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, [email protected]
• 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