Minimal Energy Decentralized Estimation via Exploiting the Statistical Knowledge of Sensor Noise Variance

Wu, Jwo-Yuh; Huang, Qian-Zhi; Lee, Ta-Sung

doi:10.1109/tsp.2007.912281

Cited by 40 publications

(2 citation statements)

References 8 publications

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“…with ( ) p z refers to the associated distribution of observation noise power. The inequality above can be simplified as [4]:…”

Section: Problom Formulationmentioning

confidence: 99%

“…They set the data length equals to the constellation size in order to minimize the delay, although we know that the transmission delay is not a severe constraint in sensor networks sometimes. Since timely knowledge of the instantaneous noise profiles will be too costly to acquire, the authors in [4] provide an energyefficient decentralized estimation solution by considering the long-term noise variance information. However in their transmission model, all the sensors use the same bit rate, which implies each sensor's transmission time is uniquely determined by the length of transmission data.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient quantization and transmission in distributed estimation

Pan

Mao

Qiu

2010

Proceedings of the 5th International ICST Conference on Communications and Networking in China

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In this paper, we investigate the problem of energyefficient distributed estimation in wireless sensor networks. In an inhomogeneous sensing and transmission environment, the minimization of total energy is jointly determined by the optimal quantization and transmission scheduling. In order to minimize the total energy under the mean squared error (MSE) and total transmission time constraints, we design a joint algorithm which iteratively computes the optimal quantization lengths and transmission times. We proved that the algorithm is convergent. Simulations show that the iteration converges quickly, and significant energy saving can be achieved when compared with the uniform quantization and transmission scheme.

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“…with ( ) p z refers to the associated distribution of observation noise power. The inequality above can be simplified as [4]:…”

Section: Problom Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-efficient quantization and transmission in distributed estimation

Pan

Mao

Qiu

2010

Proceedings of the 5th International ICST Conference on Communications and Networking in China

View full text Add to dashboard Cite

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Adaptive control and signal processing literature survey (No. 4)

2008

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at l.giovanini@eee.strath.ac.uk. ADAPTIVE SYSTEMSCai Z, Freeman C, Lewin P, Rogers E. Iterative learning control for a non-minimum phase plant based on a reference shift algorithm. Control Engineering Practice (Special Section on Large Scale Systems) 2008; 16(6):633-643. Das T, Mukherjee R. Optimally switched linear systems. Automatica 2008; 44(5): 1437-1441. Hinnen K, Verhaegen M, Doelman N. A datadriven-optimal control approach for adaptive optics. IEEE Transactions on Control Systems Technology 2008; 16(3):381-395. Yan L, Hsu L, Costa R, Lizarralde F. A variable structure model reference robust control without a prior knowledge of high frequency gain sign. Automatica 2008; 44(4):1036-1044. Zhao J, Hill D. On stability, L 2 -gain and H ∞ control for switched systems. Automatica 2008; 44(5):1220-1232. Zhou J, Lu J, Lu J. Pinning adaptive synchronization of a general complex dynamical network. Automatica 2008; 44(4):996-1003. 2. SIGNAL PROCESSING Bagajewicz M, Nguyen D-Q. Stochastic-based accuracy of data reconciliation estimators for linear systems. Computers and Chemical Engineering 2008; 32(6):1257-1269. Ellis T, McNames J, Goldstein B. Optimal filter design to compute the mean of cardiovascular pressure signals. IEEE Transactions on Biomedical Engineering 2008; 55(4): 1399-1407. Feng Z, Teo K, Rehbock V. Hybrid method for a general optimal sensor scheduling problem in discrete time. Automatica 2008; 44(5): 1295-1303. Fira C, Goras L. An ECG signals compression method and its validation using NNs. IEEE Transactions on Biomedical Engineering 2008; 55(4):1319-1328. Foo Y, Soh Y. Robust Kalman filtering for uncertain discrete-time systems with probabilistic parameters bounded within a polytope. Systems and Control Letters 2008; 57(6): 482-488. Gao Y, Tao G-L, Deng Z-L. Decoupled distributed Kalman fuser for descriptor systems. Signal Processing 2008; 88(5):1261-1270. Geromel J, Korogui R. Robust filter design with performance certificate via convex programming. Automatica 2008; 44(4):937-948.

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