The Community for Technology Leaders
RSS Icon
Issue No.03 - March (2014 vol.25)
pp: 806-815
Ruitao Xie , Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China
Xiaohua Jia , Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China
Compressive sensing (CS) can reduce the number of data transmissions and balance the traffic load throughout networks. However, the total number of transmissions for data collection by using pure CS is still large. The hybrid method of using CS was proposed to reduce the number of transmissions in sensor networks. However, the previous works use the CS method on routing trees. In this paper, we propose a clustering method that uses hybrid CS for sensor networks. The sensor nodes are organized into clusters. Within a cluster, nodes transmit data to cluster head (CH) without using CS. CHs use CS to transmit data to sink. We first propose an analytical model that studies the relationship between the size of clusters and number of transmissions in the hybrid CS method, aiming at finding the optimal size of clusters that can lead to minimum number of transmissions. Then, we propose a centralized clustering algorithm based on the results obtained from the analytical model. Finally, we present a distributed implementation of the clustering method. Extensive simulations confirm that our method can reduce the number of transmissions significantly.
Clustering algorithms, Clustering methods, Data collection, Data communication, Routing, Vectors, Compressed sensing,clustering, Wireless sensor networks, compressive sensing, data collection
Ruitao Xie, Xiaohua Jia, "Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing", IEEE Transactions on Parallel & Distributed Systems, vol.25, no. 3, pp. 806-815, March 2014, doi:10.1109/TPDS.2013.90
[1] R. Szewczyk, A. Mainwaring, J. Polastre, J. Anderson, and D. Culler, "An Analysis of a Large Scale Habitat Monitoring Application," Proc. ACM Second Int'l Conf. Embedded Networked Sensor Systems (SenSys '04), pp. 214-226, Nov. 2004.
[2] E. Candes and M. Wakin, "An Introduction to Compressive Sampling," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21-30, Mar. 2008.
[3] R. Baraniuk, "Compressive Sensing [Lecture Notes]," IEEE Signal Processing Magazine, vol. 24, no. 4, pp. 118-121, July 2007.
[4] D. Donoho, "Compressed Sensing," IEEE Trans. Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
[5] J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak, "Compressed Sensing for Networked Data," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 92-101, Mar. 2008.
[6] C. Luo, F. Wu, J. Sun, and C.W. Chen, "Compressive Data Gathering for Large-Scale Wireless Sensor Networks," Proc. ACM MobiCom, pp. 145-156, Sept. 2009.
[7] S. Lee, S. Pattem, M. Sathiamoorthy, B. Krishnamachari, and A. Ortega, "Spatially-Localized Compressed Sensing and Routing in Multi-Hop Sensor Networks," Proc. Third Int'l Conf. GeoSensor Networks (GSN '09), pp. 11-20, 2009.
[8] C. Luo, F. Wu, J. Sun, and C.W. Chen, "Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering," IEEE Trans. Wireless Comm., vol. 9, no. 12, pp. 3728-3738, Dec. 2010.
[9] J. Luo, L. Xiang, and C. Rosenberg, "Does Compressed Sensing Improve the Throughput of Wireless Sensor Networks?" Proc. IEEE Int'l Conf. Comm (ICC), pp. 1-6, May 2010.
[10] L. Xiang, J. Luo, and A. Vasilakos, "Compressed Data Aggregation for Energy Efficient Wireless Sensor Networks," Proc. IEEE Sensor, Mesh, and Ad Hoc Comm. and Networks (SECON '11), pp. 46-54, June 2011.
[11] F. Fazel, M. Fazel, and M. Stojanovic, "Random Access Compressed Sensing for Energy-Efficient Underwater Sensor Networks," IEEE J. Selected Areas Comm., vol. 29, no. 8, pp. 1660-1670, Sept. 2011.
[12] J. Wang, S. Tang, B. Yin, and X.-Y. Li, "Data Gathering in Wireless Sensor Networks through Intelligent Compressive Sensing," Proc. IEEE INFOCOM, pp. 603-611, Mar. 2012.
[13] B. Zhang, X. Cheng, N. Zhang, Y. Cui, Y. Li, and Q. Liang, "Sparse Target Counting and Localization in Sensor Networks Based on Compressive Sensing," Proc. IEEE INFOCOM, pp. 2255-2263, Apr. 2011.
[14] E. Candes, J. Romberg, and T. Tao, "Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information," IEEE Trans. Information Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
[15] E. Candes and T. Tao, "Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies?" IEEE Trans. Information Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
[16] J. Tropp and A. Gilbert, "Signal Recovery from Random Measurements via Orthogonal Matching Pursuit," IEEE Trans. Information Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
[17] M. Youssef, A. Youssef, and M. Younis, "Overlapping Multihop Clustering for Wireless Sensor Networks," IEEE Trans. Parallel and Distributed Systems, vol. 20, no. 12, pp. 1844-1856, Dec. 2009.
[18] S. Soro and W.B. Heinzelman, "Cluster Head Election Techniques for Coverage Preservation in Wireless Sensor Networks," Ad Hoc Networks, vol. 7, no. 5, pp. 955-972, 2009.
[19] O. Younis, M. Krunz, and S. Ramasubramanian, "Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges," IEEE Network, vol. 20, no. 3, pp. 20-25, May/June 2006.
[20] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "An Application-Specific Protocol Architecture for Wireless Microsensor Networks," IEEE Trans. Wireless Comm., vol. 1, no. 4, pp. 660-670, Oct. 2002.
[21] O. Younis and S. Fahmy, "HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks," IEEE Trans. Mobile Computing, vol. 3, no. 4, pp. 366-379, Oct.-Dec. 2004.
[22] S. Bandyopadhyay and E. Coyle, "An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks," Proc. IEEE INFOCOM, vol. 3, pp. 1713-1723, Mar. 2003.
[23] D. Wang, L. Lin, and L. Xu, "A Study of Subdividing Hexagon-Clustered WSN for Power Saving: Analysis and Simulation," Ad Hoc Networks, vol. 9, no. 7, pp. 1302-1311, Sept. 2011.
[24] S. Chen, Y. Wang, X.-Y. Li, and X. Shi, "Data Collection Capacity of Random-Deployed Wireless Sensor Networks," Proc. IEEE GLOBECOM, pp. 1-6, Dec. 2009.
[25] K. Yedavalli and B. Krishnamachari, "Sequence-Based Localization in Wireless Sensor Networks," IEEE Trans. Mobile Computing, vol. 7, no. 1, pp. 81-94, Jan. 2008.
[26] A. Nasipuri and K. Li, "A Directionality Based Location Discovery Scheme for Wireless Sensor Networks," Proc. First ACM Int'l Workshop Wireless Sensor Networks and Applications (WSNA '02), pp. 105-111, 2002.
[27] S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O'Callaghan, "Clustering Data Streams: Theory and Practice," IEEE Trans. Knowledge and Data Eng., vol. 15, no. 3, pp. 515-528, May/June 2003.
[28] K. Jain and V.V. Vazirani, "Approximation Algorithms for Metric Facility Location and k-Median Problems Using the Primal-Dual Schema and Lagrangian Relaxation," J. ACM, vol. 48, no. 2, pp. 274-296, Mar. 2001.
[29] V. Arya, N. Garg, R. Khandekar, A. Meyerson, K. Munagala, and V. Pandit, "Local Search Heuristic for k-Median and Facility Location Problems," Proc. Thirty-Third Ann. ACM Symp. Theory of Computing (STOC '01), pp. 21-29, 2001.
[30] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless Sensor Networks: A Survey," Computer Networks, vol. 38, no. 4, pp. 393-422, 2002.
[31] D.B. Johnson and D.A. Maltz, "Dynamic Source Routing in Ad Hoc Wireless Networks," Mobile Computing, pp. 153-181, Kluwer Academic Publishers, 1996.
[32] C. Perkins and E. Royer, "Ad-Hoc On-Demand Distance Vector Routing," Proc. IEEE Second Workshop Mobile Computing Systems and Applications (WMCSA '99), pp. 90-100, Feb. 1999.
44 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool