SlideShare a Scribd company logo
1 of 4
Download to read offline
A Simulation Model of IEEE 802.15.4 in OMNeT++
Feng Chen Falko Dressler
Computer Networks and Communication Systems
University of Erlangen-Nuremberg
91058 Erlangen, Germany
{feng.chen,dressler}@informatik.uni-erlangen.de
ABSTRACT
IEEE 802.15.4 defines the physical and MAC layer specifica-
tions for low-rate wireless personal area networks (WPANs).
In order to evaluate its performance, we develop a simulation
model of IEEE 802.15.4 in OMNeT++, which is a popular
simulation platform especially suitable for the simulation of
communication networks. The model consists of two mod-
ules for PHY and MAC layers respectively and supports star
and cluster tree topologies. Our model is built conforming
to the latest version - IEEE Std 802.15.4-2006 and has the
extendibility to the ZigBee protocol stack.
Keywords
IEEE 802.15.4, simulation model, OMNeT++
1. INTRODUCTION
IEEE 802.15.4 [1] is a standard designed for low-rate wireless
personal area networks (LR-WPAN) and defines the spec-
ifications at the physical layer (PHY) and medium access
control (MAC) sublayer. In contrast to wireless local area
network (WLAN), which is standardized by IEEE 802.11
family, LR-WPAN stresses short-range operation, low-data-
rate, energy-efficiency and low-cost. Thus, LR-WPAN has
become one of the most foreseen technologies enabling WSNs.
An example is ZigBee [4], which is an open specification
built on the LR-WPAN standard and targeted at low-cost,
low-data-rate and low-power wireless networking.
OMNeT++ [2] is a public-source, component-based and dis-
crete event simulation environment and is becoming a very
popular simulation platform especially in communication
and networking community. Its primary application area
covers the simulation of communication networks, IT sys-
tems, queuing networks and business processes as well. [5]
has shown that OMNeT++ is very suitable for simulating
wireless sensor networks owing to its modular structure and
using NED language for ease of simulation configuration.
In this paper, we present a simulation model of IEEE 802.15.4
in OMNeT++. It consists of two modules for PHY and
MAC layers respectively and supports simulations of star
and cluster tree topologies. The PHY model implements the
complete functions defined in the specifications. The MAC
model implements three data transfer modes (direct, indi-
rect and GTS), beacon transmission and synchronization,
complete CSMA-CA mechanism and partial PAN manage-
ment functions, like association. To measure energy con-
sumption of nodes. we implement an energy model in the
MAC module. All parameters defined in the specification
except for those for security functions are implemented in
the model and adjustable in the OMNeT++ configuration
file omnetpp.ini or in a C++ source file where the default
values for the majority protocol parameters are stored.
The rest of the paper is organized as follows. In section
2, we give a brief description of 802.15.4. In section 3, the
model of IEEE 802.15.4 in OMNeT++ will be introduced
in detail. Section 4 concludes the paper and give a vision to
the future work.
2. A BRIEF OVERVIEW OF IEEE 802.15.4
In this section, we give a brief overview of IEEE 802.15.4.
Only those parts related to our model are introduced.
The IEEE 802.15.4 network can work in one of three ISM
frequency bands and choose from a total of 27 channels.
Two different types of devices are defined in an LR-WPAN,
a full function device (FFD) and a reduced function device
(RFD). An FFD can talk to any other device and serves as
a PAN coordinator, a coordinator or a device. An RFD can
only talk to an FFD node. The standard supports two net-
work topologies, star and peer-to-peer. In the star network,
the communication occurs only between devices and a single
central controller, called the PAN coordinator, which man-
ages the whole PAN. The peer-to-peer topology also has a
PAN coordinator, however is differs from the star topology
in that any devices can communicate with any other one as
long as they are in range of one another. A special case of
peer-to-peer topology is cluster tree, in which a node talks
only to its parent or children nodes.
To achieve better energy-efficiency, the IEEE 802.15.4 can
operate on beacon-enabled mode, for which a superframe
structure is utilized. A super frame is bounded by period-
ically transmitted beacon frames, which allow nodes to as-
sociate with and synchronize to their coordinators. It con-
MAC
PHY
Traffic
ü CSMA
-
CA
ü BeaconTx
/Rx
ü Direct, indirect and GTS
data transmission
ü Frame filtering and
duplication detection
ü Simplified association
process
ü IFS delay
ü Radio sleeping control
ü Energy model
ü Radio model
ü ED
ü CCA
ü Channel switch
ü PacketTx
/Rx
ü Collision detection
ü Ideal/
lossychannel
Packets generator with
a FIFO queue
Channel
Control
Figure 1: The structure and components of the 802.15.4 model
sists of two parts, active and inactive period. An active
portion is divided into 16 contiguous time slots that form
three parts: the beacon, contention access period (CAP)
and contention-free period (CFP). In CAP, all data trans-
mission should follow a successful execution of the slotted
CSMA-CA algorithm. There are two data transfer modes
defined in CAP, the indirect transmission for downlink data
and the direct transmission for uplink data. In CFP, a de-
vice can communicate with the PAN coordinator directly in
the called guaranteed time slots (GTS) without contending
for the channel using CSMA-CA mechanism. The GTS are
allocated by the PAN coordinator, therefor GTS transfer
mode is only applicable in the star network.
3. DESCRIPTION OF IEEE 802.15.4 MODEL
IN OMNET++
The IEEE 802.15.4 model is developed in the INET frame-
work, which is an open-source communication networks sim-
ulation package for the OMNeT++ simulation environment
and suited for simulations of wired, wireless and ad-hoc net-
works. The architecture of the 802.15.4 model is shown in
Fig. 1. There are three sub models, traffic, MAC and PHY,
each of which is a independent module and inherited from
the basic C++ class cSimpleModule in OMNeT++. The
modules are connected with each other via gates and com-
municate via messages. A snapshot of the model in the
graphical interface of OMNeT++ called Tkenv is shown in
Fig. 2. In the following sub sections, each of these modules
is introduced in detail.
3.1 PHY Module
In the INET framework, a general radio module called Ab-
stractRadio implements the common functionality of the ra-
dio, like packet transmission and reception with collision
detection, channel switch, etc.. We adapt this module to
create a proper radio model conforming to IEEE 802.15.4,
by changing or adding the following contents:
• Redefine radio states: according to the specification,
the radio is modeled with three states: transceiver dis-
abled (TRX OFF), transmitter enabled (TX ON ) and
receiver enabled (RX ON ). The MAC module com-
pletely controls radio operation and can set the radio
into different states via a request primitive. The PHY
module reports the setting result back to the MAC via
a confirm primitive.
• Clear channel access (CCA): the MAC module requires
the PHY to perform CCA via the PLME-CCA.request
primitive. The channel state (busy or idle) is deter-
mined in the model by checking two flags, isRxing and
isTxing, which indicate whether the radio is currently
receiving or transmitting packets. The PHY will set
the flag isRxing by either of the following two facts: a
packet (not a noise) is currently being received or the
current noise level is above the sensitivity value. For
a CCA request, checking flags will take place twice at
both the beginning and the end of a period of 8 sym-
bols. The CCA result will be reported back to the
MAC via a corresponding confirm primitive.
In addition, the PHY module can be configured to work in
any one of a total 27 channels and transmit packets at three
different data rates, as defined in the specification. Dynam-
ical channel switch during the simulation is also supported.
3.2 MAC Module
This is the main module in the whole model and contains
the following three main parts:
3.2.1 Channel access
Channel access is the core function for any MAC protocol.
We have implemented the majority functions and primi-
tives for channel access defined in the specifications, includ-
ing three data transfer modes (direct, indirect and GTS),
MAC frame filtering, detection for duplicate received pack-
ets and the most important part - CSMA-CA mechanism
Figure 2: The 802.15.4 model in TKenv, the graphical interface of OMNeT++
(slotted and unslotted). To make the model more accurate,
we also consider the IFS delay, which is defined as the re-
quired process time for a frame received from the PHY by
the MAC.
3.2.2 Beacon mechanism and PAN management
IEEE 802.15.4 achieves good energy-efficiency owing to its
beaconing mechanism. We implements in our model the
complete beacon transmission and reception for nodes form-
ing star and cluster tree network automatically. For exam-
ple, to simulate a cluster tree topology, user can specify in
the simulation setup phase which node to be the PAN co-
ordinator and when to broadcast its first beacon. During
the simulation running, when those nodes being placed one
hop away from the PAN coordinator receive their first bea-
con from the PAN coordinator, they initiate the association
process. After successful association, if those nodes are not
leaf nodes, they will act as a coordinator and start transmit-
ting beacons to those nodes placed at the next level in the
cluster tree. When all leaf nodes are associated, the network
forming phase is complete. With respect to PAN manage-
ment functions, a simplified association process is modeled,
that is, the node will associate with the coordinator, from
which it receives the first beacon. Furthermore, loss of syn-
chronization due to time drift (e.g. use of oscillator with
±20ppm) can be simulated with our model. Other PAN
management functions, like channel scan and dissociation,
are not considered in our model.
3.2.3 Energy model
Energy consumption is always a major interest in study-
ing MAC protocols like IEEE 802.15.4 designed for energy-
restricted wireless applications. In our model, we measure
only the energy consumed at the radio. To achieve this in
the MAC module, we let the MAC module keep tracking
the current radio state in the PHY module via a message
passing module defined in the INET framework called Noti-
ficationBoard. Therefor we only need to count the total time
that the radio has spent in each state during the simulation.
As long as the radio power for each state is known, the total
energy consumption can be easily calculated. Although in
our model we use a single state RX ON to represent two
possible working state in a real radio, idle listening and re-
ceiving, it is reasonable to assume that radios in these two
states consume approximately the same power.
3.3 Traffic Module
The traffic module is not a part of IEEE 802.15.4, however
we need a traffic generator to run the simulation. This mod-
ule is modeled as the upper layers above the IEEE 802.15.4
model and acts as both a traffic source generating packets
for the MAC module and a traffic sink collecting and ana-
lyzing received packets. The traffic module is inherited from
the existing traffic generator class, which is implemented by
Dietrich Isabel [3] and supports generating traffic with vari-
ous types, including CBR, Exponential, and On-off. It uses
a flexible XML-based parameter structure and supports dy-
namical change of traffic pattern during the simulation.
Table 1: Model Parameters
transmitterPower power for sending packets (mW)
sensitivity carrier sense threshold
thermalNoise base noise level (dBm)
snirThreshold signal/noise threshold
isPANCoor is a PAN coordinator or not
BO beacon order
SO superframe order
startTime when PAN starts beaconing
3.4 Model Parameters
Our model provides a vast amount of parameters adjustable
for conducting a comprehensive study of IEEE 802.15.4.
The default values for the majority protocol parameters are
stored in a single C++ header file. We also put some pa-
rameters, which are most likely to be modified during the
simulation, into the corresponding NED file for each simple
module, so that user can change them conveniently in the
simulation configuration file omnetpp.ini. Those parameters
are listed in Table 1.
4. CONCLUSIONS AND FUTURE WORK
In this paper, we present a simulation model for IEEE Std.
802.15.4 developed in the popular simulation environment
OMNeT++. Except for the PAN management and security
functions, the model implements the majority parameters
and functions defined in the specifications. Compared with
the existing IEEE 802.15.4 model in ns-2, our model is built
conforming to the latest IEEE Std. 802.15.4-2006 and im-
plements the GTS data transfer mode, as well as an energy
model. In the future work, more PAN management func-
tions will be added to support simulating more complicated
scenarios, e.g. mesh topology with ZigBee routing. A se-
ries of simulations for evaluating the performance of IEEE
802.15.4 in the QoS aspect are running. We also plan to
integrate the security functions into our model and inves-
tigate how the performances are affected by those security
mechanisms.
5. REFERENCES
[1] IEEE std. 802.15.4-2006, wireless medium access
control (MAC) and physical layer (PHY) specifications
for low-rate wireless personal area networks (WPANs).
[2] OMNeT++ homepage. [online]. available:
http://www.omnetpp.org.
[3] OMNeT++ page at Isabel Dietrich’s homepage.
[online]. available: http://www7.informatik.uni-
erlangen.de/ isabel/omnet/.
[4] ZigBee Alliance homepage. [online]. available:
http://www.zigbee.org.
[5] C. Mallanda, A. Suri, V. Kunchakarra, S. S. Iyengar,
R. Kannan, A. Durresi, and S. Sastry. Simulating
wireless sensor networks with omnet++.

More Related Content

What's hot

Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...
Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...
Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...IDES Editor
 
Simulating the triba noc architecture
Simulating the triba noc architectureSimulating the triba noc architecture
Simulating the triba noc architectureijmnct
 
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...ijceronline
 
Architecture neural network deep optimizing based on self organizing feature ...
Architecture neural network deep optimizing based on self organizing feature ...Architecture neural network deep optimizing based on self organizing feature ...
Architecture neural network deep optimizing based on self organizing feature ...journalBEEI
 
Distributed vertex cover
Distributed vertex coverDistributed vertex cover
Distributed vertex coverIJCNCJournal
 
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILE
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILE2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILE
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILEZac Darcy
 
PROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKS
PROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKSPROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKS
PROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKSZac Darcy
 
Image transmission in wireless sensor networks
Image transmission in wireless sensor networksImage transmission in wireless sensor networks
Image transmission in wireless sensor networkseSAT Publishing House
 
Hidden Layer Leraning Vector Quantizatio
Hidden Layer Leraning Vector Quantizatio Hidden Layer Leraning Vector Quantizatio
Hidden Layer Leraning Vector Quantizatio Armando Vieira
 
Review of crosstalk free Network
Review of crosstalk free NetworkReview of crosstalk free Network
Review of crosstalk free NetworkIJMER
 
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
 
Designing Run-Time Environments to have Predefined Global Dynamics
Designing  Run-Time  Environments to have Predefined Global DynamicsDesigning  Run-Time  Environments to have Predefined Global Dynamics
Designing Run-Time Environments to have Predefined Global DynamicsIJCNCJournal
 
A survey research summary on neural networks
A survey research summary on neural networksA survey research summary on neural networks
A survey research summary on neural networkseSAT Publishing House
 
Image Segmentation Using Two Weighted Variable Fuzzy K Means
Image Segmentation Using Two Weighted Variable Fuzzy K MeansImage Segmentation Using Two Weighted Variable Fuzzy K Means
Image Segmentation Using Two Weighted Variable Fuzzy K MeansEditor IJCATR
 
Simulator for Energy Efficient Clustering in Mobile Ad Hoc Networks
Simulator for Energy Efficient Clustering in Mobile Ad Hoc NetworksSimulator for Energy Efficient Clustering in Mobile Ad Hoc Networks
Simulator for Energy Efficient Clustering in Mobile Ad Hoc Networkscscpconf
 
Pretzel: optimized Machine Learning framework for low-latency and high throu...
Pretzel: optimized Machine Learning framework for  low-latency and high throu...Pretzel: optimized Machine Learning framework for  low-latency and high throu...
Pretzel: optimized Machine Learning framework for low-latency and high throu...NECST Lab @ Politecnico di Milano
 
Multistage interconnection networks a transition to optical
Multistage interconnection networks a transition to opticalMultistage interconnection networks a transition to optical
Multistage interconnection networks a transition to opticaleSAT Publishing House
 
From Simulation to Online Gaming: the need for adaptive solutions
From Simulation to Online Gaming: the need for adaptive solutions From Simulation to Online Gaming: the need for adaptive solutions
From Simulation to Online Gaming: the need for adaptive solutions Gabriele D'Angelo
 

What's hot (20)

Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...
Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...
Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...
 
Simulating the triba noc architecture
Simulating the triba noc architectureSimulating the triba noc architecture
Simulating the triba noc architecture
 
A040101001006
A040101001006A040101001006
A040101001006
 
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...
 
Architecture neural network deep optimizing based on self organizing feature ...
Architecture neural network deep optimizing based on self organizing feature ...Architecture neural network deep optimizing based on self organizing feature ...
Architecture neural network deep optimizing based on self organizing feature ...
 
Distributed vertex cover
Distributed vertex coverDistributed vertex cover
Distributed vertex cover
 
Iv3515241527
Iv3515241527Iv3515241527
Iv3515241527
 
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILE
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILE2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILE
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILE
 
PROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKS
PROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKSPROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKS
PROTECTOR CONTROL PC-AODV-BH IN THE AD HOC NETWORKS
 
Image transmission in wireless sensor networks
Image transmission in wireless sensor networksImage transmission in wireless sensor networks
Image transmission in wireless sensor networks
 
Hidden Layer Leraning Vector Quantizatio
Hidden Layer Leraning Vector Quantizatio Hidden Layer Leraning Vector Quantizatio
Hidden Layer Leraning Vector Quantizatio
 
Review of crosstalk free Network
Review of crosstalk free NetworkReview of crosstalk free Network
Review of crosstalk free Network
 
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...
 
Designing Run-Time Environments to have Predefined Global Dynamics
Designing  Run-Time  Environments to have Predefined Global DynamicsDesigning  Run-Time  Environments to have Predefined Global Dynamics
Designing Run-Time Environments to have Predefined Global Dynamics
 
A survey research summary on neural networks
A survey research summary on neural networksA survey research summary on neural networks
A survey research summary on neural networks
 
Image Segmentation Using Two Weighted Variable Fuzzy K Means
Image Segmentation Using Two Weighted Variable Fuzzy K MeansImage Segmentation Using Two Weighted Variable Fuzzy K Means
Image Segmentation Using Two Weighted Variable Fuzzy K Means
 
Simulator for Energy Efficient Clustering in Mobile Ad Hoc Networks
Simulator for Energy Efficient Clustering in Mobile Ad Hoc NetworksSimulator for Energy Efficient Clustering in Mobile Ad Hoc Networks
Simulator for Energy Efficient Clustering in Mobile Ad Hoc Networks
 
Pretzel: optimized Machine Learning framework for low-latency and high throu...
Pretzel: optimized Machine Learning framework for  low-latency and high throu...Pretzel: optimized Machine Learning framework for  low-latency and high throu...
Pretzel: optimized Machine Learning framework for low-latency and high throu...
 
Multistage interconnection networks a transition to optical
Multistage interconnection networks a transition to opticalMultistage interconnection networks a transition to optical
Multistage interconnection networks a transition to optical
 
From Simulation to Online Gaming: the need for adaptive solutions
From Simulation to Online Gaming: the need for adaptive solutions From Simulation to Online Gaming: the need for adaptive solutions
From Simulation to Online Gaming: the need for adaptive solutions
 

Similar to A simulation model of ieee 802.15.4 in om ne t++

Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...ijsrd.com
 
Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks
Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc NetworksTowards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks
Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networksambitlick
 
Efectos de nodos ieee 802.15.4 zig bee
Efectos de nodos ieee 802.15.4 zig beeEfectos de nodos ieee 802.15.4 zig bee
Efectos de nodos ieee 802.15.4 zig beeGregorio Toro Rivera
 
Ccna PPT
Ccna PPTCcna PPT
Ccna PPTAIRTEL
 
Traffic Engineering in Metro Ethernet
Traffic Engineering in Metro EthernetTraffic Engineering in Metro Ethernet
Traffic Engineering in Metro EthernetCSCJournals
 
7 satishkumar final_paper--75-84
7 satishkumar final_paper--75-847 satishkumar final_paper--75-84
7 satishkumar final_paper--75-84Alexander Decker
 
Optical networks
Optical networks Optical networks
Optical networks ashok mane
 
A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...
A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...
A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...IDES Editor
 

Similar to A simulation model of ieee 802.15.4 in om ne t++ (20)

R,aouami wccs
R,aouami wccsR,aouami wccs
R,aouami wccs
 
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...
 
Hb3512341239
Hb3512341239Hb3512341239
Hb3512341239
 
Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks
Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc NetworksTowards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks
Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks
 
Efectos de nodos ieee 802.15.4 zig bee
Efectos de nodos ieee 802.15.4 zig beeEfectos de nodos ieee 802.15.4 zig bee
Efectos de nodos ieee 802.15.4 zig bee
 
mn paper
mn papermn paper
mn paper
 
Ccna day1
Ccna day1Ccna day1
Ccna day1
 
Ccna day 1
Ccna day 1Ccna day 1
Ccna day 1
 
Ccna day1
Ccna day1Ccna day1
Ccna day1
 
Ccna day1
Ccna day1Ccna day1
Ccna day1
 
Ccna day1-130802165909-phpapp01
Ccna day1-130802165909-phpapp01Ccna day1-130802165909-phpapp01
Ccna day1-130802165909-phpapp01
 
Ccna PPT
Ccna PPTCcna PPT
Ccna PPT
 
Traffic Engineering in Metro Ethernet
Traffic Engineering in Metro EthernetTraffic Engineering in Metro Ethernet
Traffic Engineering in Metro Ethernet
 
7 satishkumar final_paper--75-84
7 satishkumar final_paper--75-847 satishkumar final_paper--75-84
7 satishkumar final_paper--75-84
 
Ccna day1
Ccna day1Ccna day1
Ccna day1
 
Optical networks
Optical networks Optical networks
Optical networks
 
C C N A Day1
C C N A  Day1C C N A  Day1
C C N A Day1
 
Ccna day1
Ccna day1Ccna day1
Ccna day1
 
Ccna day1
Ccna day1Ccna day1
Ccna day1
 
A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...
A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...
A New Approach to Improve the Efficiency of Distributed Scheduling in IEEE 80...
 

Recently uploaded

Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Celine George
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptxVS Mahajan Coaching Centre
 
History Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptxHistory Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptxsocialsciencegdgrohi
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxAvyJaneVismanos
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxOH TEIK BIN
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxSayali Powar
 
internship ppt on smartinternz platform as salesforce developer
internship ppt on smartinternz platform as salesforce developerinternship ppt on smartinternz platform as salesforce developer
internship ppt on smartinternz platform as salesforce developerunnathinaik
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Celine George
 
Pharmacognosy Flower 3. Compositae 2023.pdf
Pharmacognosy Flower 3. Compositae 2023.pdfPharmacognosy Flower 3. Compositae 2023.pdf
Pharmacognosy Flower 3. Compositae 2023.pdfMahmoud M. Sallam
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformChameera Dedduwage
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTiammrhaywood
 
Science lesson Moon for 4th quarter lesson
Science lesson Moon for 4th quarter lessonScience lesson Moon for 4th quarter lesson
Science lesson Moon for 4th quarter lessonJericReyAuditor
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsanshu789521
 
Painted Grey Ware.pptx, PGW Culture of India
Painted Grey Ware.pptx, PGW Culture of IndiaPainted Grey Ware.pptx, PGW Culture of India
Painted Grey Ware.pptx, PGW Culture of IndiaVirag Sontakke
 
Science 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its CharacteristicsScience 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its CharacteristicsKarinaGenton
 
Class 11 Legal Studies Ch-1 Concept of State .pdf
Class 11 Legal Studies Ch-1 Concept of State .pdfClass 11 Legal Studies Ch-1 Concept of State .pdf
Class 11 Legal Studies Ch-1 Concept of State .pdfakmcokerachita
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxNirmalaLoungPoorunde1
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)eniolaolutunde
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxRaymartEstabillo3
 

Recently uploaded (20)

Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
 
History Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptxHistory Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptx
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptx
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
 
internship ppt on smartinternz platform as salesforce developer
internship ppt on smartinternz platform as salesforce developerinternship ppt on smartinternz platform as salesforce developer
internship ppt on smartinternz platform as salesforce developer
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17
 
Pharmacognosy Flower 3. Compositae 2023.pdf
Pharmacognosy Flower 3. Compositae 2023.pdfPharmacognosy Flower 3. Compositae 2023.pdf
Pharmacognosy Flower 3. Compositae 2023.pdf
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy Reform
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
 
Science lesson Moon for 4th quarter lesson
Science lesson Moon for 4th quarter lessonScience lesson Moon for 4th quarter lesson
Science lesson Moon for 4th quarter lesson
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha elections
 
Painted Grey Ware.pptx, PGW Culture of India
Painted Grey Ware.pptx, PGW Culture of IndiaPainted Grey Ware.pptx, PGW Culture of India
Painted Grey Ware.pptx, PGW Culture of India
 
Science 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its CharacteristicsScience 7 - LAND and SEA BREEZE and its Characteristics
Science 7 - LAND and SEA BREEZE and its Characteristics
 
Class 11 Legal Studies Ch-1 Concept of State .pdf
Class 11 Legal Studies Ch-1 Concept of State .pdfClass 11 Legal Studies Ch-1 Concept of State .pdf
Class 11 Legal Studies Ch-1 Concept of State .pdf
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptx
 
Staff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSDStaff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSD
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
 

A simulation model of ieee 802.15.4 in om ne t++

  • 1. A Simulation Model of IEEE 802.15.4 in OMNeT++ Feng Chen Falko Dressler Computer Networks and Communication Systems University of Erlangen-Nuremberg 91058 Erlangen, Germany {feng.chen,dressler}@informatik.uni-erlangen.de ABSTRACT IEEE 802.15.4 defines the physical and MAC layer specifica- tions for low-rate wireless personal area networks (WPANs). In order to evaluate its performance, we develop a simulation model of IEEE 802.15.4 in OMNeT++, which is a popular simulation platform especially suitable for the simulation of communication networks. The model consists of two mod- ules for PHY and MAC layers respectively and supports star and cluster tree topologies. Our model is built conforming to the latest version - IEEE Std 802.15.4-2006 and has the extendibility to the ZigBee protocol stack. Keywords IEEE 802.15.4, simulation model, OMNeT++ 1. INTRODUCTION IEEE 802.15.4 [1] is a standard designed for low-rate wireless personal area networks (LR-WPAN) and defines the spec- ifications at the physical layer (PHY) and medium access control (MAC) sublayer. In contrast to wireless local area network (WLAN), which is standardized by IEEE 802.11 family, LR-WPAN stresses short-range operation, low-data- rate, energy-efficiency and low-cost. Thus, LR-WPAN has become one of the most foreseen technologies enabling WSNs. An example is ZigBee [4], which is an open specification built on the LR-WPAN standard and targeted at low-cost, low-data-rate and low-power wireless networking. OMNeT++ [2] is a public-source, component-based and dis- crete event simulation environment and is becoming a very popular simulation platform especially in communication and networking community. Its primary application area covers the simulation of communication networks, IT sys- tems, queuing networks and business processes as well. [5] has shown that OMNeT++ is very suitable for simulating wireless sensor networks owing to its modular structure and using NED language for ease of simulation configuration. In this paper, we present a simulation model of IEEE 802.15.4 in OMNeT++. It consists of two modules for PHY and MAC layers respectively and supports simulations of star and cluster tree topologies. The PHY model implements the complete functions defined in the specifications. The MAC model implements three data transfer modes (direct, indi- rect and GTS), beacon transmission and synchronization, complete CSMA-CA mechanism and partial PAN manage- ment functions, like association. To measure energy con- sumption of nodes. we implement an energy model in the MAC module. All parameters defined in the specification except for those for security functions are implemented in the model and adjustable in the OMNeT++ configuration file omnetpp.ini or in a C++ source file where the default values for the majority protocol parameters are stored. The rest of the paper is organized as follows. In section 2, we give a brief description of 802.15.4. In section 3, the model of IEEE 802.15.4 in OMNeT++ will be introduced in detail. Section 4 concludes the paper and give a vision to the future work. 2. A BRIEF OVERVIEW OF IEEE 802.15.4 In this section, we give a brief overview of IEEE 802.15.4. Only those parts related to our model are introduced. The IEEE 802.15.4 network can work in one of three ISM frequency bands and choose from a total of 27 channels. Two different types of devices are defined in an LR-WPAN, a full function device (FFD) and a reduced function device (RFD). An FFD can talk to any other device and serves as a PAN coordinator, a coordinator or a device. An RFD can only talk to an FFD node. The standard supports two net- work topologies, star and peer-to-peer. In the star network, the communication occurs only between devices and a single central controller, called the PAN coordinator, which man- ages the whole PAN. The peer-to-peer topology also has a PAN coordinator, however is differs from the star topology in that any devices can communicate with any other one as long as they are in range of one another. A special case of peer-to-peer topology is cluster tree, in which a node talks only to its parent or children nodes. To achieve better energy-efficiency, the IEEE 802.15.4 can operate on beacon-enabled mode, for which a superframe structure is utilized. A super frame is bounded by period- ically transmitted beacon frames, which allow nodes to as- sociate with and synchronize to their coordinators. It con-
  • 2. MAC PHY Traffic ü CSMA - CA ü BeaconTx /Rx ü Direct, indirect and GTS data transmission ü Frame filtering and duplication detection ü Simplified association process ü IFS delay ü Radio sleeping control ü Energy model ü Radio model ü ED ü CCA ü Channel switch ü PacketTx /Rx ü Collision detection ü Ideal/ lossychannel Packets generator with a FIFO queue Channel Control Figure 1: The structure and components of the 802.15.4 model sists of two parts, active and inactive period. An active portion is divided into 16 contiguous time slots that form three parts: the beacon, contention access period (CAP) and contention-free period (CFP). In CAP, all data trans- mission should follow a successful execution of the slotted CSMA-CA algorithm. There are two data transfer modes defined in CAP, the indirect transmission for downlink data and the direct transmission for uplink data. In CFP, a de- vice can communicate with the PAN coordinator directly in the called guaranteed time slots (GTS) without contending for the channel using CSMA-CA mechanism. The GTS are allocated by the PAN coordinator, therefor GTS transfer mode is only applicable in the star network. 3. DESCRIPTION OF IEEE 802.15.4 MODEL IN OMNET++ The IEEE 802.15.4 model is developed in the INET frame- work, which is an open-source communication networks sim- ulation package for the OMNeT++ simulation environment and suited for simulations of wired, wireless and ad-hoc net- works. The architecture of the 802.15.4 model is shown in Fig. 1. There are three sub models, traffic, MAC and PHY, each of which is a independent module and inherited from the basic C++ class cSimpleModule in OMNeT++. The modules are connected with each other via gates and com- municate via messages. A snapshot of the model in the graphical interface of OMNeT++ called Tkenv is shown in Fig. 2. In the following sub sections, each of these modules is introduced in detail. 3.1 PHY Module In the INET framework, a general radio module called Ab- stractRadio implements the common functionality of the ra- dio, like packet transmission and reception with collision detection, channel switch, etc.. We adapt this module to create a proper radio model conforming to IEEE 802.15.4, by changing or adding the following contents: • Redefine radio states: according to the specification, the radio is modeled with three states: transceiver dis- abled (TRX OFF), transmitter enabled (TX ON ) and receiver enabled (RX ON ). The MAC module com- pletely controls radio operation and can set the radio into different states via a request primitive. The PHY module reports the setting result back to the MAC via a confirm primitive. • Clear channel access (CCA): the MAC module requires the PHY to perform CCA via the PLME-CCA.request primitive. The channel state (busy or idle) is deter- mined in the model by checking two flags, isRxing and isTxing, which indicate whether the radio is currently receiving or transmitting packets. The PHY will set the flag isRxing by either of the following two facts: a packet (not a noise) is currently being received or the current noise level is above the sensitivity value. For a CCA request, checking flags will take place twice at both the beginning and the end of a period of 8 sym- bols. The CCA result will be reported back to the MAC via a corresponding confirm primitive. In addition, the PHY module can be configured to work in any one of a total 27 channels and transmit packets at three different data rates, as defined in the specification. Dynam- ical channel switch during the simulation is also supported. 3.2 MAC Module This is the main module in the whole model and contains the following three main parts: 3.2.1 Channel access Channel access is the core function for any MAC protocol. We have implemented the majority functions and primi- tives for channel access defined in the specifications, includ- ing three data transfer modes (direct, indirect and GTS), MAC frame filtering, detection for duplicate received pack- ets and the most important part - CSMA-CA mechanism
  • 3. Figure 2: The 802.15.4 model in TKenv, the graphical interface of OMNeT++ (slotted and unslotted). To make the model more accurate, we also consider the IFS delay, which is defined as the re- quired process time for a frame received from the PHY by the MAC. 3.2.2 Beacon mechanism and PAN management IEEE 802.15.4 achieves good energy-efficiency owing to its beaconing mechanism. We implements in our model the complete beacon transmission and reception for nodes form- ing star and cluster tree network automatically. For exam- ple, to simulate a cluster tree topology, user can specify in the simulation setup phase which node to be the PAN co- ordinator and when to broadcast its first beacon. During the simulation running, when those nodes being placed one hop away from the PAN coordinator receive their first bea- con from the PAN coordinator, they initiate the association process. After successful association, if those nodes are not leaf nodes, they will act as a coordinator and start transmit- ting beacons to those nodes placed at the next level in the cluster tree. When all leaf nodes are associated, the network forming phase is complete. With respect to PAN manage- ment functions, a simplified association process is modeled, that is, the node will associate with the coordinator, from which it receives the first beacon. Furthermore, loss of syn- chronization due to time drift (e.g. use of oscillator with ±20ppm) can be simulated with our model. Other PAN management functions, like channel scan and dissociation, are not considered in our model. 3.2.3 Energy model Energy consumption is always a major interest in study- ing MAC protocols like IEEE 802.15.4 designed for energy- restricted wireless applications. In our model, we measure only the energy consumed at the radio. To achieve this in the MAC module, we let the MAC module keep tracking the current radio state in the PHY module via a message passing module defined in the INET framework called Noti- ficationBoard. Therefor we only need to count the total time that the radio has spent in each state during the simulation. As long as the radio power for each state is known, the total energy consumption can be easily calculated. Although in our model we use a single state RX ON to represent two possible working state in a real radio, idle listening and re- ceiving, it is reasonable to assume that radios in these two states consume approximately the same power. 3.3 Traffic Module The traffic module is not a part of IEEE 802.15.4, however we need a traffic generator to run the simulation. This mod- ule is modeled as the upper layers above the IEEE 802.15.4 model and acts as both a traffic source generating packets for the MAC module and a traffic sink collecting and ana- lyzing received packets. The traffic module is inherited from the existing traffic generator class, which is implemented by Dietrich Isabel [3] and supports generating traffic with vari- ous types, including CBR, Exponential, and On-off. It uses a flexible XML-based parameter structure and supports dy- namical change of traffic pattern during the simulation.
  • 4. Table 1: Model Parameters transmitterPower power for sending packets (mW) sensitivity carrier sense threshold thermalNoise base noise level (dBm) snirThreshold signal/noise threshold isPANCoor is a PAN coordinator or not BO beacon order SO superframe order startTime when PAN starts beaconing 3.4 Model Parameters Our model provides a vast amount of parameters adjustable for conducting a comprehensive study of IEEE 802.15.4. The default values for the majority protocol parameters are stored in a single C++ header file. We also put some pa- rameters, which are most likely to be modified during the simulation, into the corresponding NED file for each simple module, so that user can change them conveniently in the simulation configuration file omnetpp.ini. Those parameters are listed in Table 1. 4. CONCLUSIONS AND FUTURE WORK In this paper, we present a simulation model for IEEE Std. 802.15.4 developed in the popular simulation environment OMNeT++. Except for the PAN management and security functions, the model implements the majority parameters and functions defined in the specifications. Compared with the existing IEEE 802.15.4 model in ns-2, our model is built conforming to the latest IEEE Std. 802.15.4-2006 and im- plements the GTS data transfer mode, as well as an energy model. In the future work, more PAN management func- tions will be added to support simulating more complicated scenarios, e.g. mesh topology with ZigBee routing. A se- ries of simulations for evaluating the performance of IEEE 802.15.4 in the QoS aspect are running. We also plan to integrate the security functions into our model and inves- tigate how the performances are affected by those security mechanisms. 5. REFERENCES [1] IEEE std. 802.15.4-2006, wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (WPANs). [2] OMNeT++ homepage. [online]. available: http://www.omnetpp.org. [3] OMNeT++ page at Isabel Dietrich’s homepage. [online]. available: http://www7.informatik.uni- erlangen.de/ isabel/omnet/. [4] ZigBee Alliance homepage. [online]. available: http://www.zigbee.org. [5] C. Mallanda, A. Suri, V. Kunchakarra, S. S. Iyengar, R. Kannan, A. Durresi, and S. Sastry. Simulating wireless sensor networks with omnet++.