the average packet delay in the flooding
protocol gave the highest delay among others as shown in Figure17. As an average, this highest
value was 0.264 second. For the BVR protocol, this was the best because it gave
much lower delay than the two other protocols that intern it gave an average of (0.075) second.Figure 17
Average packet delay test.Figure18 shows the network lifetime of the three protocols as the packet generation increases. We observe
that the network lifetime of each protocol is decreased. The BVR protocol was
the longest network lifetime while the flooding protocol gave the shortest network
lifetime. This was expected since flooding is a very energy-consuming task.Figure 18
Network lifetime
test.


### 4.3. Effects of Routing Metrics on the Scalability According to the Network Size

This section turns our attention from the
scalability to the behavior of the protocols with respect to increasing the
number of nodes in the network. The transmission range of each mote is set to
10 units and runs the protocols with 50 to 500 nodes in steps of 50 sensor
nodes (motes placed) in a grid. From the observed performance metrics for each
of these protocols with increasing of network size, we obtain the following.(i)
The BVR protocol achieved the best
success rate over different network sizes in comparison with PGR and flooding
protocols that gave a lower success rate, respectively, as shown in the Figure19.(ii)
The average throughput for BVR was about
73% over different network sizes as shown in Figure20. While the flooding
protocol has about 43% throughput and PGR protocol has about 47% throughput
when the number of sensor nodes in the networks is increased.(iii)
The PGR protocol gave a lower latency
than the flooding protocol with increasing of network size as shown in Figure21.
Among them the BVR protocol gave the lowest latency with increasing the network
size.(iv)
The energy consumption of the BVR
protocol was the lowest in comparison with the rest of protocols according to
the increasing number of nodes as shown in Figure22.Figure 19
Success rate
test.Figure 20
The throughput test.Figure 21
Latency test.Figure 22
The energy consumption test.


## 4.1. Protocols Performance

A
set of simulations are used to evaluate the performance of the protocols: FP, BVR,
and PGR. The simulations are all performed using prowler under the default radio modelσα=45, σβ=0.02, and perror=0.05. The average radio range of
transmission was a radius of 10 m. However, the radio model in prowler was se tup to
model the transmission range as an imperfect circle. The network setup
consisted of 100 nodes dispersed in an area depicting 100m×100m. The simulations
are run on random networks model, where the nodes placements are changed
randomly in uniformly square area. The sensors are deployed in a regular grid
with random offsets.The performance of the protocols
is evaluated under two setting referred to firstly in the relation of simulation
time in seconds with defiant quantitative metrics include (success rate, latency,
and throughput. Secondly, it is reflected through the
relation between average delays, total throughput, and total energy used. All the results are driven from five runs for the
random network models for each of the three protocols. Figures3, 4, 5, and 6 illustrate the
performance of the flooding protocol. Figures 7, 8, 9, and 10 illustrate the
performance of the PGR protocol and Figures 11, 12, 13, and 14 illustrate the
performance of the BVR protocol.Figure 3
Simulation time
and success rate.Figure 4
Simulation time and latency.Figure 5
Simulation time and throughput.Figure 6
Average delays—total throughput—total energy used.Figure 7
Simulation time
and success rate.Figure 8
Simulation time and latency.Figure 9
Simulation time and throughput.Figure 10
Average delays—total throughput—total energy used.Figure 11
Simulation time and success rate.Figure 12
Simulation time and latency.Figure 13
The simulation time and throughput.Figure 14
Average delays—total throughput—total energy used.


### 4.1.1. Flooding Protocol Performance

Figures3–6 are illustrating the flooding protocol performance.


### 4.1.2. PGR Protocol Performance

Figures7–10 are illustrating the PGR protocol performance.


### 4.1.3. BVR Protocol Performance

Figures11–14 are illustrating the BVR protocol performance.


## 4.1.1. Flooding Protocol Performance

Figures3–6 are illustrating the flooding protocol performance.


## 4.1.2. PGR Protocol Performance

Figures7–10 are illustrating the PGR protocol performance.


## 4.1.3. BVR Protocol Performance

Figures11–14 are illustrating the BVR protocol performance.


## 4.2. Effects of Routing Metrics on the Scalability According to the Packet Generation Rate

The
first study of the network scalability for the selected routing protocols is by
using different packet generation rates in a prowler simulator. We randomly
place 100 sensor nodes in a100m×100m sensor field for each protocol. The
default prowler radio model is used. Each routing protocol test is performed
(with query and event locations randomly selected) which, started by one packet
and then increased the
number of packets.
In this section, performances of the selected routing protocols with increased
workload are evaluated as follows.Figure15 compares the energy used for the flooding, BVR, and PGR protocols with
respect to packet generation rate. The results show that the energy used
experienced by BVR protocol is lower than the other protocols.Figure 15
Energy test.The
message latency can be an important parameter for the scalability of the
protocols. The intrinsic relative low latency characteristic can be used as a
tradeoff to extend the network performance in accordance with the protocol
constraints. The BVR protocol gave the lowest latency among the PGR and flooding
protocols with increasing the
packet generation rate as shown in Figures16.Figure 16
Latency test.With
increasing the packet generation rate, the average packet delay in the flooding
protocol gave the highest delay among others as shown in Figure17. As an average, this highest
value was 0.264 second. For the BVR protocol, this was the best because it gave
much lower delay than the two other protocols that intern it gave an average of (0.075) second.Figure 17
Average packet delay test.Figure18 shows the network lifetime of the three protocols as the packet generation increases. We observe
that the network lifetime of each protocol is decreased. The BVR protocol was
the longest network lifetime while the flooding protocol gave the shortest network
lifetime. This was expected since flooding is a very energy-consuming task.Figure 18
Network lifetime
test.


## 4.3. Effects of Routing Metrics on the Scalability According to the Network Size

This section turns our attention from the
scalability to the behavior of the protocols with respect to increasing the
number of nodes in the network. The transmission range of each mote is set to
10 units and runs the protocols with 50 to 500 nodes in steps of 50 sensor
nodes (motes placed) in a grid. From the observed performance metrics for each
of these protocols with increasing of network size, we obtain the following.(i)
The BVR protocol achieved the best
success rate over different network sizes in comparison with PGR and flooding
protocols that gave a lower success rate, respectively, as shown in the Figure19.(ii)
The average throughput for BVR was about
73% over different network sizes as shown in Figure20. While the flooding
protocol has about 43% throughput and PGR protocol has about 47% throughput
when the number of sensor nodes in the networks is increased.(iii)
The PGR protocol gave a lower latency
than the flooding protocol with increasing of network size as shown in Figure21.
Among them the BVR protocol gave the lowest latency with increasing the network
size.(iv)
The energy consumption of the BVR
protocol was the lowest in comparison with the rest of protocols according to
the increasing number of nodes as shown in Figure22.Figure 19
Success rate
test.Figure 20
The throughput test.Figure 21
Latency test.Figure 22
The energy consumption test.


## 5. Conclusions

In this work, three WSN
protocols, namely, BVR, PGR, and FP were simulated using advance wireless
sensor simulator (prowler). Several tests were carried out using different network
parameters of WSNs. The performance of different routing protocols is measured
to determine the most efficient one for the scalability. After evaluating
several metrics which are throughput, latency, energy consumption, and delay,
it was found that the BVR protocol is the most efficient scalable protocol.



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*Source: 481046-2009-03-10.xml*