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Robust MANET Design

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. What Are MANETS ?. A MANET is a mobile ad-hoc wireless communication network that is capable of autonomous operation

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Robust MANET Design

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  1. 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

  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 (such as with the Internet and cellular phones) • 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. Types of Packets • Control Packets – • RREQ’ s and RREP’s – 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 • ACK’s – 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

  6. Single channel protocols uniform Destination based topology-based Non-uniform reactive proactive AODV TORA ABR proactive DSDV WRP reactive GSR DSR partitioning CEDAR CBRP Neighbor selection ZRP OLSR Protocol Taxonomy

  7. 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

  8. Field Measurements: Current Assumption: Rec. Power is a deterministic function of distance p(r) From Neskovic 2002 – Fig. 2 Current vs. Observed

  9. 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

  10. 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

  11. PDF of Received Power

  12. Initial Test Scenario

  13. Rec Power –Current Model

  14. Current vs Proposed Model

  15. Real Vs. Memorex

  16. Impact on Link Throughput

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

  18. Relay Source Dest. Scenario Two – DSR Protocol

  19. RF Propagation Distances Relay Source Dest.

  20. Throughput

  21. End-to-End Delay Delay = 0.004 sec In no-fading model

  22. Route Discovery Time One Route discovery In no-fading model

  23. Transmit Buffer Size Buffer size is 2.0 In no-fading model

  24. Hops per Route 1.5 hops average A-B: 1 hop A-C: 2 hops In no-fading model

  25. The Basic Problem Relay Source Dest.

  26. 0.005 p2 = 50p1 0.75 0.995 0.25 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

  27. Throughput vs. Tries

  28. Delay vs. Tries

  29. Buffer Size vs. Tries

  30. 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

  31. 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

  32. Throughput (packets/sec)

  33. Throughput (High/Low Data Rates)

  34. Relative Throughput

  35. Relative Throughput(High/Low)

  36. Mean Delay

  37. Mean Delay(High/Low)

  38. Mean Transmit Buffer Size

  39. Mean Total Bits Per Second

  40. Mean Routing Bits per Second

  41. Mean Non-Routing Bits

  42. Questions ? John Mullen jomullen@nmsu.edu Tim Matis tmatis@nmsu.edu Center for Stochastic Modelling http://engr.nmsu.edu/~csm

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