SWAN A Simulator for Wireless Ad Hoc Networks

1
SWAN A Simulator for Wireless Ad Hoc Networks

• L. Felipe Perrone ltperrone_at_bucknell.edugt
• Dept. of Computer Science
• Bucknell University, Lewisburg, PA, U.S.A.

2
What is Simulation?
System
input
output
If System is complex and defies mathematical
analysis and/or is hard to control and to observe
in experimental studies, one is better off
constructing a model of the system and using it
in a simulation study. Simulation is technique
that replicates the behavior of a system using a
model that approximates reality. It allows one to
do experimental studies with the model and learn
more about the system.
3
Example Simulating a Bank
Bank
customers with transactions pending
customers with transactions completed
Imagine that the management wants to optimize the
operation of the bank. Say that they want to make
the customers experience better and minimize
their costs. Observe the real system and create
an abstraction that describes how things happen
customers arrive unpredictably, they enter a
queue to perform a transaction, the teller serves
them, and they leave the system.
4
Experimenting with the Bank Model
Simulating these two models, we can answer
questions without much mathematical analysis -
Which bank model serves more customers per unit
of time? - What is the average time that a
customer spends in a bank?
l
2m
Bank
Model
m
l
m
Bank
Model
5
Wireless Ad-Hoc Networks

• Battery operated computing devices.
• Communication happens via radio or laser beams.
• Nodes are free to move.
• Single or multihop.
• Automatic configuration.
• No need for infrastructure.

6
Who Needs Simulation?

• We have a complex system that defies
  mathematical analysis.
• The system has several components tightly
  inter-connected.
• In order to validate and verify the correctness
  of the system, it will be necessary to run a good
  number of experiments.
• We wish to be able to have tight control over
  experimental conditions and to be able to
  reproduce them.

7
Wish List for an WAN Simulator

• Accuracy
• Detail
• Completeness
• Performance
• Scalability

8
Structure of a Wireless Ad Hoc Network Model
(macro view)
Environment Sub-models
XDIM
Space geometry, terrain Mobility
single model, mixed models Propagation
computational simplicity (performance),
accuracy (validity)
YDIM
9
Geometry Models

• Simple

• Torus

10
Mobility Models

• Random Waypoint Model (individual)

Choose a random destination (x,y).
Choose a random speed Umin,max.
Mobile goes to (x,y).
When mobile arrives at (x,y), it pauses for time
p.
Repeat.
PS Mobile moves in steps of time t.
11
Mobility Models

• Group

Choose a random destination (x,y) for the group
center.
All mobiles take one step toward (x,y).
Repeat.
PS Mobile moves in steps of time t.
12
Mobility Models

• Trace

(x1,y1)
Each mobile follows a predermined trajectory
defined when the simulation first starts. M1
(x1,y1), (x2,y2), (x3,y3), ...
(x3,y3)
(x2,y2)
PS Mobile moves in steps of time t.
13
Propagation Models

• Friis Free-Space No antenna height. Single wave
  front. Assumes line of sight is always possible.
  (Deterministic)

TX
RX

• Two-Ray Ground Reflection Antenna heights
  considered. Assumes one wave front follows line
  of sight and another bounces off the ground.
  (Deterministic)

TX
RX

• Shadowing Adds a stochastic component to Friis
  Free-Space. (Stochastic)

TX
RX
14
Energy Model

• We implement the recommendations of the model
  developed by Laura Marie Feeney for IEEE 802.11b
  based on empirical measurements.
• There are different energy budgets for message
  transmission, message reception, and radio idle
  time.

15
Structure of a Wireless Ad Hoc Network Model
(micro view)
heterogeneous or homogenous network
Network Node Sub-models
Physical Layer radio sensing, bit
transmission (SNRT, BER) MAC Layer
retransmissions, contention, throughput
(IEEE 802.11) Network Layer routing
algorithms (AODV, DSR) Application Layer
traffic generation or direct execution of
real application (CBR, VBR)
APP
APP
APP
NET
NET
NET
MAC
MAC
MAC
PHY
PHY
PHY
RADIO PROPAGATION SUB-MODEL
16
The Architecture of SWAN
Physical Process
read terrain features
Protocol Graph
Terrain Model
OS Model (DaSSF Runtime Kernel)
memory
time
run thread
Host model
read terrain features
RF Channel Model
17
The Scalable Simulation Framework (SSF)
http//www.ssfnet.org
Entity container for state variables
SSF
outChannel
Process entitys state evolution
endpoints of communication links between entities
Event messages between entities
inChannel
SSF is not a simulator its a specification with
bindings for Java and C.
18
SSF Modeling
Entity A
Entity B
outChannel
inChannel
state
state
Event
process
process
inChannel
outChannel
Timeline
A.alignto(B)
Channels have an associated delay which is used
by the kernel to determine lookahead for parallel
simulation. Channels are mapped to one
another. Obviously large models would be painful
to construct with this mechanism alone enter DML
(Domain Modeling Language).
19
The DaSSF Implementation

• An SSF-compliant simulator requires
• Fast threading mechanism,
• Efficient memory utilization,
• Portable runs on sequential and parallel
  machines (shared and distributed memory)
  IRIX/Solaris/SunOS/Linux/OSF.

DaSSF has evolved into iSSF and will see another
incarnation in the near future. It was developed
and is maintained by Jason Liu (Colorado School
of Mines).
20
DML and Design Patterns

• WIRELESS_NODE
• ID 1
• xpos 0
• ypos 0
• battery 1000.0
• graph
• ProtocolSession
• name "app" use "app.sensor-session"
• inter_arrival_time 0.002400
• packet_size 100
• ProtocolSession
• name "net" use "net.aodv-session"
• ProtocolSession
• name "mac" use "mac.mac-802-11-session
• ProtocolSession

The model is described by a hierarchical list of
key-attribute pairs. Each key is looked up in a
database, a class is fetched, an object is
constructed, and the list of attributes is passed
to the corresponding constructor of the
object. The model is constructed from the DML
specification.
21
SSFNET-like Architecture
Our host descriptions were borrowed from the
architecture of SSFNET.
Protocol Graph

• A ProtocolSession models a protocol layer (as in
  the ISO/OSI reference model).
• A ProtocolGraph is a list of ProtocolSessions and
  models the complete protocol stack in a host.
• Adjacent ProtocolSessions communicate by
  exchanging ProtocolMessages.

Application Session
Transport Session
Network Session
Link Session
PHY Session
22
The ProtocolSession API
ProtocolSession N1
A ProtocolSession is a class that defines three
methods pop, push and control. An element
higher in the stack can send it messages invoking
push. An element lower in the stack can send it
messages invoking pop. Anything that is not
related to the protocol models is communicated
using control.
(pop)
ProtocolSession N
push
control
pop
(push)
ProtocolSession N-1
23
ProtocolMessage
Application
Network
MAC
PHY
Data Message
RouterIP Header
MAC Header
PHY Header
Radio Frame
24
The development of SWAN
Project started in 2000. First milestone The
simulation of 10,000 nodes running WiroKit, a
proprietary routing algorithm developed by BBN
Technologies. Second milestone Used in the
study of a high-performance model for IEEE
802.11b channel. Third milestone Used as
substrate in the development of a simulator for
Berkeley motes running TinyOS. Fourth milestone
Used in an experimental study of lookahead
enhancement techniques. Fifth milestone Use in a
study of best practices for modeling and
simulation of wireless ad ho networks. Sixth
milestone Used in a study of attacks on wireless
ad hoc networks.
25
Validating and Verifying SWAN

• We looked for simulation studies done with other
  simulators that we could use as reference to
  validate SWAN.
• Roadblock We found it very difficult to repeat
  previously published studies because we could not
  obtain information on all their settings (models
  and/or parameters). At times, we also failed to
  understand why certain parameter values had been
  chosen and perpetuated in the community.
• Roadblock We could not find incontrovertible
  evidence that the simulators used in those
  studies had been validated.
• We resorted to comparing SWAN models to those of
  other simulators only to discover inconsistencies
  or errors in their models.

26
Crisis, what crisis?

• Pawlikowski et al On credibility of simulation
  studies of telecommunication networks. IEEE
  Communications Magazine 40 (1)
• An opinion is spreading that one cannot rely on
  the majority of the published results on
  performance evaluation studies of
  telecommunication networks based on stochastic
  simulation, since they lack credibility. Indeed,
  the spread of this phenomenon is so wide that one
  can speak about a deep crisis of credibility.

27
Crisis indeed...

• Kotz et al. The mistaken axioms of
  wireless-network research. Technical Report
  TR2003-467, Dept. of Computer Science, Dartmouth
  College, July, 2003
• The Flat Earth model of the world is
  surprisingly popular all radios have circular
  range, have perfect coverage in that range, and
  travel on a two-dimensional plane. CMU's ns2
  radio models are better but still fail to
  represent many aspects of realistic radio
  networks, including hills, obstacles, link
  asymmetries, and unpredictable fading. We briefly
  argue that key axioms'' of these types of
  propagation models lead to simulation results
  that do not adequately reflect real behavior of
  ad-hoc networks, and hence to network protocols
  that may not work well (or at all) in reality.

28
Why is it so difficult?

• Models for a wireless networks are complex and
  have many, many parameters. Articles in print
  cant afford to list all the parameters used in a
  study.
• There isnt a general consensus on the
  appropriate composition of the model (i.e.
  protocol stack) for wireless networks.
• Were not all speaking the same language all the
  time people may refer to the name of a
  well-known model and actually implement a
  different one (the terminology is sometimes
  perverted).
• Some of the people doing simulations lack
  wireless networking expertise (improper
  modeling), while others who have that expertise
  dont understand much about simulation (improper
  output analysis).

29
Exemple Experimental Scenario

• RF propagation 2-ray ground reflection, antenna
  height 1.5m, tx power 15dBm, SNR threshold packet
  reception.
• Mobility density 7 neighbors per node, initial
  deployment triangular, stationary (pauseH,
  minmax0), low (pause60s, min1, max3), high
  (pause0, min1, max10).
• Traffic generation variation of CBR session
  length60s, ist20s, destination is random for
  each session, CBR for each session, packet
  size512 octets, vary packet rates to produce
  16kbps, 56kbps, and 300kbps.

Protocol stack IEEE 802.11b PHY (message
retraining modem capture), IEEE 802.11b MAC
(DCF), ARP, IP, AODV routing. Arena size
variable changed according to the number of
nodes simulated to maintain constant density of 7
neighbors per node. Replications 10 runs with
different seeds for every random stream in the
model. For all metrics estimated, we produced 95
confidence intervals. Scale 20, 30, 40, and 50
nodes.
30
Case Study mobility model

• Yoon et al. Random waypoint considered harmful.
  INFOCOM 2003.
• Demonstrates how a bad choice of parameters can
  lead to a mobile network that tends to become
  stationary (no steady state).
• Called out attention to the fact that the vast
  majority of simulation studies with wireless
  networks ignores the ramp-up period in their
  sub-models.

31
The impact of mobility transient on network
metrics

• We verified that using data deletion to avoid the
  mobility transient led to significant changes in
  relative error
• - from 5 to 30 in packet end-to-end delay,
• - from 5 to 30 in the ratio of data to
  control packets sent,
• - up to 10 in packet delivery ratio.
• Interesting results with algorithms for
  estimation of when steady-state is reached were
  presented at WSC 03
• Bause Eickhoff. Truncation Point Estimation
  Using Multiple Replications in Parallel.
• PS Our study shows that transients due to the
  ramp-up effect in traffic, further compromise the
  correctness of network metrics. (Perrone, Yuan,
  and Nicol. Best Practices in Modeling and
  Simulation of Wireless Networks. Winter
  Simulation Conference 2003).

32
Lesson learned

• The simulation framework should be flexible
  enough in the collection of statistics to allow
  for data deletion. SWAN allows the modeler to
  define estimates for mobility and for network
  transient.
• All the statistics we collect are stored in data
  types derived from a base class that takes
  truncation point in time as a parameter. Only the
  values recorded after the truncation point are
  kept.
• In our experiments we run several simulations
  just to determine the right truncation point. It
  would be beneficial to automate this.
• Question How many simulation replications does
  one need to produce reasonable estimates of 95
  confidence of a given metric?

33
Case study composition of the protocol stack

• Broch et al. A performance comparison of
  multi-hop wireless ad hoc networking protocols.
  Mobicom 98.
• States that the use of ARP in the protocol stack
  produces non-negligible effects in the simulation
  of a wireless network.
• We found no mention to the use of ARP models in
  other simulation studies save for one other
  paper. Our inquisitiveness lead us to attempt to
  quantify the effect of ARP on the networking
  metrics our simulation estimates.

34
The impact of ARP

• For 16kbps and 56kbps traffic loads, the relative
  error in end-to-end delay observed was as high as
  16.
• Packet delivery ratio showed much less pronounced
  sensitivity the relative error went only as high
  as 1.6.
• The protocol contributes to the simulation with
  small processing load, and also with small
  additional memory requirement.

35
Case study radio interference model

• A common approach to reducing the complexity of
  interference computation is to limit, or
  truncate, the sensing range of a node. This range
  can be defined by a maximum path loss parameter.
  We have investigated two values 106dB and 126dB.
  
• Results were consistent with what has been
  observed in the simulation of wireless cellular
  phone networks (Liljenstam Ayani 1998 Perrone
  Nicol 2000)
• - truncation leads to a substantial reduction in
  number of events to process at the cost of a
  small relative error in network metrics.

For a given node, we can define a receiving range
and a sensing range.
36
A question of time

• How long does one need to run a simulation in
  order to produce good estimates of the network
  metrics?
• We have run simulations of 1000s after 500s of
  warm-up for mobility and traffic generation
  models. This choice, however, has proved to be
  insufficient to avoid problems
• At high-traffic loads, due to contention and
  interference, the estimates obtained for
  end-to-end delay exhibit very large confidence
  intervals indicating that a higher number of
  samples should have been taken.

37
Work for the future

• Sadly, the IEEE 802.11 model needs to be
  rewritten before we can distribute SWAN publicly.
• Need to allow simulations with mixed mobility
  models.
• Need to add support for improved radio
  propagation models.
• A tool for organizing and controlling experiments
  would be very helpful.

38
SWAN Experiment Organizer

• Want to be able to create a template with
  pre-defined blanks for experimental parameters.
• Want to be able to create each particular
  experiment from combinations of experimental
  parameters.
• Want to be able to distribute experiments across
  a network of processors and to control their
  execution.
• Want to be able to have the simulation results be
  organized into a database that can be consulted
  from a web browser.

39
SWAN Experiment Organizer
WIRELESS_NODE ID 1 xpos 0 ypos
0 battery 1000.0 graph
ProtocolSession name "app" use
"app.tstapp-session" bitrate BITRATE
packet_size PACKET on ON off
OFF jitter JITTER ProtocolSession
name "net" use "net.aodv-session"
ProtocolSession name "mac" use
"mac.mac-802-11-session ProtocolSession
name "phy" use "phy.phy-802-11-session"

• PACKET128,512
• BITRATE5,50,300
• JITTER0,10,100
• ON1,5,25,50
• OFF54,50,30,5
• 1TO1ON,OFF

128,5,0,1,54
128,5,0,5,50
128,5,0,25,30
128,5,0,50,5
...
128,50,0,1,54
512,5,0,1,54
40
Closing Words
SWAN is maturing into a useful tool that has been
applied in a number of different research
studies. See http//www.eg.bucknell.edu/swan for
a list of papers. Documentation is available,
though it is at a very rough draft stage. It can
be helpful to get one started. We have made
strong efforts to document individual classes
(Doxygen, DOC). The tool is being prepared for
public release.
41
Acknowledgments

• The following people have participated in the
  SWAN Project
• Jason Liu
• Yougu Yuan
• Noah Miller
• Evan Richardson
• Samuel Nelson
• Eric Graham
• Chris Kenna