Cluster-Gossip Based Distributed Kalman Consensus Filter Algorithm with Energy Efficiency

Abstract:

According to these constrains that wireless sensor networks are composed of many wireless nodes with limited power, a new energy efficient cluster-based distributed consensus kalman filtering algorithm is proposed in this paper. In this algorithm, each cluster contains a cluster-head and some member nodes where the cluster-head is used to fuse data which come from member nodes and consensus process between neighbor cluster-head. This clustering method divide wireless sensor networks into two classes of networks: cluster units network and cluster-heads network. In this way, numbers of information transmission among nodes are reduced efficiently and communication distances among nodes are also shortened. As a result, node’s energy in wireless sensor network can be saved greatly. Moreover, Gossip algorithm is introduced to deal with the consensus problem between cluster-heads for improving power consumption and the convergence analysis for the algorithm which is given by applying to graph theory and matrix theory. Finally, a simulation example is given to show the effectively of our method.

• Periodical: Applied Mechanics and Materials (Volume 667)
• Pages: 291-299
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.667.291
• Online since: October 2014
• Authors: Chun Xi Yang, Chao Sun, Sha Fan, Ning Wu
• Keywords: Clustering, Distributed Consensus, Energy-Efficient, Gossip Algorithms, Kalman Filtering, Wireless Sensor Network (WSN)

References

[1] R. Olfati-Saber. Distributed Kalman filter with embedded consensus filters[C]. /Proceedings of the 44th IEEE Conference on Decision and Control. Seville: IEEE Press, 2005: 8179-8184.

DOI: 10.1109/cdc.2005.1583486

[2] C.C. Wang ,G.Q. Qi and Y.Y. Li: Control Theory & Applications. Vol. 29 (2012), p.1645.

[3] R. Olfati-Saber, Distributed Kalman filtering for sensor networks[C]. / Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans: IEEE Press, 2007: 5492-5498.

DOI: 10.1109/cdc.2007.4434303

[4] R. Olfati-Saber, Kalman-consensus filter: Optimality, stability and performance[C]. / Proceedings of IEEE Conference on Decision and Control. Shanghai: IEEE Press, 2009: 7032-7046.

DOI: 10.1109/cdc.2009.5399678

[5] J.Z. Li , J.B. Li and S.F. Shi: Journal of Software Vol. 14 (2003), p.1717.

[6] W. Heinzelman, A. Chandrakasan and H. Balakri-Shnan. Energy-Efficient communication protocol for wireless micro-sensor networks[C]. /Proceedings of the 33rd Annual Hawaii International Conference on System Science. Maui: IEEE Computer Society, 2000. 3005−3014.

DOI: 10.1109/hicss.2000.926982

[7] O. Younis, S. Fahmy: IEEE Transactions on Mobile Computer. Vol. 3 (2004), p.366.

[8] X. Yi , L. Deng and Y. Liu: Control Theory & Application. Vol. 29 (2012), p.145.

[9] Y. Xu , J. Heideman , D. Estrin. Geography-informed energy conservation for Ad Hoc routing[C]. / Proceedings of the 7th Annual International Conference on Mobile Computing and Networking. Rome, Italy: IEEE Computer Society Press, 2001, 70-84.

DOI: 10.1145/381677.381685

[10] W. Heinzelman, A. Chandrakasan and H. Balakri-Shnan: IEEE Transactions on Wireless Communications. Vol. 1 (2002), p.660.

[11] S. Boyd , A. Ghosh, B. Prabhakar and D. Shah: IEEE Transactions on Information Theory. Vol. 52 (2006), p.2508.

[12] J.H. Luo ,W.D. Fang: An Introduction to the Matrix Analysis (The press of South China University of Technology, Guangzhou 2006).

[13] P. Gupta , P.R. Kumar: IEEE Transactions on Information Theory. Vol. 46 (2000), p.388.

[14] L. Ding , Z.H. Guan: Physica A. Vol. 387 (2008), p.3008.

[15] L. Xiao , S. Boyd: Decision and Control. Vol. 53 (2004), p.65.