Wireless Networks, vol. 17, no. 5, May 2011, pp. 1387-1400

Woo-Sung Jung, Keun-Woo Lim, Young-Bae Ko and Sang-Joon ParkSpeaker: Wun-Cheng Li

Efficient clustering-based data aggregation techniques for wireless sensor networks

Outline

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• Introduction• Related work

▫Statically clustered networks▫Dynamically clustered networks

• Goal• Proposed scheme

▫Combined clustering based data aggregation▫Adaptive clustering based data aggregation

• Performance evaluation• Conclusion

Introduction

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• In wireless sensor network applications for surveillance and reconnaissance, large amounts of redundant sensing data are frequently generated.

• Reduce the cost and simplify the calculation is one of the key technologies for future application

Introduction• It is important to control these data with efficient data

aggregation techniques to reduce energy consumption in the network.

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• Statically clustered networks▫Proactively divide the network into many clusters

Related work

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Target Sink Node

Data Transmission

Data Aggregation

Cluster Head

Sensor Node

Related work

• Statically clustered networks▫no additional transmission delay▫ low energy consumption

• More than one cluster may sense a target at the same time▫ reducing the data aggregation efficiency

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Related work

• Dynamically clustered networks▫Reactively create a cluster

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Sink Node

Data Transmission

Data Aggregation

Cluster Head

Sensor Node

Target

Related work

• Dynamically clustered networks▫preserving energy of the other sensor nodes▫high data aggregation

• Clusters are made upon sensing of an event▫additional transmission delay

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Goals• By efficient data aggregation techniques can ensuring quick

and high data aggregation rates, while avoiding excessive use of control packets.▫ reducing energy consumption▫ increase network lifetime▫decrease end-to-end delay

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Preliminary• Network initialization phase

▫ initial tree topology

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Sink Node

Sensor Node

Combined clustering based data aggregation

• using the values α and β

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Data Transmission

Data Aggregation

Cluster Head

Sensor Node

Sink Node

Dynamic Cluster Area Static Cluster Area No Aggregation

β α

Target

Adaptive clustering based data aggregation

• Initial Phase (Dynamic)• Cluster Change Triggered by Node Mobility

▫Threshold Value(data traffic)

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Adaptive clustering based data aggregation

• Cluster Method Change (Control Packet Flooding)

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Adaptive clustering based data aggregation

• Static Data Aggregation

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Performance evaluation• QualNet 4.0

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Parameter SettingsRandomly deployed 500 m × 500 mNodes 100Targets 1 to 4Sensing range 30 to 70 mSimulation time 100 sSimulation carried 10 timesTransmission rate 250 kbpsTransmission power 100 mTransmit data /control packets 100/8 bytesEnergy consumption of tx/rx 31.32/35.46 mW/sα and β 2 and 5Packet threshold for aggregation switching 5 packets per secondSwitching frequency 3 sData aggregation time 200 ms

Performance evaluation

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• Performance results in relation to target velocity(0~15 m/s)

Performance evaluation

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• Performance results in relation to target velocity(0~15 m/s)

Performance evaluation

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• Performance results in relation to sensing range▫Average energy consumption

Velocity 10 m/s Velocity 0 m/s

Performance evaluation

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• Performance results in relation to sensing range▫Network life time

Velocity 10 m/s Velocity 0 m/s

Performance evaluation

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• Performance results in relation to sensing range▫Packet transmission success ratio

Velocity 10 m/s Velocity 0 m/s

Performance evaluation

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• Performance results in relation to sensing range▫Average aggregation count

Velocity 10 m/s Velocity 0 m/s

Performance evaluation

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Conclusions• This paper proposes hybrid mechanisms for improving data

aggregation efficiency in target tracking applications of wireless sensor networks.

• By choosing the clustering technique, it was able to achieve low energy consumption, high aggregation ratio and packet transmission success ratio.

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Thank you!

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