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Distributed scheduling of measurements in a sensor network for parameter estimation of spatio-temporal systems

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EN
Abstrakty
EN
The main aim of the paper is to develop a distributed algorithm for optimal node activation in a sensor network whose measurements are used for parameter estimation of the underlying distributed parameter system. Given a fixed partition of the observation horizon into a finite number of consecutive intervals, the problem under consideration is to optimize the percentage of the total number of observations spent at given sensor nodes in such a way as to maximize the accuracy of system parameter estimates. To achieve this, the determinant of the Fisher information matrix related to the covariance matrix of the parameter estimates is used as the qualitative design criterion (the so-called D-optimality). The proposed approach converts the measurement scheduling problem to a convex optimization one, in which the sensor locations are given a priori and the aim is to determine the associated weights, which quantify the contributions of individual gaged sites to the total measurement plan. Then, adopting a pairwise communication scheme, a fully distributed procedure for calculating the percentage of observations spent at given sensor locations is developed, which is a major novelty here. Another significant contribution of this work consists in derivation of necessary and sufficient conditions for the optimality of solutions. As a result, a simple and effective computational scheme is obtained which can be implemented without resorting to sophisticated numerical software. The delineated approach is illustrated by simulation examples of a sensor network design for a two-dimensional convective diffusion process.
Rocznik
Strony
39--54
Opis fizyczny
Bibliogr. 52 poz., rys., tab.
Twórcy
autor
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland
autor
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland
Bibliografia
  • [1] Alifanov, O.M., Artyukhin, E.A. and Rumyantsev, S.V. (1995). Extreme Methods for Solving Ill Posed Problems with Applications to Inverse Heat Transfer Problems, Begell House, New York, NY.
  • [2] Atkinson, A.C., Donev, A.N. and Tobias, R.D. (2007). Optimum Experimental Designs, with SAS, Oxford University Press, Oxford.
  • [3] Boukerche, A. (2006). Handbook of Algorithms for Wireless Networking and Mobile Computing, Chapman & Hall/CRC, Boca Raton, FL.
  • [4] Boyd, S., Ghosh, A., Prabhakar, B. and Shah, D. (2006). Randomized gossip algorithms, IEEE Transactions on Information Theory 52(6): 2508–2530.
  • [5] Braca, P., Marano, S. and Matta, V. (2008). Enforcing consensus while monitoring the environment in wireless sensor network, IEEE Transactions on Signal Processing 56(7): 3375–3380.
  • [6] Cacuci, D.G., Navon, I.M. and Ionescu-Bujor, M. (2014). Computational Methods for Data Evaluation and Assimilation, CRC Press, Boca Raton, FL.
  • [7] Cassandras, C.G. and Li, W. (2005). Sensor networks and cooperative control, European Journal of Control 11(4–5): 436–463.
  • [8] Demetriou, M.A. (2010). Guidance of mobile actuator-plus-sensor networks for improved control and estimation of distributed parameter systems, IEEE Transactions on Automatic Control 55(7): 1570–1584.
  • [9] Demetriou, M.A. and Hussein, I.I. (2009). Estimation of spatially distributed processes using mobile spatially distributed sensor network, SIAM Journal on Control and Optimization 48(1): 266–291.
  • [10] Fedorov, V.V. and Hackl, P. (1997). Model-Oriented Design of Experiments, Lecture Notes in Statistics, Vol. 125, Springer-Verlag, New York, NY.
  • [11] Holder, D.S. (Ed.) (2004). Electrical Impedance Tomography: Methods, History and Applications, Taylor & Francis, Philadelphia, PA.
  • [12] Jacobson, M.Z. (1999). Fundamentals of Atmospheric Modeling, Cambridge University Press, Cambridge.
  • [13] Jain, N. and Agrawal, D.P. (2005). Current trends in wireless sensor network design, International Journal of Distributed Sensor Networks 1(1): 101–122.
  • [14] Joshi, S. and Boyd, S. (2009). Sensor selection via convex optimization, IEEE Transactions on Signal Processing 57(2): 451–462.
  • [15] Kowalów, D., Patan, M., Paszke, W. and Romanek, A. (2015). Sequential design for model calibration in iterative learning control of DC motor, 20th International Conference on Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp. 794–799.
  • [16] Kubrusly, C.S. and Malebranche, H. (1985). Sensors and controllers location in distributed systems—A survey, Automatica 21(2): 117–128.
  • [17] Nehorai, A., Porat, B. and Paldi, E. (1995). Detection and localization of vapor-emitting sources, IEEE Transactions on Signal Processing 43(1): 243–253.
  • [18] Ögren, P., Fiorelli, E. and Leonard, N.E. (2004). Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment, IEEE Transactions on Automatic Control 49(8): 1292–1302.
  • [19] Patan, K. and Patan, M. (2010). Selection of training data for locally recurrent neural network, in K. Diamantaras et al. (Eds.), Artificial Neural Networks—ICANN 2010, Lecture Notes in Computer Science, Vol. 6353, Springer, Berlin/Heidelberg, pp. 134–137.
  • [20] Patan, M. (2006). Optimal activation policies for continuous scanning observations in parameter estimation of distributed systems, International Journal of Systems Science 37(11): 763–775.
  • [21] Patan, M. (2008). A parallel sensor scheduling technique for fault detection in distributed parameter systems, in E. Luque et al. (Eds.), Parallel Processing—Euro Par 2008, Lecture Notes in Computer Science, Vol. 5168, Springer, Berlin/Heidelberg, pp. 833–843.
  • [22] Patan, M. (2012a). Distributed scheduling of sensor networks for identification of spatio-temporal processes, International Journal of Applied Mathematics and Computer Science 22(2): 299–311, DOI: 10.2478/v10006-012-0022-9.
  • [23] Patan, M. (2012b). Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems, Springer-Verlag, Berlin.
  • [24] Patan, M. and Bogacka, B. (2007). Optimum experimental designs for dynamic systems in the presence of correlated errors, Computational Statistics & Data Analysis 51(12): 5644–5661.
  • [25] Patan, M., Chen, Y. and Tricaud, C. (2008). Resource-constrained sensor routing for parameter estimation of distributed systems, 17th IFAC World Congress, Seoul, South Korea, pp. 7772–7777.
  • [26] Patan, M. and Kowalów, D. (2014). Robust sensor scheduling via iterative design for parameter estimation of distributed systems, 19th International Conference on Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp. 618–623.
  • [27] Patan, M. and Romanek, A. (2016). Communication scheduling for fast distributed averaging in sensor networks, 2016 International Symposium on Wireless Communication Systems, Poznań, Poland, pp. 109–113.
  • [28] Patan, M. and Uciński, D. (2010). Time-constrained sensor scheduling for parameter estimation of distributed systems, 49th IEEE Conference on Decision and Control, Atlanta, GA, USA, pp. 7–12.
  • [29] Patan, M. and Uciński, D. (2016a). Cost-constrained D-optimum node activation for large-scale monitoring networks, IEEE 2016 American Control Conference (ACC), Boston, MA, USA, pp. 1643–1648.
  • [30] Patan, M. and Uciński, D. (2016b). D-optimal spatio-temporal sampling design for identification of distributed parameter systems, 55th IEEE Conference on Decision and Control, Las Vegas, NV, USA, pp. 3985–3990.
  • [31] Pázman, A. (1986). Foundations of Optimum Experimental Design, Mathematics and Its Applications, D. Reidel Publishing Company, Dordrecht.
  • [32] Point, N., Wouwer, A.V. and Remy, M. (1996). Practical issues in distributed parameter estimation: Gradient computation and optimal experiment design, Control Engineering Practice 4(11): 1553–1562.
  • [33] Porat, B. and Nehorai, A. (1996). Localizing vapor-emitting sources by moving sensors, IEEE Transactions on Signal Processing 44(4): 1018–1021.
  • [34] Pronzato, L. (2003). Removing non-optimal support points in D-optimum designs algorithms, Statistics and Probability Letters 63(3): 223–228.
  • [35] Rafajłowicz, E. (1986). Optimum choice of moving sensor trajectories for distributed-parameter system identification, International Journal of Control 43(5): 1441–1451.
  • [36] Silvey, S.D. (1980). Optimal Design. An Introduction to the Theory for Parameter Estimation, Chapman & Hall, London.
  • [37] Silvey, S.D., Titterington, D.M. and Torsney, B. (1978). An algorithm for optimal designs on a finite design space, Communications in Statistics—Theory and Methods 7(14): 1379–1389.
  • [38] Song, Z., Chen, Y., Sastry, C.R. and Tas, N.C. (2009). Optimal Observation for Cyber-Physical Systems: A Fisher-Information-Matrix-Based Approach, Springer-Verlag, London.
  • [39] Sun, N.-Z. (1994). Inverse Problems in Groundwater Modeling, Theory and Applications of Transport in Porous Media, Kluwer Academic Publishers, Dordrecht.
  • [40] Torsney, B. and Mandal, S. (2004). Multiplicative algorithms for constructing optimizing distributions: Further developments, in A. Di Bucchianico et al. (Eds), mODa 7: 7th International Workshop on Model-Oriented Data Analysis, Physica-Verlag, Heidelberg, pp. 163–171.
  • [41] Tricaud, C. and Chen, Y. (2012). Optimal Mobile Sensing and Actuation Policies in Cyber-physical Systems, Springer-Verlag, London.
  • [42] Tricaud, C., Patan, M., Uciński, D. and Chen, Y. (2008). D-optimal trajectory design of heterogeneous mobile sensors for parameter estimation of distributed systems, American Control Conference, Seattle, WA, USA, pp. 663–668.
  • [43] Uciński, D. (2000). Optimal selection of measurement locations for parameter estimation in distributed processes, International Journal of Applied Mathematics and Computer Science 10(2): 357–379.
  • [44] Uciński, D. (2004). Optimal Measurement Methods for Distributed Parameter System Identification, CRC Press, Boca Raton, FL.
  • [45] Uciński, D. (2012). Sensor network scheduling for identification of spatially distributed processes, International Journal of Applied Mathematics and Computer Science 22(1): 25–40, DOI: 10.2478/v10006-012-0002-0.
  • [46] Uciński, D. and Patan, M. (2002). Optimal location of discrete scanning sensors for parameter estimation of distributed systems, 15th IFAC World Congress, Barcelona, Spain, pp. 22–26.
  • [47] Uciński, D. and Patan, M. (2007). D-optimal design of a monitoring network for parameter estimation of distributed systems, Journal of Global Optimization 39(2): 291–322.
  • [48] Walter, É. and Pronzato, L. (1997). Identification of Parametric Models from Experimental Data, Communications and Control Engineering, Springer-Verlag, Berlin.
  • [49] Wu, J. (Ed.) (2006). Handbook on Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks, Auerbach Publications/Taylor & Francis Group, Boca Raton, FL.
  • [50] Xiao, L. and Boyd, S. (2004). Fast linear iterations for distributed averaging, Systems & Control Letters 53(1): 65–78.
  • [51] Zhao, F. and Guibas, L.J. (2004). Wireless Sensor Networks: An Information Processing Approach, Morgan Kaufmann, Amsterdam.
  • [52] Zhong, M. and Cassandras, C.G. (2011). Distributed coverage control and data collection with mobile sensor networks, IEEE Transactions on Automatic Control 56(10): 2445–2455.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-69bb6574-1055-48b8-b716-ca1a75d4a7e8
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