Robust MANET Design

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Robust MANET Design

• John P. Mullen, Ph.D.
• Timothy I. Matis, Ph.D.
• Smriti Rangan
• Karl Adams
• Center for Stochastic Modeling
• New Mexico State University
• May 16, 2004

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What Are MANETS ?

• A MANET is a mobile ad-hoc wireless
  communication network that is capable of
  autonomous operation
• Each node is capable of transmitting, receiving,
  and routing packets of information.
• The network has no fixed backbone (such as with
  the Internet and cellular phones)
• The nodes are able to enter, leave, and move
  around the network independently and randomly

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Mobile Ad Hoc Path Search
Y
X
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Same MANET After a While
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Types of Packets

• Control Packets
• RREQ s and RREPs Used to establish
  communication links between the source and
  destination nodes. There are numerous protocols
  that have been proposed for their optimal use
  in finding reliable links at minimal bandwidth
• ACKs Used to ascertain the quality of a link
  and ensure successful communication. The
  destination node sends an acknowledgement (ack)
  packet back to the source after each successful
  data packet transmission.
• Data Packets
• Contain the actual information that is to be
  communicated broken up into packets of uniform
  size
• Data packets are much larger than control packets

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Protocol Taxonomy
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MANET Models

• Current MANET Models
• Received power is a deterministic function of
  distance
• Node communication (preceived ? pmin) is flawless
  within a nominal range, r0, and is not possible
  (preceived ? pmin) beyond this range
• In actuality, the received power process is
  highly stochastic due primarily to shadowing and
  fading

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Current vs. Observed
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Evaluating Protocols

• The deterministic power assumption is the default
  of most simulation software (OpNet, NS2, NAB)
  used to evaluate protocol performance
• The stochastic problem is typically viewed as a
  minor (and unimportant) nuisance by the CS and EE
  communities that design these protocols

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Rayleigh Fading

• The instantaneous received voltage in an
  inefficient, low power, and complex RF
  environment often follows a Rayleigh distribution
• As a result, it follows that received power is
  exponentially distributed
• Further, we assume power exponentially decays
  with distance

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PDF of Received Power
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Initial Test Scenario
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Rec Power Current Model
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Current vs Proposed Model
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Real Vs. Memorex
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Impact on Link Throughput
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Findings

• Not all packets within nominal range are
  transmitted successfully
• Not all packets outside the range are unsuccessful

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Scenario Two DSR Protocol
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RF Propagation Distances
Relay
Source
Dest.
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Throughput
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End-to-End Delay
Delay 0.004 sec In no-fading model
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Route Discovery Time
One Route discovery In no-fading model
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Transmit Buffer Size
Buffer size is 2.0 In no-fading model
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Hops per Route
1.5 hops average A-B 1 hop A-C 2 hops
In no-fading model
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The Basic Problem
Relay
Source
Dest.
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Ping - Pong
A
B
C
A
B
C
1 - 0.46
0.4
1-hop
2-hop
0.2
0.6
p2 2p1
0.8
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Throughput vs. Tries
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Delay vs. Tries
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Buffer Size vs. Tries
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Findings

• Only through accurate stochastic simulations can
• The true performance of existing protocols be
  evaluated
• The parameters of these protocols be optimized
  for robust performance
• New robust protocols be developed
• Parameters not significant in deterministic
  models (such as packet retry) are important in
  stochastic models

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Robust MANET Design

• RSM may be used to optimize the performance of
  established protocols for the controllable
  parameters (F, number of TX tries, etc.) over the
  uncontrollable parameters (c, TX rate, etc.)
• As an example, consider optimizing the number of
  TX tries (1,2,3,4) over 2 levels of TX rate
  (71.5,143 in packets/sec) using throughput as a
  measure of performance

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Throughput (packets/sec)
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Throughput (High/Low Data Rates)
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Relative Throughput
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Relative Throughput(High/Low)
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Mean Delay
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Mean Delay(High/Low)
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Mean Transmit Buffer Size
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Mean Total Bits Per Second
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Mean Routing Bits per Second
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Mean Non-Routing Bits
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Questions ?

• John Mullen
• jomullen_at_nmsu.edu
• Tim Matis
• tmatis_at_nmsu.edu
• Center for Stochastic Modelling
• http//engr.nmsu.edu/csm