reducing energy consumption in human- centric wireless sensor networks the 2012 ieee international...
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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, Spain3Universidad de Chile, Santiago, Chile
4Universidad Nacional del Sur, Bahia Blanca, Argentina
• Motivation
• Potentiality
• OLSRp
• Conclusions & Future Work
OLSROutlineOutline
Motivation
MotivationMotivation
Human-Centric Wireless Sensor Networks (HWSN)
oppnet that uses mobile devices to build a mesh
Human-Centric Wireless Sensor Networks (HWSN)
oppnet that uses mobile devices to build a mesh
• 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
MotivationMotivation
… but when the number of nodes is high …… but when the number of nodes is high …
… can overload the network!!!… can overload the network!!!
OLSROLSR: Control Traffic and EnergyOLSR: Control Traffic and Energy
Traffic and energy do NOT scale !!!
Traffic and energy do NOT scale !!!
OLSR is one of the most intensive
energy-consumers
OLSR is one of the most intensive
energy-consumers
… can we increase scalability of routing protocols for Human-centric Wireless Sensor Networks? …
… can we increase scalability of routing protocols for Human-centric Wireless Sensor 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 principleDQ principle
PREDICTING MESSAGES !!!!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 HWSN
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 Prop. Model
• ICMP traffic
• OLSR control messages:– HELLO=2s– TC=5s
OLSRExperimental SetupExperimental Setup
OLSR
TC vs HELLO
OLSR: Messages distributionOLSR: Messages distribution
Ratio of TC messages is significant for low density of nodesRatio of TC messages is significant for low density of nodes
OLSRControl Information RepetitionControl Information Repetition
Number of nodes does not affect repetitionNumber of nodes does not affect repetition
Density of nodes slightly affects repetitionDensity of nodes slightly affects repetition
OLSRControl Information RepetitionControl Information Repetition
Repetition is mainly affected by mobilityRepetition is mainly affected by mobility
OLSRControl Information RepetitionControl Information Repetition
OLSRControl Information RepetitionControl Information Repetition
Repetition still being significant for high node speedsRepetition still being significant for high node speeds
OLSRp
Prevent MPRs from transmitting duplicated TC throughout the network:
Prevent MPRs from transmitting duplicated TC throughout the network:
OLSROLSRp: BasisOLSRp: 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: LayersOLSRp: 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: BasisOLSRp: 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)
• 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
OLSRExperimental SetupExperimental Setup
OLSROLSRp: BenefitsOLSRp: Benefits
Reduction in energy consumption Reduction in energy consumption
OLSROLSRp: BenefitsOLSRp: Benefits
Reduction in control traffic & CPU processingReduction in control traffic & CPU processing
Conclusions & Future Work
OLSRConclusions & Future WorkConclusions & 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?
Thanks for Your Attention
The 2012 IEEE International Conference on Systems, Man, and Cybernetics
October 14-17, 2012, COEX, Seoul, Korea
Questions?
The 2012 IEEE International Conference on Systems, Man, and Cybernetics
October 14-17, 2012, COEX, Seoul, Korea
ANEXOS
OLSROLSRp: ExampleOLSRp: Example
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NODE D TABLENODE D TABLE
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NODE D TABLENODE D TABLE
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OLSROLSRp: ExampleOLSRp: Example
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NODE D TABLENODE D TABLE
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OLSROLSRp: ExampleOLSRp: Example
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NODE D TABLENODE D TABLE
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OLSROLSRp: Other ResultsOLSRp: Other Results
OLSROLSRp: Other ResultsOLSRp: Other Results
OLSROLSRp: Other ResultsOLSRp: Other Results