Efficient clustering-based data aggregation techniques for wireless sensor networks

1. Efficient clustering-based data aggregation techniques for wireless sensor networks Woo-Sung Jung, Keun-Woo Lim, Young-BaeKoand Sang-Joon Park Speaker: Wun-Cheng Li • Wireless Networks, vol. 17, no. 5, May 2011, pp. 1387-1400

2. Outline • 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

3. Introduction • In wireless sensor network applications for surveillance and reconnaissance, large amounts of redundantsensing data are frequently generated. • Reduce the cost and simplify the calculation is one of the key technologies for future application

4. It is important to control these data with efficient data aggregation techniques to reduce energy consumption in the network. Introduction

5. Statically clustered networks • Proactively divide the network into many clusters Related work Sink Node Target Data Transmission Data Aggregation Cluster Head Sensor Node

6. 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 Related work

7. Dynamically clustered networks • Reactively create a cluster Related work Sink Node Target Data Transmission Data Aggregation Cluster Head Sensor Node

8. Dynamically clustered networks • preserving energy of the other sensor nodes • highdata aggregation • Clusters aremade upon sensing of an event • additional transmission delay Related work

9. 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 Goals

10. Network initialization phase • initial tree topology Preliminary Sink Node Sensor Node

11. using the values αand β Combined clustering based data aggregation Dynamic Cluster Area Static Cluster Area No Aggregation α β Sink Node Target Data Transmission Data Aggregation Cluster Head Sensor Node

12. Initial Phase (Dynamic) • Cluster Change Triggered by Node Mobility • Threshold Value(data traffic) Adaptive clustering based data aggregation

13. Cluster Method Change (Control Packet Flooding) Adaptive clustering based data aggregation

14. Static Data Aggregation Adaptive clustering based data aggregation

15. QualNet4.0 Performance evaluation

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

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

18. Performance evaluation • Performance results in relation to sensing range • Average energy consumption Velocity0 m/s Velocity10 m/s

19. Performance evaluation • Performance results in relation to sensing range • Network life time Velocity0 m/s Velocity10 m/s

20. Performance evaluation • Performance results in relation to sensing range • Packettransmission success ratio Velocity0 m/s Velocity10 m/s

21. Performance evaluation • Performance results in relation to sensing range • Average aggregation count Velocity0 m/s Velocity10 m/s

22. Performance evaluation

23. 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. Conclusions

24. Thank you!