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

Motivation Potentiality OLSRp Conclusions & Future Work OLSR Outline

Motivation

OLSR Motivation Ad-hoc networks: – Control messages consume network resources for maintaining network topology (management information) Proactive link state routing protocols: – Nodes periodically broadcast routing information (neighbors) – Each node has a topology map

… but when the number of nodes is high … OLSR Motivation Ad-hoc networks: – Control messages consume network resources for maintaining network topology (management information) Proactive link state routing protocols: – Nodes periodically broadcast routing information (neighbors) – Each node has a topology map

… can overload the network!!!

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

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

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 !!!! OLSR DQ principle

When applied to OLSR protocol: – Called OLSRp (OLSR predictor) – It can predict duplicated topology-update messages (TC). – Is independent of the OLSR configuration parameters and it can dynamically self-adapt to network changes. – Contributes to reduce the number of messages transmitted through the network and to save computational processing and energy consumption. We propose a novel mechanism for increasing scalability of ad-hoc networks based on link state proactive routing protocols.

Potentiality

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

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

OLSR Control Information Repetition Number of nodes does not affect repetition

Density of nodes slightly affects repetition OLSR Control Information Repetition

Repetition is mainly affected by mobility OLSR Control Information Repetition

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

OLSRp

Prevent the MPRs from transmitting duplicated TC messages throughout the network: – Last-value predictor placed in every node of the network – MPRs execute a prediction 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 OLSR OLSRp: Basis

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

– Each node keeps a table whose dimensions depends on the number of nodes – Each entry records info about a specific node: The The list of the MPRs (O.A.) that announce the node in their TCs and 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, the new TC message will be sent OLSR OLSRp: Basis

OLSR OLSRp: Example

Reduction in: – Control traffic – CPU processing – Energy consumption OLSR OLSRp: Benefits

OLSR OLSRp: Some Results

Conclusions & Future Work

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

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