Reducing Energy Consumption in Human-centric Wireless Sensor Networks

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


Repetition is mainly affected by mobilityRepetition is mainly affected by mobility



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



Questions?



ANEXOS

OLSROLSRp: ExampleOLSRp: Example

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NODE D TABLENODE D TABLE


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OLSROLSRp: Other ResultsOLSRp: Other Results



Documents

Reducing Energy Consumption in Human-centric Wireless Sensor Networks