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OLSRp: Predicting Control Information to Achieve Scalability in OLSR Ad Hoc Networks

Santander (SPAIN) - September 22-24, 2010. OLSRp: Predicting Control Information to Achieve Scalability in OLSR Ad Hoc Networks. Esunly Medina ф Roc Meseguer ф Carlos Molina λ Dolors Royo ф. ф Dept. Arquitectura de Computadors Universitat Politècnica de Catalunya Barcelona, Spain

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OLSRp: Predicting Control Information to Achieve Scalability in OLSR Ad Hoc Networks

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  1. Santander (SPAIN) - September 22-24, 2010 OLSRp: Predicting Control Information to Achieve Scalability in OLSR Ad Hoc Networks Esunly Medina ф Roc Meseguerф Carlos Molina λ DolorsRoyoф ф Dept. Arquitectura de Computadors Universitat Politècnica de Catalunya Barcelona, Spain {esunlyma, meseguer, dolors}@ac.upc.edu λ Dept. Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili Tarragona, Spaincarlos.molina@urv.net

  2. OLSR Outline • Motivation • Potentiality • OLSRp • Conclusions & Future Work

  3. Motivation

  4. Motivation • Ad-hoc networks: • Need for maintaining network topology • Control messages consume network resources • Proactive link state routing protocols: • Each node has a topology map • Periodically broadcast routing information to neighbors … but when the number of nodes is high …

  5. … can overload the network!!!

  6. OLSR OLSR: Control Traffic and Energy OLSR is one of the most intensive energy-consumers Traffic and energy do NOT scale !!!

  7. … can we increase scalability of routing protocols for ad-hoc networks? …

  8. OLSR DQ principle • Data per query × Queries per second →constant • For routing protocols: • D = Size of packets • Q = Number of packets per second sent to the network • We focus on Q: • Reducing transmitted packets • Without adding complexity to network management • HOW? PREDICTING MESSAGES !!!!

  9. We propose a mechanism for increasing scalability of ad-hoc networks based on link state proactiverouting protocols • Called OLSRp • Predicts duplicated topology-update messages • Reduce messages transmitted through the network • Saves computationalprocessingand energy • Independent of the OLSR configuration • Self-adapts to network changes.

  10. Potentiality

  11. OLSR Experimental Setup • NS-2 & NS-3 • Grid topology, D = 100, 200, … 500 m • 802.11b Wi-Fi cards, Tx rate 1Mbps • Node mobility: • Static, 0.1, 1, 5, 10 m/s • Friis Propagation Model • ICMP traffic • OLSR control messages: • HELLO=2s • TC=5s

  12. OLSR: Messages distribution OLSR TC vs HELLO Ratio of TC messages is significant for low density of nodes

  13. OLSR Control Information Repetition Number of nodes does not affect repetition

  14. OLSR Control Information Repetition Density of nodes slightly affects repetition

  15. OLSR Control Information Repetition Repetition is mainly affected by mobility

  16. OLSR Control Information Repetition Repetition still being significant for high node speeds

  17. OLSRp

  18. OLSR OLSRp: Basis Prevent MPRs from transmitting duplicated TC throughout the network: • Last-value predictorplaced in every node of the network • MPRs predicts when they have a new TC to transmit • The other network nodes predict and reuse the same TC • 100% accuracy: • If predicted TC ≠ new TC  MPR sends the new TC • HELLO messages for validation • The topology have changed and the new TC must be sent • The MPR is inactive and we must deactivate the predictor

  19. Upper Levels OLSR OLSRp: Layers OLSR Input OLSR Output Upper Levels OLSR Input OLSR Output Lower Levels OLSRp Input OLSRp Output Wifi Input Wifi Output Lower Levels TCWifiTCOLSR if MPR: TCOLSRTCWifi Wifi Input Wifi Output if (TC[n]=TC[n-1]): TCOLSRp TCOLSR else: TCWifi TCOLSR if MPR if(TC[n]=TC[n-1]): TCOLSRp else: TCOLSR TCWifi

  20. OLSR OLSRp: Basis • Each node keeps a table whose dimensions depends on the number of nodes • Each entry records info about a specific node: • The node’s @IP • The list of @IP of the MPRs (O.A.) that announce the node in their TCsand the current state of the node (A or I). (HELLO messages received). • A predictor state indicator for MPR nodes (On or Off): • On when at least one of the TC that contains information about the MPR is active • Off when the node is inactive in all the announcing TC messages (new TC message will be sent)

  21. E OLSR OLSRp: Example B B

  22. E OLSR OLSRp: Example B B NODE D TABLE

  23. E X X X OLSR OLSRp: Example X B B NODE D TABLE

  24. E X X X OLSR OLSRp: Example X B B NODE D TABLE

  25. E X X X OLSR OLSRp: Example X B B NODE D TABLE

  26. Energy consumption • CPU processing OLSR OLSRp: Benefits • Reduction in: • Control traffic

  27. OLSR OLSRp: Some Results

  28. Conclusions & Future Work

  29. OLSR Conclusions & Future Work • Conclusions: • OLSRp has similar performance than standard OLSR • Can dynamically self-adapt to topology changes • Reduces network congestion • Saves computer processing and energy consumption • Future Work: • Further evaluation of OLSRp performance • Assessment in real-world testbeds • Application in other routing protocols

  30. Santander (SPAIN) - September 22-24, 2010 OLSRp: Predicting Control Information to Achieve Scalability in OLSR Ad Hoc Networks Questions?

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