Download or read book Distributed Position Estimation for Wireless Sensor Networks written by Victor Cheung and published by . This book was released on 2006 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Detection and Estimation in Wireless Sensor Networks written by Mohammad Fanaei and published by . This book was released on 2016 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiscale Optimization Methods and Applications written by William W. Hager and published by Springer Science & Business Media. This book was released on 2006-06-18 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level. This volume contains 22 chapters highlighting some recent research. The topics of the chapters selected for this volume are focused on the development of new solution methodologies, including general multilevel solution techniques, for tackling difficult, large-scale optimization problems that arise in science and industry. Applications presented in the book include but are not limited to the circuit placement problem in VLSI design, a wireless sensor location problem, optimal dosages in the treatment of cancer by radiation therapy, and facility location.

Download or read book Localization Algorithms and Strategies for Wireless Sensor Networks Monitoring and Surveillance Techniques for Target Tracking written by Mao, Guoqiang and published by IGI Global. This book was released on 2009-05-31 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks. Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.

Download or read book Distributed Quantization estimation for Wireless Sensor Networks written by Alejandro Ribeiro and published by . This book was released on 2006 with total page 242 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 751 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 Distributed Estimation in Wireless Sensor Networks Under a Semi orthogonal Multiple Access Technique written by Jian Su and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Distributed Algorithms for Localization and Management in Wireless Ad hoc Sensor Networks written by Chih-Yu Wen and published by . This book was released on 2005 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Wireless Sensor Networks written by Kazem Sohraby and published by John Wiley & Sons. This book was released on 2007-04-06 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Infrastructure for Homeland Security Environments Wireless Sensor Networks helps readers discover the emerging field of low-cost standards-based sensors that promise a high order of spatial and temporal resolution and accuracy in an ever-increasing universe of applications. It shares the latest advances in science and engineering paving the way towards a large plethora of new applications in such areas as infrastructure protection and security, healthcare, energy, food safety, RFID, ZigBee, and processing. Unlike other books on wireless sensor networks that focus on limited topics in the field, this book is a broad introduction that covers all the major technology, standards, and application topics. It contains everything readers need to know to enter this burgeoning field, including current applications and promising research and development; communication and networking protocols; middleware architecture for wireless sensor networks; and security and management. The straightforward and engaging writing style of this book makes even complex concepts and processes easy to follow and understand. In addition, it offers several features that help readers grasp the material and then apply their knowledge in designing their own wireless sensor network systems: * Examples illustrate how concepts are applied to the development and application of * wireless sensor networks * Detailed case studies set forth all the steps of design and implementation needed to solve real-world problems * Chapter conclusions that serve as an excellent review by stressing the chapter's key concepts * References in each chapter guide readers to in-depth discussions of individual topics This book is ideal for networking designers and engineers who want to fully exploit this new technology and for government employees who are concerned about homeland security. With its examples, it is appropriate for use as a coursebook for upper-level undergraduates and graduate students.

Download or read book On Distributed Estimation for Resource Constrained Wireless Sensor Networks written by Alireza Sani and published by . This book was released on 2017 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly:

Download or read book Contributions to Distributed Detection and Estimation Over Sensor Networks written by Gene Whipps and published by . This book was released on 2017 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless sensor networks have matured over the last several years from popular research and development platforms to commercially-available sensors and systems. In many applications, wireless sensor networks have size, weight, power, and cost limitations. These constraints directly affect the ability of sensor nodes to adequately process and reliably communicate information within the sensor network. This dissertation examines aspects of distributed detection and estimation over a sensor network while considering limitations inherent in wireless networks. First, we consider the problem of distributed detection from a large network of sensors and introduce a realistic network model. Sensor nodes make individual decisions from their local observation and then communicate these decisions through a shared and imperfect communications channel to a central decision node. The key difference from previous research is the network model allows the decision rule to leverage errors in the channel to improve detection performance. We derive analytical expressions that characterize the detection performance of the system with respect to both sensor density and communications delay. We show that the detection performance improves with network density when sensor nodes are appropriately censored and desensitized, despite increasing message collisions. In addition, we show that detection performance using the protocol model, with imperfect communications, rapidly converges to the perfect communications case as the number of communication slots increase.

Download or read book Theoretical Aspects of Distributed Computing in Sensor Networks written by Sotiris Nikoletseas and published by Springer Science & Business Media. This book was released on 2011-01-15 with total page 904 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless ad hoc sensor networks has recently become a very active research subject. Achieving efficient, fault-tolerant realizations of very large, highly dynamic, complex, unconventional networks is a real challenge for abstract modelling, algorithmic design and analysis, but a solid foundational and theoretical background seems to be lacking. This book presents high-quality contributions by leading experts worldwide on the key algorithmic and complexity-theoretic aspects of wireless sensor networks. The intended audience includes researchers and graduate students working on sensor networks, and the broader areas of wireless networking and distributed computing, as well as practitioners in the relevant application areas. The book can also serve as a text for advanced courses and seminars.

Download or read book Duality and Approximation Methods for Cooperative Optimization and Control written by Mathias Bürger and published by Logos Verlag Berlin GmbH. This book was released on 2014 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates the role of duality and the use of approximation methods in cooperative optimization and control. Concerning cooperative optimization, a general algorithm for convex optimization in networks with asynchronous communication is presented. Based on the idea of polyhedral approximations, a family of distributed algorithms is developed to solve a variety of distributed decision problems, ranging from semi-definite and robust optimization problems up to distributed model predictive control. Optimization theory, and in particular duality theory, are shown to be central elements also in cooperative control. This thesis establishes an intimate relation between passivity-based cooperative control and network optimization theory. The presented results provide a complete duality theory for passivity-based cooperative control and lead the way to novel analysis tools for complex dynamic phenomena. In this way, this thesis presents theoretical insights and algorithmic approaches for cooperative optimization and control, and emphasizes the role of convexity and duality in this field.

Download or read book Distributed Estimation in Sensor Networks with Modeling Uncertainty written by Qing Zhou and published by . This book was released on 2013 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major issue in distributed wireless sensor networks (WSNs) is the design of efficient distributed algorithms for network-wide dissemination of information acquired by individual sensors, where each sensor, by itself, is unable to access enough data for reliable decision making. Without a centralized fusion center, network-wide reliable inferencing can be accomplished by recovering meaningful global statistics at each sensor through iterative inter-sensor message passing. In this dissertation, we first consider the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a WSN, where each sensor receives a single snapshot of the field. An iterative distributed least-squares (DLS) algorithm is investigated with and without the consideration of node failures. In particular, without sensor node failures it is shown that every instantiation of the DLS algorithm converges, i.e., consensus is reached among the sensors, with the limiting agreement value being the centralized least-squares estimate. With node failures during the iterative exchange process, the convergence of the DLS algorithm is still guaranteed; however, an error exists be- tween the limiting agreement value and the centralized least-squares estimate. In order to reduce this error, a modified DLS scheme, the M-DLS, is provided. The M-DLS algorithm involves an additional weight compensation step, in which a sensor performs a one-time weight compensation procedure whenever it detects the failure of a neighbor. Through analytical arguments and simulations, it is shown that the M-DLS algorithm leads to a smaller error than the DLS algorithm, where the magnitude of the improvement dependents on the network topology. We then investigate the case when the observation or sensing mode is only partially known at the corresponding nodes, perhaps, due to their limited sensing capabilities or other unpredictable physical factors. Specifically, it is assumed that the observation validity at a node switches stochastically between two modes, with mode I corresponding to the desired signal plus noise observation mode (a valid observation), and mode II corresponding to pure noise with no signal information (an invalid observation). With no prior information on the local sensing modes (valid or invalid), we introduce a learning-based distributed estimation procedure, the mixed detection-estimation (MDE) algorithm, based on closed-loop interactions between the iterative distributed mode learning and the target estimation. The online learning (or sensing mode detection) step re-assesses the validity of the local observations at each iteration, thus refining the ongoing estimation update process. The convergence of the MDE algorithm is established analytically, and the asymptotic performance analysis studies shows that, in the high signal-to-noise ratio (SNR) regime, the MDE estimation error converges to that of an ideal (centralized) estimator with perfect information about the node sensing modes. This is in contrast with the estimation performance of a naive average consensus based distributed estimator (with no mode learning), whose estimation error blows up with an increasing SNR. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/149566

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 Wireless Sensor Networks written by Shafiullah Khan and published by CRC Press. This book was released on 2016-04-21 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless sensor networks (WSNs) utilize fast, cheap, and effective applications to imitate the human intelligence capability of sensing on a wider distributed scale. But acquiring data from the deployment area of a WSN is not always easy and multiple issues arise, including the limited resources of sensor devices run with one-time batteries. Additi