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

Esunly Medina фRoc Meseguer фCarlos Molina λDolors Royo ф

Santander (SPAIN) - September 22-24, 2010

ф Dept. Arquitectura de Computadors

Universitat Politècnica de Catalunya Barcelona, Spain

{esunlyma, meseguer, dolors}@ac.upc.edu

λ Dept. Enginyeria Informàtica i MatemàtiquesUniversitat Rovira i Virgili

Tarragona, Spaincarlos.molina@urv.net

• Motivation

• Potentiality

• OLSRp

• Conclusions & Future Work

OLSROutline

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

Motivation

… but when the number of nodes is high …

… can overload the network!!!

OLSROLSR: 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?

OLSRDQ principle

PREDICTING MESSAGES !!!!

– Called OLSRp

– Predicts duplicated topology-update messages

– Reduce messages transmitted through the network

– Saves computational processing and energy

– Independent of the OLSR configuration

– Self-adapts to network changes.

We propose a 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

• 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

OLSRExperimental Setup

OLSR

TC vs HELLO

OLSR: Messages distribution

Ratio of TC messages is significant for low density of nodes

OLSRControl Information Repetition

Number of nodes does not affect repetition

Density of nodes slightly affects repetition

OLSRControl Information Repetition

Repetition is mainly affected by mobility

OLSRControl Information Repetition

OLSRControl Information Repetition

Repetition still being significant for high node speeds

OLSRp

Prevent MPRs from transmitting duplicated TC throughout the network:

OLSROLSRp: Basis

– Last-value predictor placed 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

Upper Levels

Lower Levels

OLSR Input

OLSR Output

Wifi Input Wifi Output

TCWifi TCOLSR if MPR: TCOLSR TCWifi

OLSROLSRp: Layers

Upper Levels

Lower Levels

OLSR Input

OLSR Output

OLSRp Input

OLSRp Output

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

OLSROLSRp: 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 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 (new TC message will be sent)

OLSROLSRp: Example

B

B

E

OLSROLSRp: Example

B

B

E

NODE D TABLE

OLSROLSRp: Example

B

B

E

NODE D TABLE

XXX

X

OLSROLSRp: Example

B

B

E

NODE D TABLE

XXX

X

OLSROLSRp: Example

B

B

E

NODE D TABLE

XXX

X

• Reduction in:– Control traffic

OLSROLSRp: Benefits

– CPU processing – Energy consumption

OLSROLSRp: Some Results

Conclusions & Future Work

OLSRConclusions & 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

Questions?

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

Santander (SPAIN) - September 22-24, 2010