A general distributed consensus algorithm for wireless sensor networks
作者： Jinho Choi , Shancang Li , Xinheng Wang , Jeongseok Ha
DOI: 10.1109/WIAD.2012.6296556
关键词: Sensor node 、 Brooks–Iyengar algorithm 、 Iterative method 、 Mobile wireless sensor network 、 Computer science 、 Key distribution in wireless sensor networks 、 Wireless sensor network 、 Algorithm 、 Consensus 、 Distributed algorithm
摘要: In wireless sensor networks, distributed consensus algorithms can be employed for detection. Each node compute its log-likelihood ratio (LLR) from local observations a target event and using an iterative algorithm, the average of sensors' LLRs available to all nodes. While allows each make final decision as statistic overall detection problem with LLRs, it may desirable if or observations, which form full information vector denoted by x, could other purposes more than this paper, we show that have not only but also vector, (or estimate) well-known algorithm. We extend proposed approach estimate x when is sparse based on notion compressed sensing.
sci-hub.se 参考文章(9)
R.O. Saber, R.M. Murray, Consensus protocols for networks of dynamic agents american control conference. ,vol. 2, pp. 951- 956 ,(2003) , 10.1109/ACC.2003.1239709
Lin Xiao, Stephen Boyd, Fast linear iterations for distributed averaging Systems & Control Letters. ,vol. 53, pp. 65- 78 ,(2004) , 10.1016/J.SYSCONLE.2004.02.022
E.J. Candes, M.B. Wakin, An Introduction To Compressive Sampling IEEE Signal Processing Magazine. ,vol. 25, pp. 21- 30 ,(2008) , 10.1109/MSP.2007.914731
J. Haupt, W.U. Bajwa, M. Rabbat, R. Nowak, Compressed Sensing for Networked Data IEEE Signal Processing Magazine. ,vol. 25, pp. 92- 101 ,(2008) , 10.1109/MSP.2007.914732
Emmanuel J. Candes, Terence Tao, Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? IEEE Transactions on Information Theory. ,vol. 52, pp. 5406- 5425 ,(2006) , 10.1109/TIT.2006.885507
E.J. Candes, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information IEEE Transactions on Information Theory. ,vol. 52, pp. 489- 509 ,(2006) , 10.1109/TIT.2005.862083
Qing Zhao, A. Swami, Lang Tong, The interplay between signal processing and networking in sensor networks IEEE Signal Processing Magazine. ,vol. 23, pp. 84- 93 ,(2006) , 10.1109/MSP.2006.1657819
R.L.G. Cavalcante, B. Mulgrew, Adaptive Filter Algorithms for Accelerated Discrete-Time Consensus IEEE Transactions on Signal Processing. ,vol. 58, pp. 1049- 1058 ,(2010) , 10.1109/TSP.2009.2032450
D.L. Donoho, Compressed sensing ,(2004)