Download or read book Distributed Parameter and State Estimation for Wireless Sensor Networks written by Jia Yu and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Distributed Parameter Estimation in Wireless Sensor Networks written by Jongmin Lee (Electrical engineer) and published by . This book was released on 2017 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities. Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate to each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense. Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis. Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.

Download or read book Distributed Parameter Estimation Using Sensor Networks with Multiple Receive Antennas written by Anthony Dale Smith and published by . This book was released on 2008 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems written by Maciej Patan and published by Springer. This book was released on 2012-02-23 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed algorithms, and the numerical examples to be invaluable.

Download or read book Wireless Sensor Networks written by Cailian Chen and published by Springer. This book was released on 2014-12-10 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief evaluates the cooperative effort of sensor nodes to accomplish high-level tasks with sensing, data processing and communication. The metrics of network-wide convergence, unbiasedness, consistency and optimality are discussed through network topology, distributed estimation algorithms and consensus strategy. Systematic analysis reveals that proper deployment of sensor nodes and a small number of low-cost relays (without sensing function) can speed up the information fusion and thus improve the estimation capability of wireless sensor networks (WSNs). This brief also investigates the spatial distribution of sensor nodes and basic scalable estimation algorithms, the consensus-based estimation capability for a class of relay assisted sensor networks with asymmetric communication topology, and the problem of filter design for mobile target tracking over WSNs. From the system perspective, the network topology is closely related to the capability and efficiency of network-wide scalable distributed estimation. Wireless Sensor Networks: Distributed Consensus Estimation is a valuable resource for researchers and professionals working in wireless communications, networks and distributed computing. Advanced-level students studying computer science and electrical engineering will also find the content helpful.

Download or read book Distributed Estimation and Quantization Algorithms for Wireless Sensor Networks written by Sahar Movaghati and published by . This book was released on 2014 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: In distributed sensing systems, measurements from a random process or parameter are usually not available in one place. Also, the processing resources are distributed over the network. This distributed characteristic of such sensing systems demands for special attention when an estimation or inference task needs to be done. In contrast to a centralized case, where the raw measurements are transmitted to a fusion centre for processing, distributed processing resources can be used for some local processing, such as data compression or estimation according to distributed quantization or estimation algorithms. Wireless sensor networks (WSNs) consist of small sensor devices with limited power and processing capability, which cooperate through wireless transmission, in order to fulfill a common task. These networks are currently employed on land, underground, and underwater, in a wide range of applications including environmental sensing, industrial and structural monitoring, medical care, etc. However, there are still many impediments that hold back these networks from being pervasive, some of which are characteristics of WSNs, such as scarcity of energy and bandwidth resources and limited processing and storage capability of sensor nodes. Therefore, many challenges still need to be overcome before WSNs can be extensively employed. In this study, we concentrate on developing algorithms that are useful for estimation tasks in distributed sensing systems, such as wireless sensor networks. In designing these algorithms we consider the special constraints and characteristics of such systems, i.e., distributed nature of the measurements and the processing resources, as well as the limited energy of wireless and often small devices. We first investigate a general stochastic inference problem. We design a non-parametric algorithm for tracking a random process using distributed and noisy measurements. Next, we narrow down the problem to the distributed parameter estimation, and design distributed quantizers to compress measurement data while maintaining an accurate estimation of the unknown parameter. The contributions of this thesis are as follows. In Chapter 3, we design an algorithm for the distributed inference problem. We first use factor graphs to model the stochastic dependencies among the variables involved in the problem and factorize the global inference problem to a number of local dependencies. A message passing algorithm called the sum-product algorithm is then used on the factor graph to determine local computations and data exchanges that must be performed by the sensing devices in order to achieve the estimation goal. To tackle the nonlinearities in the problem, we combine the particle filtering and Monte-Carlo sampling in the sum-product algorithm and develop a distributed non-parametric solution for the general nonlinear inference problems. We apply our algorithm to the problem of distributed target tracking and show that even with a few number of particles the algorithm can efficiently track the target. In the next three chapters of the thesis, we focus on the distributed parameter quantization under energy limitations. In such problems, each sensor device sends a compressed version of its noisy observation of the same parameter to the fusion centre, where the parameter is estimated from the received data. In Chapter 4, we design a set of local quantizers that quantize each sensor's measurement to a few bits. We optimize the quantizers' design by maximizing the mutual information of the quantized data and the unknown parameter. At the fusion centre, we design the appropriate estimator that incorporates the compressed data from all sensors to estimate the parameter. For very stringent energy constraints, in Chapter 5, we focus on the binary quantization, where each sensor quantizes its data to exactly one bit. We find a set of local binary quantizers that jointly quantize the unknown variable with high precision. In the fusion centre, a maximum likelihood decoder is designed to estimate the parameter from the received bits. In Chapter 6, for an inhomogeneous scenario, where measurements have different signal-to-noise ratios, we find the best sensor-to-quantizer assignment that minimizes the estimation error, using the Hungarian algorithm.

Download or read book System Design in Wireless Sensor Networks for Parameter Estimation and Dynamic Event Region Detection written by Tao Wu and published by . This book was released on 2013 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, the practical system design issues are studied for statistical inference in wireless sensor networks (WSNs). First, the problem of distributed estimation of an unknown parameter corrupted by noise is studied. Imperfect data transmission between local sensors and a fusion center is considered and modeled as a Rayleigh fading channel. The conventional maximum likelihood estimation usually involves high computational complexity and is not suitable for resource-constrained WSNs. Efficient estimators are designed for different receiver models and their efficiency is shown both theoretically and through experiments. The distributed parameter estimation performance also depends on the selection of local quantization thresholds. Therefore, different threshold schemes are investigated under the minimax criterion. Two quantizer structures (sinusoid function and raised cosine function) are proposed. Simulation results show that the simple sinusoid structure outperforms the intuitive uniform structure and the raised cosine structure achieves near optimal performance. The problem of dynamic event region detection in WSNs is studied next. To provide detection results at each time step, a distributed event region tracking algorithm is proposed. The system dynamics (modeled by a dynamic Markov random field) and information collected from neighbors are used to predict the underlying hypothesis at each sensor node and its local observation is used for update. The performance of the proposed algorithm is analyzed both theoretically and through simulations. Detecting and reconstructing critical dynamic event regions at a control center is an important application of bandwidth-limited WSNs. This problem is studied with emphasis on adaptive bandwidth allocation for sensor data transmission. To meet the stringent bandwidth and energy constraints in WSNs, only a few selected sensors are allowed to transmit compressed data to a control center. To reconstruct and track the field state map at each time step, a processing framework including sensor selection, local and central processing is proposed. Adaptive bandwidth allocation is obtained by solving a conditional entropy based optimization problem. The overall communication costs in terms of bandwidth and energy consumption of the proposed framework are evaluated by considering all possible overheads in a practical communication protocol.

Download or read book Distributed Compression Estimation Using Wireless Sensor Networks written by and published by . This book was released on 2006 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we consider deterministic parameter estimation problems. We study the intertwining of quantization and estimation in general and shows particular results in 1) low SNR situations where the noise standard deviation is in the order of the parameter's dynamic range; and 2) universal estimation when the sensor data and noise model are unknown. The goal is to understand how the signal processing capability of a WSN scales up with its size, and to develop robust distributed signal processing algorithms and protocols with low bandwidth requirement and optimal performance. We show that for universal estimation in low signal to noise ratio (SNR), the universal distributed estimators not only exist but achieve performance close to that of estimators based on the original (un-quantized) observations. We also generalize these results a Bayesian estimation framework with a particular application to state estimation of dynamic stochastic processes.

Download or read book Distributed Fusion Estimation for Sensor Networks with Communication Constraints written by Wen-An Zhang and published by Springer. This book was released on 2016-05-27 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically presents energy-efficient robust fusion estimation methods to achieve thorough and comprehensive results in the context of network-based fusion estimation. It summarizes recent findings on fusion estimation with communication constraints; several novel energy-efficient and robust design methods for dealing with energy constraints and network-induced uncertainties are presented, such as delays, packet losses, and asynchronous information... All the results are presented as algorithms, which are convenient for practical applications.

Download or read book Control and State Estimation for Dynamical Network Systems with Complex Samplings written by Bo Shen and published by CRC Press. This book was released on 2022-09-14 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, sufficient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations. Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspective Systematically introduces the complex sampling concept, methods, and application for the control and state estimation Presents unified framework for control and state estimation problems of dynamical network systems with complex samplings Exploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approach Explains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specifications This book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.

Download or read book State Estimation in Distributed Parameter Systems written by Gary Byron Lamont and published by . This book was released on 1970 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Distributed Network Structure Estimation Using Consensus Methods written by Sai Zhang and published by Springer Nature. This book was released on 2022-05-31 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.

Download or read book State and parameter estimation and optimal sensor location for distributed parameter systems written by H. Malebranche and published by . This book was released on 1986 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Control of Distributed Parameter Systems with State Estimation written by Prashant Sadananda Rao and published by . This book was released on 1988 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Distributed Sensor Networks written by S. Sitharama Iyengar and published by CRC Press. This book was released on 2016-04-19 with total page 767 pages. Available in PDF, EPUB and Kindle. Book excerpt: The best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for

Download or read book On State Estimation for Distributed Parameter Systems written by J. S. Meditch and published by . This book was released on 1969 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sequential algorithms for prediction, filtering, and smoothing are developed for a class of linear distributed parameter systems. The class of systems concerned is that involving noisy measurement data which are obtained from 'averaging' and 'scanner' type sensors. The basic tools of the development are the least-squares estimation viewpoint, the calculus of variations, and the sweep method for two-point boundary-value problems. An example involving the heat equation is presented to illustrate the results. (Author).

Download or read book Numerical Validation in Current Hardware Architectures written by Annie A.M. Cuyt and published by Springer Science & Business Media. This book was released on 2009-04-24 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The major emphasis of the Dagstuhl Seminar on “Numerical Validation in C- rent Hardware Architectures” lay on numerical validation in current hardware architecturesand softwareenvironments. The generalidea wasto bring together experts who are concerned with computer arithmetic in systems with actual processor architectures and scientists who develop, use, and need techniques from veri?ed computation in their applications. Topics of the seminar therefore included: – The ongoing revision of the IEEE 754/854 standard for ?oating-point ari- metic – Feasible ways to implement multiple precision (multiword) arithmetic and to compute the actual precision at run-time according to the needs of input data – The achievement of a similar behavior of ?xed-point, ?oating-point and - terval arithmetic across language compliant implementations – The design of robust and e?cient numerical programsportable from diverse computers to those that adhere to the IEEE standard – The development and propagation of validated special-purpose software in di?erent application areas – Error analysis in several contexts – Certi?cation of numerical programs, veri?cation and validation assessment Computer arithmetic plays an important role at the hardware and software level, when microprocessors, embedded systems, or grids are designed. The re- ability of numerical softwarestrongly depends on the compliance with the cor- sponding ?oating-point norms. Standard CISC processors follow the 1985 IEEE norm 754, which is currently under revision, but the new highly performing CELL processor is not fully IEEE compliant.