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INFORMS 2004. Stochastic Characterization of Mobile Ad-hoc Networks. John P. Mullen and Timothy I. Matis Center for Stochastic Modeling Department of Industrial Engineering New Mexico State University. What Are MANETS ?.
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INFORMS 2004 Stochastic Characterization of Mobile Ad-hoc Networks John P. Mullen and Timothy I. Matis Center for Stochastic Modeling Department of Industrial Engineering New Mexico State University
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 • The nodes are able to enter, leave, and move around the network independently and randomly
G D H I A B E F C Mobile Ad Hoc Path Search Y X
G G D Y D A H H X I A X Y B F B E E C F I C Same MANET After a While
Nutshell • MANET field performance differs greatly from simulation’s • Field & testbed performance is much poorer • Developing MANET protocols in the field is very difficult • Improving simulation fidelity increases the value of simulation in design. • Higher fidelity earlier in the design process leads to better designs • Research focus: • Significantly improve the fidelity of MANET simulations • Without significantly increasing • Simulation run time or • Modeling effort. • Research results • Up to an order of magnitude improvement in fidelity • Runtime increases are often insignificant, but generally less than 100% • Very little added modeling effort
Overview • Multipath Fading and its impact on mobile ad hoc nets • The Stochastic Model • Objectives • Implementation • Validation • Demonstrations of the Model • Small Models • Impact of Short Retry Limit (SRL) • Comparing AODV and DSR • Large Models • AODV vs. DSR • AODV vs. DSR using GPS data • Impact of SRL on DSR • Summary, Conclusions and Further Work
Shadowing and Fading • Shadowing • Is caused by objects absorbing part of the signal • Can be estimated by looking at the Line of Sight (LOS) path • Causes a random reduction in signal strength. • Fading • Is the result of the algebraic sum of signals from many paths • Because movement of any object in the vicinity can change the sum • Multipath fading is extremely difficult to model and predict • Would be very time consuming to simulate exactly • And would have little predictive value. • This phenomena causes: • Very rapid large-scale fluctuations in signal strength • Can cause the signal to be significantly lesser or greater than expected.
Main causes of signal variation T Shadowing R Multipath
Stochastic Variation Model • The Model • Given mp(d), the expectation of power at distance d • Rayleigh fading model of the instantaneous power, P(d) • Pr {P(d) ≤ p} = 1 – exp{-[p/mp(d)]} • Inverse transform of the Rayleigh fading model • P(d) = -mp(d)ln(1-r)
Simulated vs. Real Power Actual Measurements Simulated Values
Validation • Simulated reported field tests and compared results • K.-W. Chin, J. Judge, A. Williams, and R. Kermode, "Implementation experience with MANET routing protocols," ACM SIGCOMM Computer Communications Review, vol. 32, pp. 49 - 59, 2002. • I. D. Chakeres and E. M. Belding-Royer, "The Utility of Hello messages for determining link connectivity," Wireless Personal Multimedia Communications, vol. 2, pp. 504 - 508, 2003. • D. S. J. D. Couto, D. Aguayo, J. Bicket, and R. Morris, "A High Throughput Path Metric for MultiHop Wireless Routing," presented at MobiCom '03, San Diego, California, USA, 2003. • S. Desilva and S. Das, "Experimental evaluation of a wireless ad hoc network," 2000. • Simulations with • Standard non-fading model were exceedingly optimistic • Proposed fading model were very much more realistic.
Impact of Multipath Fading on MANETs • How does it affect MANETs? • Unnecessary route searches • Selection of false routes
Stub Cellular Nominal Range (r0) Fading margin Impact of Multipath Fading On MANETs False Routes OK Dropped Packets OK
The MANET fading Trade-off Increase Risk of Selecting Bad Routes Improve Reliability On Good Routes Protocol MANET: Nominal range is a matter of balance. Most Wireless: Nominal range is a matter of design.
Demonstrations • Small Models (validations of field tests) • Scenario 1 – Performance vs. distance. • Used for the two cases above • Scenario 2 – Routing Test • Focus mainly on fading effects • Models: • Fading vs. nonfading simulations of AODV • DSR vs. AODV with fading model • Large models (exploration) • Scenario 3 – 24 nodes. • Also consider other effects, such as interference • Models: Fading and non-fading versions of • AODV vs. DSR • AODV vs. DSR using GPS data • Impact of SRL on DSR
Scenario 2: Routing test(from Chin et. al., 2002) r0 = 39m 10 pps 0.5 m/s
Sc 2: Fading vs. Nonfading: AODV Notes: Default values for AODV SRL = 7
Sc 2: AODV vs. DSR Notes: Default protocol values SRL = 7 Nonfading model shows no difference
Scenario 3: Larger Scale Test • Features: • More nodes (24) • Random r-t pairs • Interference • Higher loads
Mean Throughput: AODV vs. DSR • Notes: • Default protocol values • SRL = 7
Mean Delay: AODV vs. DSR • Notes: • Default protocol values • SRL = 7
A Using GPS data 2 Use GPS to block unreliable routes 1 B r0 3
Impact of GPS Without GPS With GPS
Mean Throughput: Impact of SRL on DSR • Notes: • Default protocol values
Mean Delay: Impact of SRL on DSR • Notes: • Default protocol values
Execution Time in Scenario 3 (Virtually no differences in Scenarios 1 & 2)
Summary • Non fading model • Overestimates field performance • Is very insensitive to all the contrasts shown here and more. • Fading model • Provides more realistic estimates • Better predicts impacts of protocol and parameter changes • Shows promise of new techniques. • Requires little or no additional modeling • Has little impact on execution time • (Alternative is a testbed or a field trial)
Conclusions • Multipath Fading • has a great impact on mobile ad hoc nets • Including its effects in simulation • greatly improves fidelity • Stochastic Modeling of Multipath Fading • Is a practical way to include the impact of fading • Minor modifications to code (in OPNET, at least) • Without great increases in • Modeling effort or • Execution time
Future Work • More Fading Models • Rayleigh • Ricean • Nakagami • Other significant RF effects • e.g. exponential decay factor • Better user interface • Allow selection of models & parameters without need to recompile. • Validation • Replicating published studies • Set up own testbed and field trials • Better modeling of fading impacts • Hello vs. control vs. data packet results • Other significant measurable elements.
Acknowledgements • OPNET Technologies • Software license research grant • Technical assistance • Center for Stochastic Modeling • Technical resources • Klipsch School of Electrical and Computer Engineering • Dr. Steve Horan • Dr. Hong Huang (also CSM member)