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A Comprehensive Performance Study of OPNET Modeler

For ZigBee Wireless Sensor Networks

I. S. Hammoodi

Caledonian College of

Engineering, Muscat, [email protected]

B. G. Stewart

Glasgow Caledonian

University, Glasgow, [email protected]

A. Kocian1

University of Rome "Tor

Vergata", Rome, [email protected]

S. G. McMeekin

Glasgow Caledonian

University, Glasgow, [email protected]

Abstract - OPNET has been widely used as a network

simulator, but not much emphasis has been given on the

performance of this simulator for ZigBee wireless sensor

networks (WSN). Simulation of WSNs is a challenging task

due to the nature of hardware design, energy limitations,

and deployment of a vast number of nodes. An inclusive

study and analysis of the QoS performance evaluation of the

ZigBee protocol within the OPNET simulator for different

WSN topologies and routing schemes is presented here.

Based on simulation and analysis of results this paper can be

considered as a guide for researchers in evaluating OPNETModeler as a WSN simulator for Zigbee networks. Some

enhancements needed in OPNET Modeler to be more

suitable for the simulation of ZigBee WSNs are discussed.

Keywords: OPNET; Wireless Sensor Networks; QoS; ZigBee

I. INTRODUCTION

A wireless sensor network (WSN) comprises spatially

distributed autonomous devices using sensors to

cooperatively monitor physical or environmental

conditions, such as temperature, sound, vibration,

pressure, motion etc. at different locations. Subsequent to

collaborative sensing of a given environment, the nodes

perform in-network computation and communicate with abase station when a targeted event takes place [1]. A WSN

has a number of exclusive characteristics when compared

with conventional wireless networks. These include

limited bandwidth, limited computation capability ofindividual nodes, and limited energy supply. Self-

organization, dynamic network topology, and multi-hop

routing are additional key possible features of a WSN,

which make them important for many applications. It is

advantageous to perform precise simulations or to develop

models before deploying WSNs in the field. This isbecause WSNs may be deployed randomly in an ad-hocmanner with a large number of tiny nodes. Simulations

help in the validation and evaluation of the performanceof sensor networks within certain application

environments, something which was not possible to

achieve a number of years ago. Consequently, simulationof sensor networks is therefore gaining greater demand

because of their capabilities, lower energy constraints and

the use of a larger number of nodes compared to

conventional wireless networks. ZigBee (a set of

specifications built around the IEEE 802.15.4 wirelessprotocol) is a common platform for WSNs. Some

published work exists on the evaluation of WSNsimulation software. As an example reference [2]

evaluates NS-2 as a simulator for ZigBee WSNs and

concludes that NS-2 itself does not perform well in terms

of efficiency and support for real world applications. In[3] a comparative study between OPNET Modeler and

NS-2 has been reported in which the accuracy of NS-2

and OPNET Modeler in a wired network is compared

using Constant-Bit-Rate (CBR) data traffic and a File

Transfer Protocol (FTP) session. It was found that NS-2

provides very similar results compared to OPNETModeler in terms of throughput. However the

performance evaluation or comparison in [3] does not

cover the performance evaluation of wireless networks orWSNs in particular. A survey of simulation within sensor

networks has also been covered in [4]. Here different

kinds of sensor networks simulators including OPNETwere briefly discussed. The constraints and limitations of

each sensor simulator were identified as well as focusing

on open research issues in WSNs. However no simulation

results or actual analysis of the simulation tools were

presented.

To provide further clarity on software simulationperformance of WSNs, the intention of this paper is to

present a QoS (Quality of Service) performance

investigation of OPNET Modeler for three different

ZigBee WSN topologies and to ascertain its generalcapabilities for these situations. The effect of the numberof nodes on the MAC throughput and end-to-end delay is

inclusively evaluated for these topologies. Further, the

effect of handshaking on the end-to-end delay between the

nodes is also investigated. Possible improvements to

OPNET are also proposed to make simulation of ZigBee

WSNs more suitable within this software. To the best ofthe authors’ knowledge no work has previously been

presented which focuses on the performance evaluation of

OPNET Modeler as a WSN simulator for the ZigBeeprotocol. Therefore this work may assist researchers in

evaluating and validating OPNET Modeler as an

appropriate WSN simulation tool.The paper is structured as follows. Section 2 presents an

overview of the performance evaluation summarizing

QoS parameters and the simulation scenarios considered.

Section 3 presents the results and a critical analysis of the

different scenarios adopted. In section 4, improvements to

OPNET are suggested and in section 5, conclusions aredrawn.

II. PERFORMANCE EVALUATION

Based on several criteria such as availability,

reliability, response time, load, throughput, bandwidth

capacity, and packet loss ratio, a real network can beevaluated. These parameters can be summarized as QoS.

1Most of the work was performed when the author was with Birla


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QoS can be regarded as a low level “networking device

visible attribute” such as bandwidth, delay, jitter and

packet loss rate, or as a high level “user observable” like

the quality of voice communication or video

communication [5]. The low level attributes are applicablefor most WSNs.

A network simulator performance refers to its ability and

accuracy to simulate and imitate an actual network whilemaintaining an accurate log of the QoS attributes

discussed above. Additionally, a number of other factors

such as network protocol, routing, topology, energymodel, etc., have considerable influence on the operation

of a simulator and hence on its performance. Further,

architecture, data structure, and the algorithms

implemented within the simulator software also have an

effect on its performance. Clearly the accurate simulationof a WSN is a complex task.

In order to provide a general performance evaluation of

OPNET Modeler for ZigBee WSNs, the performance

criteria presented in this paper will include end-to-end

delay, number of employed hops, throughput, datadropped and simulation time. In general these parametersgive a clear prospective of the QoS parameters in WSNs.

A. Introduction to OPNET Modeler

The OPNET Modeler environment includes tools for

all phases of a study, including model design, simulation,

data collection, and data analysis [6]. OPNET Modeler

provides a comprehensive development environmentsupporting the modelling of communication networks and

distributed systems. Both behaviour and performance of a

model can be analyzed by performing discrete event

simulations. A Graphical User Interface (GUI) supports

the configuration of the scenarios and the development ofnetwork models. Three hierarchical levels for

configuration are differentiated: i) the network level

creating the topology of the network under investigation,ii) the node level defining the behaviour of the node and

controlling the flow of data between different functional

elements inside the node, and iii) the process level,describing the underlying protocols, represented by finite

state machines (FSMs) and are created with states and

transitions between states. The source code is based on

C/C++. The analysis of simulated data is supported by a

variety of built-in functions [7]. Different graphical

presentations for the simulation results are available andnode mobility can be easily implemented in different

kinds of nodes i.e. ZigBee coordinator, end device androuter nodes.

The OPNET ZigBee model uses four process models:

• ZigBee MAC model which implements a model of theIEEE 802.15.4 MAC protocol. The model implementschannel scanning, joining and failure/recovery process

of the protocol in the unslotted operation mode.

• ZigBee Application model which represents a lowfidelity version of the ZigBee Application Layer as

specified in the ZigBee Specification. The processmodel initiates network joins and formations,

generates and receives traffic and generates different

simulation reports.

• ZigBee Carrier Sense Multiple Access/Collision

Avoidance (CSMA/CA) model which implements the

media access protocol of the MAC layer.

• ZigBee Network model which implements the ZigBee

Network Layer as specified in the ZigBeespecification. This model is responsible for routing

traffic, process network join, formation requests andgenerating beacons [6].

B.

Simulation Scenarios

To evaluate the performance of OPNET in simulatingZigBee WSNs, the three common topologies of WSNs

will be investigated, namely: star, mesh and tree

topologies. In star topology, nodes are connected to a

single hub node. The hub “coordinator” requires greater

message handling, routing, and decision-making

capabilities than the other nodes or “end devices”. If acommunication link is cut, it only affects one node.

However, if the coordinator fails the network is destroyed.

In mesh topology nodes are regularly distributed to allow

transmission only to a node’s nearest neighbours. Thenodes in these networks are generally identical, so that

mesh nets are also referred to as peer-to-peer nets. Meshnets can be good models for large-scale networks of

wireless sensors that are distributed over a geographic

region. An advantage of mesh topologies is that, while all

nodes are possibly identical and have the same computing

and transmission capabilities, certain nodes can bedesignated as coordinators that take on additional

functions. If a coordinator fails, another node can then

take over these duties. In tree topology the coordinator

node is connected to one or more other nodes that are onelevel lower in the hierarchy with a point-to-point link

between each of the end nodes and the coordinator node.Also each of the end nodes that are connected to the

coordinator node will have one or more other nodes that

are one level lower in the hierarchy connected to it with a

point-to-point. The initial star topology scenario

considered here consists of 8 ZigBee end devices (reduced

function devices) and 1 coordinator (full function devices)as shown in Fig. 1. The initial mesh and tree topology

scenarios considered consist of 8 ZigBee end devices, 6

ZigBee routers and 1 coordinator as shown in Fig. 2. Theselection of the number of ZigBee routers in the tree and

mesh topologies is made to ensure that each topology can

handle the increase in the number of end devices in thesimulation scenarios that follow without rapid increase in

the delay. The mesh and the tree routing scenarios

normally use the ad-hoc on-demand distance vector

(AODV) routing protocol. All the three topologies will be

simulated with and without the use of Request-to-Send/Clear-to-Send (RTS/CTS) handshaking to study the effect

of handshaking on the delay and other QoS parameters. In

sensor networks hundreds to several thousands of nodes

could be deployed throughout the sensor field. Thereforeconsideration of the number of nodes was included in the

simulation evaluation process. Simulation was performedfor scenarios of 8 nodes up to scenarios of 200 nodes in

the three selected WSNs topologies.


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All sensor nodes were configured with CBR traffic, and

for evaluation purposes, all the nodes in a single scenario

were assumed to be in the same personal area network

(i.e. have the same Personal Area Network (PAN) ID).

For simplicity, the node will choose a random destinationnode within its own PAN. The altitude of all the nodes in

all the scenarios is set to be 1 meter above ground level.

This altitude is the default value of the OPNET ZigBeenode model and can be varied according to the application

being simulated. Normally in WSNs one of the

coordinator’s duties is to dictate the topology of thatnetwork, therefore in each topology the type of network

(star/tree/mesh) is set at the coordinator node. The

simulation run time for all the scenarios is set at 30

minutes. Table 1 shows the network parameters set at the

coordinator nodes only. Table 2 shows the MAC, Physicaland Application layer simulation parameters that have

been implemented in the simulation scenarios.

Figure 1 Initial scenario of star topology.

Figure 2 Initial scenario of mesh and tree topologies.

III. SIMULATION AND ANALYSIS

A. End-to-End Delay

The end-to-end delay (ETE) is defined as the end-to-

end delay of all the packets received by the 802.15.4MACs of all WPAN nodes in the network and forwarded

to the higher layer. As the number of nodes in the WANs

increase the delay obviously will increase. A simulation of

ETE delay of the three topologies with increasing number

of nodes was undertaken. Fig. 3 shows the simulation

results of the average ETE delay for the mesh, tree andstar topologies as a function of the number of nodes.

In this simulation scenario the RTS/CTS handshake is

enabled. It is seen that the difference in delays between

the mesh and the tree topology is small even when thenumber of nodes increase. However, there are slightly

higher delays in the network with the mesh topology when

compared with the tree topology; this is basically due to

the differences in the routing techniques and the size of

the routing table used in mesh routing. It is seen that the

ETE delay in the star topology is higher than the other twotopologies. This is due to the fact that the star topology is

a single hop topology and there is only one path from one

node to another through the coordinator. Also it is seenthat the delay increases proportionally with the increase in

the number of nodes; this is as expected since increasing

the node numbers in WSNs will lead to higher traffic andhence higher delay. The general trend of the simulation

results agree with the results presented in [7] therefore

demonstrates that OPNET is providing acceptable results.

Fig. 4 shows the simulation results of average ETE delay

of the WSN star topology with and without usingRTS/CTS handshaking. In general the ETE delay

confirms the behaviour in Fig. 3 in that for small node

numbers the ETE delay is very low then it increases

exponentially for higher node numbers. It is also shown

that the delay increases rapidly when RTS/CTS is notused. This is due to excessive collisions when there is nocongestion control leading to very much higher delays.

However, for low node numbers, the delay in the case of

not utilizing RTS/CTS is slightly lower. This occurs since

there is no need for congestion control for small numbers

of nodes and data packets are processed quicker when

handshaking is not in use.

TABLE 1 COORDINATOR’S NETWORK LAYER PARAMETERS

Coordinator’s Network Layer Parameters

Maximum number of end devices and routers in one PAN 250

Maximum number of routers in a single PAN 6

Route discovery timeout (sec)

The duration of route discovery entries remaining in the

table before they are removed. (Only used in mesh

networks).

10

TABLE 2 THE MAC, PHYSICAL AND APPLICATION LAYER

SIMULATION PARAMETERS

MAC Layer Parameters

Acknowledge wait duration (sec) 0.05

Maximum Number of retransmissions 5

Minimum value of the back-off exponent in the

CSMA/CA (if this value is set to 0, collision avoidance is

disabled during the first iteration of the algorithm)

3

Maximum number of back-offs the CSMA/CA algorithm

will attempt before declaring a channel access failure.4

Channel sensing duration (sec) 0.1

Physical Layer Parameters

Data rate (kbps) 250

Receiver sensitivity (dB) -85

Transmission band (GHz) 2.4

Transmission power (W) 0.05

Application Layer Parameters

Packet interval time/ type (sec/constant ) 1

Packet size/type (bits/constant) 1024


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B.

Number of Hops

Fig. 5 shows the average number of hops taken by

application traffic sent by a particular node for different

numbers of sensor nodes for the three test scenarios. For

mesh and tree topologies, the minimum hop number isaround two. However for the star topology the number of

hops remains at 2 as the number of nodes increase. Thetree topology obviously exhibits a higher number of hops

than the other two topologies. This is due to the longerroute taken for the data from source to destination. There

are no alternative routes in this topology hence the path islonger. In mesh topology the number of hops is lower than

the tree topology because there is always an alternative

route to reach the destination and this route is based on the

shortest path to destination. The maximum number of

hops in tree topology is expected to reach as high as 7hops at 100 nodes but since the average number of hops is

considered it shows a maximum of 4. In star topology the

hop count is maintained at 2 and this number is the

average over the entire simulation run. It is obvious that as

node number increases the average hops will increase.However, it is seen that when there are more than 100sensor nodes, the hop number decreases. It appears that

the network is performing better as the number of nodes

increase. In fact, this takes place since higher data

collisions occur in crowded networks thus only some data

packets possess a probability of delivery to allow them to

reach the destination and hence only few nodes can senddata to the receiver successfully. Again the general trends

of these results agree with the results obtained in [2].

C.

Global MAC Throughput

Global MAC throughput is the total data traffic in

bits/sec successfully received and forwarded to the higherlayer by the 802.15.4 MAC in all the nodes of the WSN. It

is known that throughput usually depends on many

aspects of networks such as power control, scheduling

strategies, routing schemes and network topology [8]. Fig.6 shows the average global MAC throughput against the

number of nodes for all 3 simulation topologies. It canclearly be seen that when the number of nodes increases

the MAC throughput increases. This is correct because the

data being received by the MAC layer increases.

Figure 3 Simulation of the average ETE delay for the mesh, star and

tree topologies.

Figure 4 Simulation results of average ETE delay of the WSN star

topology with and without RTS/CTS handshaking.

Figure 5 Average number of hops traveled by application traffic.

Figure 6 Average global MAC throughputs against the number of nodes.

This behaviour agrees in general with the resultspresented in [8]. However when the number of nodes

increases above 60 in mesh and tree topologies, more

collisions will take place as the MAC layer cannot handle

the increased number of nodes. The throughput decreasessharply if the number of nodes increases above 80 due to

access collisions. This throughput drop (10-40kbps) also

agrees in general with the results presented in [5]. There is

a slight difference between the throughput of the mesh


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and tree topologies for the same number of nodes, this is

because the mesh topology supports multipath routes

which can cause a minor increase in the throughput. In the

star topology the throughput increases until it reaches a

maximum at 100 nodes and drops rapidly for higher nodenumbers. This difference in throughput between star

topology and the other two topologies is not related to the

ETE delay explained earlier. This difference appears innode numbers between 80 and 100. This is due to

excessive demand from all the nodes within their MAC

layers which will cause the MAC throughput to increasemomentary then after 100 nodes, due to excessive

collisions, the throughput drops rapidly.

D. Simulation Time

Time to run the simulation, generate reports and

statistics in OPNET varies as the numbers of sensor nodesincrease or decrease. It is apparent that increasing the

number of nodes leads to an increase in running time. This

increase in simulation time starts slowly when the numberof nodes is low then takes an exponential manner as the

number of nodes increase - this relationship is illustrated

in Fig. 7. The results indicate that a network with more

nodes is much more complex internally than a network

with fewer nodes and requires much more time togenerate reports by OPNET. At certain levels, simulation

times may take hours. Even in some real application

environments, 180 nodes is not considered a high number.

Comparing the simulation times of OPNET with NS-2 [2]reveals that the latter has a much faster simulation time

than that of OPNET. However this does not mean thatOPNET Modeler lacks performance in this aspect but this

is due to the significant amount of data collected inreports and statistics. The simulation time analysis will

assist in deciding which simulation tool is faster

especially when there are a very high number of nodes inthe WSN to be simulated.

Figure 7 Simulation time for different number of sensor nodes

IV. IMPROVEMENTS SUGGESTED FOR OPNET MODELER

Simulation results show that OPNET is suitable for

simulating ZigBee WSNs with a wide range of available

statistics. Features that are useful for the applications of

WSNs include large number of reports and statistics that

are available across different network layers of the ZigBee

WSN, especially at the MAC layer where the quantity ofreports generated was not found in any other ZigBee

WSN simulator. However from this investigation it may

be suggested that it would be beneficial if a number offeatures of OPNET were enhanced to make it more

suitable for the simulations and analysis of WSNs.Improvements proposed for OPNET ZigBee model in

simulating WSNs include; multicast traffic, indirect

transmission, security and a model for energy. These are

currently the key aspects related to WSN implementation.

A.

Multicast and Unicast Traffic

Multicasting means transmitting a single messagefrom one node to a selected group of nodes. Unicasting

means transmitting a single message from one node to

another node, e.g. coordinator node to a specific node [9].

In OPNET ZigBee WSN simulation only the message

broadcast is available, thus any option for multicasting or

unicasting is not available even though OPNET Modelerconfigures multicasting and unicasting in the

IEEE802.11a, b protocols. Multicasting is essentiallyimportant in simulating applications where control of the

coordinator over certain nodes is required. The results

presented in this paper represent generally the QoS

parameters using broadcast traffic. However theevaluation of the ETE delay and throughput using

multicasting and unicasting could not be accomplished.

The option of multicasting and broadcasting traffic can bedone by adding or modifying the kernel procedure of the

OPNET ZigBee node model at the network layer so that aparticular node can transmit to one or more nodes in a

network with the same network ID.

B.

Security Model

In some WSN applications simulating security issues

is essential and required especially when the simulated

WSN handles critical information. However, OPNET

simulator does not support or give an option for involving

security models in the simulation of WSN. An encryptionalgorithm such as AES (Advanced Encryption Standard)

[5] can be used to encrypt data packets within the samePAN. Having such encryption algorithm in a ZigBee

WSN will have an impact on the QoS performance of the

entire network since WSNs in general have limited data

rates and bandwidth recourses. The ETE delay and thethroughput simulation results presented here could not be

evaluated with the presence of AES. AES can be included

as an attribute in the application layer of OPNET Modeler

attributes by adapting the application layer kernel of the

OPNET ZigBee model, hence the affect of the AES

algorithm on the QoS parameters could be investigatedand studied in the simulated network.


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C.

Energy Model

Unlike NS-2 and other WSN simulation tools the

OPNET model for ZigBee networks does not support

energy models or the simulation of any energy relatedaspects of WSNs [2]. Evaluating and simulating energy

resources sometimes is an essential factor for estimating

the life-time of a sensor node since the full operation ofthe wireless node depends on internal battery power. The

simulation results presented here generally discuss the

QoS parameters with varying number of nodes. However

the WSN throughput will have an effect energy consumed

in the entire network. This evaluation could not be done

with OPNET Modeler due to the non availability ofenergy model. The new energy model can be introducedat the OPNET ZigBee process level at the MAC layer by

including a new kernel procedure for energy which

evaluates the energy consumed when the node is

accessing the channel and when it is in idle state. In sodoing the operator will then have a clear understanding of

node life-time under certain operating conditions and howthe QoS parameters are affected by the available energy

resources.

D.

Contention – Free Operation Mode

The OPNET model for ZigBee networks does notsupport contention-free operation and slotted operation

mode. Generally, schedule-based protocols are

contention-free and hence energy waste caused by

collisions is eliminated. In addition to that, sensor nodes

require turning their RF transmitters and receivers onduring slots where data is to be transmitted or received(i.e. carrier sensing). The sensor node can turn off its

transmitter and receiver in all other slots, thereby avoiding

overhearing. This results in low-duty-cycle nodeoperations, which may significantly extend the network

lifetime. Schedule-based MAC protocols have several

disadvantages [10] such as missing a reporting eventwhile the node is a sleep or the incorporated delays that

occur between the wakeup and sleep times. The effect of

contention – free mode of operation on the QoS

parameters was not included here. The contention freemode of operation can be included by adding a newfunction at the MAC layer process model of OPNET

ZigBee model which allows the selection between the two

modes of operation.

V. CONCLUSION

The purpose of this paper was to investigate the

performance capabilities of OPNET Modeler insimulating ZigBee WSNs. In general the results presented

here show consistency with other software simulators of

WSNs. It can be concluded that OPNET has good

potential in simulating ZigBee WSNs since it can provide

a vast variety of reports and statistics at different network

layers (particularly at the MAC layer) for an individualnode or for the entire WSN. Further, it was established

that ZigBee WSNs are somewhat easier to deploy and

configure compared to other WSN simulators. The effect

of varying the number of nodes and the use of

handshaking on the performance the ZigBee WSN was

also demonstrated. However it was found that OPNET

ZigBee WSN does not perform well in the physical and

application layers since the essential energy and security

models are not incorporated in the simulation of the

ZigBee WSNs. Potential improvements were proposed tofurther develop OPNET Modeler to compete with other

well known WSNs simulators. These improvements will

enhance OPNET Modeler to cover all aspects of WSNssimulations and investigations for both researchers and

network operators.

REFERENCES

[1] Becker et al, “Comparative Simulations of WSN”, ICT-Mobile

Summit 2008, Cunningham, P., 2008.

[2] Hue et al, “Performance Evaluation of NS-2 Simulator for

Wireless Sensor Networks”, Proceedings of Canadian Conference

on Electrical and Computer Engineering, CCECE 2007,

Vancouver, BC, 2007, pp.1372-1375.

[3] G. Lucio et al "OPNET Modeler and ns-2 - comparing the

accuracy of network simulators for packet-level analysis using a

network testbed", wseas transactions on computers, Vol. 2, No. 3.

(July 2003), pp. 700-707.

[4] Curren, "A survey of simulation in sensor networks",http://www.cs.binghamton.edu/ kang/teaching/cs580s/david.pdf.

[5] Karl, Willig, "Protocols and Architectures for Wireless Sensor

Networks", John Wiley & Sons, Ltd, 2006.

[6] OPNET official website, http://www.opnet.com.

[7] S. Wu, Y. Tseng, "Wireless AD HOC Networking –Personal-

Area, Local Area, and Sensory-Area Networks", Auerbach

Publications, 2007.

[8] Mathioudakis, I et al, "Wireless Sensor Networks: A case study

for Energy Efficient Environmental Monitoring", Proceedings of

Eurosensors conference 2008, Dresden, Germany, September

2008.

[9] K Sohraby, D Minoli, T Znati, “Wireless Sensor Networks

Technology, Protocols, and Applications”, John Wiley & Sons,

2007.

[10] Huang et al, “OPNET Simulation of a Multi-hop Self-organizing

Wireless Sensor Network”, Proceedings of OPNETWORK 2002conference, Washington D.C, 2002.


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