Clustering in Wireless Multimedia Sensor Networks

Pushpender Kumar, Narottam Chand

Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, India.

DOI: 10.4236/jst.2013.34019                

Abstract

Wireless Multimedia Sensor Networks (WMSNs) are comprised of small embedded audio/video motes capable of extracting the surrounding environmental information, locally processing it and then wirelessly transmitting it to sink/base station. Multimedia data such as image, audio and video is larger in volume than scalar data such as temperature, pressure and humidity. Thus to transmit multimedia information, more energy is required which reduces the lifetime of the network. Limitation of battery energy is a crucial problem in WMSN that needs to be addressed to prolong the lifetime of the network. In this paper we present a clustering approach based on Spectral Graph Partitioning (SGP) for WMSN that increases the lifetime of the network. The efficient strategies for cluster head selection and rotation are also proposed as part of clustering approach. Simulation results show that our strategy is better than existing strategies.

Keywords

Wireless Multimedia Sensor Network; Clustering; Spectral Graph Partitioning; Eigenvector; Eigenvalue

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	F. Akyldiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless Sensor Networks: A Survey,” Computer Networks, Vol. 38, No. 4, 2002, pp. 393-422.
[2]	S. Taruna and M. R. Tiwari, “An Event Driven Energy Efficient Data Reporting System for Wireless Sensor Networks,” Vol. 2, No. 2, 2013, pp. 70-75.
[3]	I. F. Akyildiz, T. Melodia and K. R. Chowdhury, “A Survey on Wireless Multimedia Sensor Networks,” Computer Networks, Vol. 51, No. 4, 2007, pp. 921-960.
[4]	P. Kumar and N. Chand, “Clustering in Wireless Multimedia Sensor Networks Using Spectral Graph Partitioning,” International Journal of Communication, Network and System Sciences, Vol. 6, No. 3, 2013, pp. 128-133.
[5]	M. Demirbas, A. Arora, V. Mittal and V. Kulathumani, “A Fault-Local Self-Stabilizing Clustering Service for Wireless Ad Hoc Networks,” IEEE Transactions on Parallel and Distributed Systems, Vol. 17, No. 9, 2006, pp. 912-922.
[6]	B. Auffarth, “Spectral Graph Clustering,” Course Report. http://wwwlehre.inf.uos.de/~bauffart/spectral.pdf
[7]	B. Elbhiri, S. El Fkihi, R. Saadane and D. Aboutajdine, “Clustering in Wireless Sensor Network Based on Near Optimal Bi-partitions,” 6th EURO-NF Conference on Next Generation Internet (NGI), 2010, pp. 1 -6.
[8]	M. Qin and R. Zimmermann, “VCA: An Energy Efficient Voting Based Clustering Algorithm for Sensor Networks,” Journal of Universal Computer Science, Vol. 13, No. 1, 2007, pp. 87-109.
[9]	M. Chatterjee, S. K. Das and D. Turgut, “WCA: A Weighted Clustering Algorithm for Mobile Ad hoc Networks,” Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks), Vol. 5, No. 2, 2002, pp. 193-204.
[10]	S. Banerjee and S. Khuller, “A Clustering Scheme for Hierarchical Control in Multi-Hop Wireless Networks,” IEEE INFOCOM, 2001, pp. 1028-1037.
[11]	S. Bandyopadhyay and E. Coyle, “An Energy Efficient Hierarchical Clustering Algorithmfor Wireless Sensor Networks,” 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), Vol. 3, 2003, pp. 1713-1723.
[12]	C. Li, M. Ye, G. Chen and J. Wu, “An Energy Efficient Unequal Clustering Mechanism for Wireless Sensor Networks,” 2nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), 2005, pp. 125-132.
[13]	Y. He, Y. Zhang, Y. Ji and S. X. Shen, “A New Energy Efficient Approach by Separating Data Collection and Data Report in Wireless Sensor Networks,” International Conference on Communications and Mobile Computing, 2006, pp. 180-192.
[14]	A. Bertrand and M. Moonen, “Distributed Computation of the Fiedler Vector with Application to Topology Inference in Ad Hoc Networks,” Internal Report KU Leuven ESAT-SCD, 2012.
[15]	Laplacian Matrix Wikipedia. http://en.wikipedia.org/wiki/Laplacian_matrix
[16]	Adjacency Matrix Wikipedia. http://en.wikipedia.org/wiki/Adjacency_matrix
[17]	Degree Matrix Wikipedia. http://en.wikipedia.org/wiki/Degree_matrix
[18]	A. Savvides, C. Han and M. B. Srivastva, “Dynamic Fine-Grained Localization in Ad Hoc Networks of Sensors,” 7th International Conference on Mobile Computing and Networking (MOBICOM), 2001, pp. 166-179.
[19]	D. Lu, N. Nan and X.-X. Huang, “Clustering Based Spectrum Allocation Scheme in Mobile Ad Hoc Networks,” Bulletin of Advanced Technology Research (BATR), Vol. 5, No. 12, 2011, pp. 37-41.
[20]	N. Bulusu, J. Heidemann and D. Estrin, “GPS-Less Low Cost Outdoor Localization for Very Small Devices,” IEEE Personal Communication Magazine, Vol. 7, No. 5, 2000, pp. 28-34.
[21]	A. Savvides, C. Han and M. B. Srivastva, “Dynamic Fine-Grained Localization in Ad Hoc Networks of Sensors,” 7th International Conference on Mobile Computing and Networking (MOBICOM), 2001, pp. 166-179.
[22]	O. Younis and S. Fahmy, “Distributed Clustering in Ad-Hoc Sensor Networks: A Hybrid, Energy Efficient Approach,” IEEE Transactions on Mobile Computing, Vol. 3, No. 4, 2004, pp. 366-379.