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Stochastic Characterization of Mobile Ad-hoc Networks

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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Stochastic Characterization of Mobile Ad-hoc Networks

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  1. 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

  2. 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

  3. G D H I A B E F C Mobile Ad Hoc Path Search Y X

  4. 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

  5. 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

  6. 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

  7. 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.

  8. Main causes of signal variation T Shadowing R Multipath

  9. Measured Received Signal Strength(from Neskovic 2000)

  10. 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)

  11. Simulated vs. Real Power Actual Measurements Simulated Values

  12. 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.

  13. Impact of Multipath Fading on MANETs • How does it affect MANETs? • Unnecessary route searches • Selection of false routes

  14. Stub Cellular Nominal Range (r0) Fading margin Impact of Multipath Fading On MANETs False Routes OK Dropped Packets OK

  15. Impact of Multiple Retries on MANETs

  16. 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.

  17. 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

  18. Scenario 2: Routing test(from Chin et. al., 2002) r0 = 39m 10 pps 0.5 m/s

  19. Sc 2: Fading vs. Nonfading: AODV Notes: Default values for AODV SRL = 7

  20. Sc 2: AODV vs. DSR Notes: Default protocol values SRL = 7 Nonfading model shows no difference

  21. Scenario 3: Larger Scale Test • Features: • More nodes (24) • Random r-t pairs • Interference • Higher loads

  22. Mean Throughput: AODV vs. DSR • Notes: • Default protocol values • SRL = 7

  23. Mean Delay: AODV vs. DSR • Notes: • Default protocol values • SRL = 7

  24. A Using GPS data 2 Use GPS to block unreliable routes 1 B r0 3

  25. Impact of GPS Without GPS With GPS

  26. Mean Throughput: Impact of SRL on DSR • Notes: • Default protocol values

  27. Mean Delay: Impact of SRL on DSR • Notes: • Default protocol values

  28. Execution Time in Scenario 3 (Virtually no differences in Scenarios 1 & 2)

  29. 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)

  30. 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

  31. 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.

  32. 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)

  33. Final Questions?

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