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Nurellari, E; McLernon, DC; Ghogho, M (2016)
Publisher: Institute of Electrical and Electronics Engineers
Languages: English
Types: Article
Subjects: H641 Telecommunications Engineering, H600 Electronic and Electrical Engineering
We consider the problem of distributed soft decision fusion in a bandwidth-constrained spatially uncorrelated wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. Existing distributed consensus-based fusion rules algorithms only ensure equal combining of local data and in the case of bandwidth-constrained WSNs, we show that their performance is poor and does not converge across the sensor nodes (SNs). Motivated by this fact, we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels (the SNs exchange quantized information matched to the channel capacity of each link). In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a fusion center) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] J. N. Tsitsiklis, “Decentralized detection,” In Advances in Statistical Signal Processing: vol. 2 - Signal Detection, H. V. Poor, and John B. Thomas, eds., JAI Press, Greenwich, CT, pp. 297-344, Nov. 1993.
    • [2] R. Niu, B. Chen, and P. K. Varshney, “Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks,” IEEE Trans. Signal Process., vol. 54, pp. 1018-1027, Mar. 2006.
    • [3] J. F. Chamberland and V. V. Veeravalli, “Asymptotic results for decentralized detection in power constrained wireless sensor networks,” IEEE Journal on Selected Areas in Communications, vol. 22, no. 6, pp. 1007- 1015, Aug. 2004.
    • [4] A. Ribeiro and G. B. Giannakis, “Bandwidth-constrained distributed estimation for wireless sensor networks, part I: Gaussian case,” IEEE Trans. Signal Process., vol. 54, no. 3, pp. 1131-1143, 2006.
    • [5] J. J. Xiao, S. Cui, Z. Q. Luo, and A. J. Goldsmith, “Power scheduling of universal decentralized estimation in sensor networks,” IEEE Trans. Signal Process., vol. 54, no. 2, pp. 413-422, Feb. 2006.
    • [6] S. Barbarossa, S. Sardellitti, and P. Di Lorenzo, “Distributed Detection and Estimation in Wireless Sensor Networks,” In Rama Chellappa and Sergios Theodoridis eds., Academic Press Library in Signal Processing, vol. 2, Communications and Radar Sig. Process., pp. 329-408, 2014.
    • [7] Z. Quan, S.Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE J. Sel. Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, Feb. 2008.
    • [8] J. Li and G. Alregib, “Rate-constrained distributed estimation in wireless sensor networks,” IEEE Trans. Signal Process., vol. 55, pp. 1634-1643, May 2007.
    • [9] X. Zhang, H. V. Poor, and M. Chiang, “Optimal power allocation for distributed detection over MIMO channels in wireless sensor networks,” IEEE Trans. Signal Process., vol. 56, no. 9, pp. 4124-4140, Sep. 2008.
    • [10] E. Nurellari, D. McLernon, M. Ghogho, and S. Aldalahmeh, “Optimal quantization and power allocation for energy-based distributed sensor detection,” Proc. EUSIPCO, Lisbon, Portugal, 1-5 Sep. 2014.
    • [11] E. Nurellari, S. Aldalahmeh, M. Ghogho, and D. McLernon, “Quantized Fusion Rules for Energy-Based Distributed Detection in Wireless Sensor Networks,” Proc. SSPD, Edinburgh, Scotland, 8-9 Sep. 2014.
    • [12] J. J. Xiao and Z. Q. Luo, “Universal Decentralized Detection in a Bandwidth-Constrained Sensor Network,” IEEE Trans. Signal Process., vol. 53, no. 8, pp. 2617-2624, Aug. 2005.
    • [13] S. Kar and P. K. Varshney, “A decentralized framework for linear coherent estimation with spatial collaboration,” Proc. ICASSP, Florence, Italy, 4-9 May. 2014.
    • [14] M. Fanaei, M. C. Valenti, A. Jamalipour, and N. A. Schmid, “Optimal power allocation for distributed blue estimation with linear spatial collaboration,” Proc. ICASSP, Florence, Italy, 4-9 May 2014.
    • [15] S. Kar and P. K. Varshney, “Linear coherent estimation with spatial collaboration,” IEEE Trans. Information Theory, vol. 59, no. 6, pp. 3532- 3553, 2013.
    • [16] D. Estrin, L. Girod, G. Pottie, and M. Srivastava, “Instrumenting the world with wireless sensor networks,” Proc. ICASSP, Salt Lake City, UT, vol. 4, pp. 2033-2036, May 2001,
    • [17] F. Cattivelli and A. H. Sayed, “Diffusion LMS strategies for distributed estimation,” IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1035-1048, Mar. 2010.
    • [18] F. Cattivelli and A. H. Sayed, “Distributed Detection Over Adaptive Networks Using Diffusion Adaptation,” IEEE Trans. Signal Process., vol. 59, no. 5, pp. 1917-1932, May 2011.
    • [19] I. D. Schizas, G. Mateos, and G. B. Giannakis, “Distributed LMS for consensus-based in-network adaptive processing,” IEEE Trans. Signal Process., vol. 8, no. 6, pp. 2365-2381, Jun. 2009.
    • [20] S. Barbarossa and G. Scutari, “Bio-Inspired Sensor Network Design: Distributed decisions through self-synchronization,” IEEE Signal Processing Magazine, pp. 26-35, May 2007.
    • [21] S. Barbarossa and G. Scutari, “Distributed Decision Through SelfSynchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels,” IEEE Trans. Signal Process., vol. 56, no. 4, pp. 1667-1684, Apr. 2008.
    • [22] A. Bertrand and M. Moonen, Distributed computation of the Fiedler vector with application to topology inference in ad hoc networks,” Signal Process., vol. 93, no. 5, pp. 1106-1117, May 2013.
    • [23] A. Bertrand and M. Moonen, Topology-aware distributed adaption of laplacian weights for in-network averaging,” in Proc. EUSIPCO, Marrakech, Morocco, 9-13 Sep. 2013.
    • [24] S. Kar and J. M. F. Moura, “Topology for Distributed Inference on Graphs,” IEEE Trans. Signal Process., vol. 56, no. 6, pp. 2609-2613, Jun. 2008.
    • [25] W. Zhang, Z. Wang, Y. Guo, H. Liu, Y. Chen, and J. Mitola, “Distributed cooperative spectrum sensing based on weighted average consensus,” Proc. GLOBECOM, Houston, Texas, USA, 5-9 Dec. 2011.
    • [26] L. Xiao and S. Boyd, “Fast linear iteration for distributed averaging,” Sys. Contr. Lett, vol. 53, pp. 65-78, 2004.
    • [27] P. Braca, S. Marano, V. Matta, and P. Willett, “Asymptotic optimality of running consensus in testing binary hypotheses,” IEEE Trans. Signal Process., vol. 58, no. 2, pp. 814-825, Feb. 2010.
    • [28] R. O. Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proc. of the IEEE, 95(1), pp. 215-233, Jan. 2007.
    • [29] T. Aysal, M.Coates, and M. Rabbat, “Distributed Average Consensus with Dithering Quantization,” IEEE Trans. Automatic Control, vol. 56, no. 10, Oct. 2008.
    • [30] S. Kar and J. M. F. Moura, “Distributed consensus algorithms in sensor networks: quantized data and random link failures,” IEEE Trans. Automatic Control, vol. 58, no. 3, pp. 1383-1400, Mar. 2010.
    • [31] D. Thanou, E. Kokiopoulou, Y. Pu, and P. Frossard, “Distributed average consensus with quantization re-finement,” IEEE Trans. Signal Process., vol. 61, no. 1, pp. 194-205, 2013.
    • [32] E. Nurellari, D. McLernon, and M. Ghogho “Distributed detection in practical wireless sensor networks via a two step consensus algorithm,” accepted for publication in Proc. ISP, London, United Kingdom, 1-2 Dec. 2015.
    • [33] H. Urkowitz, “Energy detection of unknown deterministic signals,” Proc. IEEE , vol. 55, pp. 523-531, Apr. 1967.
    • [34] S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory , Englewood Cliffs, NJ: Prentice-Hall PTR, 1993.
    • [35] R. Horn and C. R. Johnson, “Matrix Analysis,” Cambridge University Press, 1985.
    • [36] S. Sardelliti, S. Barbarossa, and A. Swami, “Optimal Topolgy Control and Power Allocation for Minimum Energy Consumption in Consensus Networks,” IEEE Trans. Signal Process., vol. 60, no. 1, Jan. 2012.
    • [37] C. Asensio-Marco and B. Beferull-Lozano, “Network topology optimization for accelerating consensus algorithms under power constraints,” Proc. DCOSS, 16-18 May 2012.
    • [38] S. Zheng, X. Yang, and C. Lou, “Distributed consensus algorithms for decision fusion based cooperative spectrum sensing in cognitive radio,” Communications and Information Technologies (ISCIT), Hangzhou, China, 12-14 Oct. 2011.
    • [39] C.R. Johnson, Positive definite matrices, American Mathematical Monthly, vol. 77, issue 3, pp. 259-264, Mar. 1970.
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