The Community for Technology Leaders
RSS Icon
Issue No.08 - Aug. (2013 vol.24)
pp: 1525-1534
Chun Tung Chou , University of New South Wales, Sydney
Aleksandar Ignjatovic , University of New South Wales, Syndey
Wen Hu , CSIRO, Australia, Brisbane
Wireless sensor networks (WSNs) enable the collection of physical measurements over a large geographic area. It is often the case that we are interested in computing and tracking the spatial-average of the sensor measurements over a region of the WSN. Unfortunately, the standard average operation is not robust because it is highly susceptible to sensor faults and heterogeneous measurement noise. In this paper, we propose a computational efficient method to compute a weighted average (which we will call robust average) of sensor measurements, which appropriately takes sensor faults and sensor noise into consideration. We assume that the sensors in the WSN use random projections to compress the data and send the compressed data to the data fusion centre. Computational efficiency of our method is achieved by having the data fusion centre work directly with the compressed data streams. The key advantage of our proposed method is that the data fusion centre only needs to perform decompression once to compute the robust average, thus greatly reducing the computational requirements. We apply our proposed method to the data collected from two WSN deployments to demonstrate its efficiency and accuracy.
Robustness, Wireless sensor networks, Equations, Compressed sensing, Noise, Vectors, data fusion, Robustness, Wireless sensor networks, Equations, Compressed sensing, Noise, Vectors, robust averaging, Wireless sensor networks, compressive sensing, distributed compressive sensing, fault tolerance
Chun Tung Chou, Aleksandar Ignjatovic, Wen Hu, "Efficient Computation of Robust Average of Compressive Sensing Data in Wireless Sensor Networks in the Presence of Sensor Faults", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 8, pp. 1525-1534, Aug. 2013, doi:10.1109/TPDS.2012.260
[1] D. Achlioptas, "Database-Friendly Random Projections: Johnson-Lindenstrauss with Binary Coins," J. Computer and System Sciences, vol. 66, pp. 671-687, Jan. 2003.
[2] K.J. Åström and R.M. Murray, Feedback Systems: An Introduction for Scientists and Engineers. Princeton Univ. Press, 2008.
[3] W. Bajwa, J. Haupt, A. Sayeed, and R. Nowak, "Joint Source-Channel Communication for Distributed Estimation in Sensor Networks," IEEE Trans. Information Theory, vol. 53, no. 10, pp. 3629-3653, Oct. 2007.
[4] M.M. Breunig, H.-P. Kriegel, R.T. Ng, and J. Sander, "LOF: Identifying Density-Based Local Outliers," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 93-104, 2000.
[5] N. Bulusu and S. Jha, Wireless Sensor Network Systems. Artech, 2005.
[6] E. Candes and J. Romberg, $\ell_1$ -Magic : Recovery of Sparse Signals via Convex Programming, http://www.acm.caltech.edul1magic/, 2013.
[7] 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.
[8] 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.
[9] C.T. Chou, A. Ignjatovi$\grave{\rm c}$, and W. Hu, "Efficient Computation of Robust Average in Wireless Sensor Networks using Compressive Sensing," technical report UNSW0915.pdf, UNSW, 2009.
[10] C.T. Chou, R. Rana, and W. Hu, "Energy Efficient Information Collection in Wireless Sensor Networks Using Adaptive Compressive Sensing," Proc IEEE 34th Conf. Local Computer Networks (LCN '09), 2009.
[11] P. Corke and P. Sikka, "Demo Abstract: FOS - A New Operating System for Sensor Networks," Proc. Fifth European Conf. Wireless Sensor Networks (EWSN), 2008.
[12] M.A. Davenport, P.T. Boufounos, M.B. Wakin, and R.G. Baraniuk, "Signal Processing with Compressive Measurements," IEEE J. Selected Topics in Signal Processing, vol. 4, no. 2, pp. 445-460, Apr. 2010.
[13] C. de Kerchove and P.V. Dooren, "Iterative Filtering for a Dynamical Reputation System," Arxiv preprint arXiv:0711.3964, Jan. 2007.
[14] T.L. Dinh, W. Hu, P. Sikka, P. Corke, L. Overs, and S. Brosnan, "Design and Deployment of a Remote Robust Sensor Network: Experiences from an Outdoor Water Quality Monitoring Network," Proc. IEEE Ann. Conf. Local Computer Networks, pp. 799-806, 2007.
[15] D. Donoho, "Compressed Sensing," IEEE Trans. Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
[16] M. Duarte, M. Wakin, D. Baron, and R. Baraniuk, "Universal Distributed Sensing via Random Projections," Proc. Fifth Int'l Conf. Information Processing in Sensor Networks (IPSN '06), Apr. 2006.
[17] S. Ganeriwal, L. Balzano, and M. Srivastava, "Reputation-Based Framework for High Integrity Sensor Networks," Trans. Sensor Networks, vol. 4, no. 3,article 15, May 2008.
[18] F. Grubbs, "Procedures for Detecting Outlying Observations in Samples," Technometrics, vol. 11, no. 1, pp. 1-21, 1969.
[19] R.A. Horn and C.R. Johnson, Matrix Analysis. Cambridge Univ. Press, 1990.
[20] A. Ignjatovi$\grave{\rm c}$, C.T. Lee, P. Compton, C. Cutay, and H. Guo, "Computing Marks from Multiple Assessors using Adaptive Averaging," Proc. Int'l Conf. Eng. Education (ICEE), 2009.
[21] B. Krishnamachari and S. Iyengar, "Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks," IEEE Trans. Computers, vol. 53, no. 3, pp. 241-250, Jan. 2004.
[22] P. Laureti, L. moret, Y. Zhang, and Y. Yu, "Information Filtering via Iterative Refinement," Europhysics Letters, vol. 75, Jan. 2006.
[23] S. Lee, S. Pattem, and M. Sathiamoorthy, "Spatially-Localized Compressed Sensing and Routing in Multi-Hop Sensor Networks," Proc. Third Int'l Conf. Geosensor Networks (GSN), 2009.
[24] P. Li, T. Hastie, and K. Church, "Very Sparse Random Projections," Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '06), Aug. 2006.
[25] C. Luo, F. Wu, J. Sun, and C. Chen, "Compressive Data Gathering for Large-Scale Wireless Sensor Networks," Proc. ACM Mobicom '09, Sept. 2009.
[26] K. Ni and G. Pottie, "Bayesian Selection of Non-Faulty Sensors," Proc. IEEE Int'l Symp. Information Theory, pp. 616-620, 2007.
[27] K. Ni, N. Ramanathan, M. Chehade, L. Balzano, S. Nair, S. Zahedi, E. Kohler, G. Pottie, M. Hansen, and M. Srivastava, "Sensor Network Data Fault Types," Trans. Sensor Networks, vol. 5, no. 3,article 25, May 2009.
[28] O. Obst, "Poster Abstract: Distributed Fault Detection Using a Recurrent Neural Network," Proc. Int'l Conf. Information Processing in Sensor Networks (IPSN '09), pp. 373-374, 2009.
[29] G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi, "On the Interplay between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks," Proc. Information Theory and Applications Workshop (ITA '09), 2009.
[30] R. Rana, W. Hu, T. Wark, and C.T. Chou, "An Adaptive Algorithm for Compressive Approximation of Trajectory (AACAT) for Delay Tolerant Networks," Proc. Eighth European Conf. Wireless Sensor Networks (EWSN '11), Feb. 2011.
[31] R.K. Rana, C.T. Chou, S.S. Kanhere, N. Bulusu, and W. Hu, "Ear-Phone: An End-to-End Participatory Urban Noise Mapping System," Proc. ACM/IEEE Ninth Int'l Conf. Information Processing in Sensor Networks (IPSN '10), Apr. 2010.
[32] Y. Shen, W. Hu, R. Rana, and C.T. Chou, "Non-Uniform Compressive Sensing in Wireless Sensor Networks: Feasibility and Application," Proc. Seventh Int'l Conf. Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 271-276, 2011.
[33] H. White, "Maximum Likelihood Estimation of Misspecified Models," Econometrica: J. Econometric Soc., vol. 50, pp. 1-25, Jan. 1982.
[34] G. Zames, "On the Input-Output Stability of Time-Varying Nonlinear Feedback Systems Part One: Conditions Derived Using Concepts of Loop Gain, Conicity, and Positivity," IEEE Trans. Automatic Control, vol. AC-11, no. 2, pp. 228-238, Jan. 1966.
46 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool