The 2012 IEEE International Conference on Systems, Man, and Cybernetics
October 14-17, 2012, COEX, Seoul, Korea
Reducing Energy Consumption in Humancentric Wireless Sensor Networks
Roc Meseguer1, Carlos Molina2, Sergio F. Ochoa3, Rodrigo Santos4
1Universitat
Politècnica de Catalunya, Barcelona, Spain
2Universitat
Rovira i Virgili, Tarragona, Spain
3Universidad
4Universidad
de Chile, Santiago, Chile
Nacional del Sur, Bahia Blanca, Argentina
Outline
OLSR
• Motivation
• Potentiality
• OLSRp
• Conclusions & Future Work
Motivation
Motivation
Human-Centric Wireless Sensor Networks (HWSN)
oppnet that uses mobile devices to build a mesh
Motivation
• Human-centric Sensor Wireless 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 …
… can overload the network!!!
OLSR: ControlOLSR
Traffic and Energy
OLSR is one of the
most intensive
energy-consumers
Traffic and energy do
NOT scale !!!
… can we increase scalability of routing protocols
for Human-centric Wireless Sensor Networks? …
DQ OLSR
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 !!!!
We propose a mechanism for
increasing scalability of HWSN
based on link state proactive routing protocols
– 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.
Potentiality
Experimental
OLSR 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 Prop. Model
• ICMP traffic
• OLSR control messages:
– HELLO=2s
– TC=5s
OLSR: Messages
OLSRdistribution
TC vs HELLO
Ratio of TC messages is significant for low density of nodes
Control Information
OLSR Repetition
Number of nodes does not affect repetition
Control Information
OLSR Repetition
Density of nodes slightly affects repetition
Control Information
OLSR Repetition
Repetition is mainly affected by mobility
Control Information
OLSR Repetition
Repetition still being significant for high node speeds
OLSRp
OLSRp:
Basis
OLSR
Prevent MPRs from transmitting duplicated TC
throughout the network:
– 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
OLSRp:
Layers
OLSR
Upper Levels
Upper Levels
OLSR
OLSR
Input
Output
OLSR
OLSR
Input
Output
OLSRp OLSRp
Lower Levels
Input
Output
Wifi Input
Lower Levels
Wifi Output
Input
TCWifi
TCOLSR
Wifi 
if (TC[n]=TC[n-1]): TCOLSRp  TCOLSR
else: TCWifi TCOLSR
WifiTCOutput
if MPR:
OLSR  TCWifi
if MPR if(TC[n]=TC[n-1]): TCOLSRp
else: TCOLSR  TCWifi
OLSRp:
Basis
OLSR
– 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)
Experimental
OLSR Setup
•
•
•
•
•
•
NS-2
Physical area of 200m X 200m
25 stationary nodes & 275 mobile nodes
Nodes are randomly deployed (11 simulations)
All nodes assume IPhone 4 features
Mobile nodes assume:
• random mobility and
• walking speed (0.7m/s)
• Wifi Channel assumes Friis Propagation loss model
• OLSR control messages: HELLO=2s & TC=5s
• Data traffic assumes UDP packets transmitted every second
OLSRp:
Benefits
OLSR
Reduction in energy consumption
OLSRp:
Benefits
OLSR
Reduction in control traffic & CPU processing
Conclusions & Future Work
ConclusionsOLSR
& 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
The 2012 IEEE International Conference on Systems, Man, and Cybernetics
October 14-17, 2012, COEX, Seoul, Korea
Thanks for Your Attention
Questions?
The 2012 IEEE International Conference on Systems, Man, and Cybernetics
October 14-17, 2012, COEX, Seoul, Korea
Questions?
ANEXOS
OLSRp:
Example
OLSR
B
B
OLSRp:
Example
OLSR
B
B
NODE D TABLE
OLSRp:
Example
OLSR
X
X
B
X
X
B
NODE D TABLE
OLSRp:
Example
OLSR
X
X
B
X
X
B
NODE D TABLE
OLSRp:
Example
OLSR
X
X
B
X
X
B
NODE D TABLE
OLSRp: Other
OLSR Results
OLSRp: Other
OLSR Results
OLSRp: Other
OLSR Results