Reducing Energy Consumption in Human-centric Wireless Sensor Networks

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Reducing Energy Consumption in Human-centric
Wireless Sensor Networks
The 2012 IEEE International Conference on
Systems, Man, and Cybernetics October 14-17,
2012, COEX, Seoul, Korea
Roc Meseguer1, Carlos Molina2, Sergio F. Ochoa3,
Rodrigo Santos4   1Universitat Politècnica de
Catalunya, Barcelona, Spain 2Universitat Rovira i
Virgili, Tarragona, Spain 3Universidad de Chile,
Santiago, Chile 4Universidad Nacional del Sur,
Bahia Blanca, Argentina
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OLSR
Outline

• Motivation
• Potentiality
• OLSRp
• Conclusions Future Work

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Motivation
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Motivation

• Human-Centric Wireless Sensor Networks (HWSN)
• oppnet that uses mobile devices to build a mesh

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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
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can overload the network!!!
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OLSR
OLSR Control Traffic and Energy

• OLSR is one of the
• most intensive
• energy-consumers

Traffic and energy do NOT scale !!!
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can we increase scalability of routing
protocols for Human-centric Wireless Sensor
Networks?
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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 !!!!
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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.

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Potentiality
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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 Prop. Model
• ICMP traffic
• OLSR control messages

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OLSR
OLSR Messages distribution

• TC vs HELLO

Ratio of TC messages is significant for low
density of nodes
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OLSR
Control Information Repetition
Number of nodes does not affect repetition
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OLSR
Control Information Repetition
Density of nodes slightly affects repetition
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OLSR
Control Information Repetition
Repetition is mainly affected by mobility
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OLSR
Control Information Repetition
Repetition still being significant for high node
speeds
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OLSRp
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OLSR
OLSRp Basis

• 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

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OLSR
OLSRp Layers
TCWifi ? TCOLSR
if MPR TCOLSR ? TCWifi
if (TCnTCn-1) TCOLSRp ? TCOLSR else TCWifi
?TCOLSR
if MPR? if(TCnTCn-1) TCOLSRp else TCOLSR ?
TCWifi
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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 nodes _at_IP
• The list of _at_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)

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OLSR
Experimental 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 HELLO2s TC5s
• Data traffic assumes UDP packets transmitted
  every second

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OLSR
OLSRp Benefits

• Reduction in energy consumption

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OLSR
OLSRp Benefits

• Reduction in control traffic CPU processing

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Conclusions Future Work
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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

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Questions?
The 2012 IEEE International Conference on
Systems, Man, and Cybernetics October 14-17,
2012, COEX, Seoul, Korea
Thanks for Your Attention
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The 2012 IEEE International Conference on
Systems, Man, and Cybernetics October 14-17,
2012, COEX, Seoul, Korea
Questions?
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ANEXOS
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OLSR
OLSRp Example
B
B
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OLSR
OLSRp Example
B
B
NODE D TABLE
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OLSR
OLSRp Example
X
B
B
NODE D TABLE
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OLSR
OLSRp Example
X
B
B
NODE D TABLE
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OLSR
OLSRp Example
X
B
B
NODE D TABLE
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OLSR
OLSRp Other Results
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OLSR
OLSRp Other Results
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OLSR
OLSRp Other Results