Download or read book Distributed Information Gathering and Estimation in Wireless Sensor Networks Anglais written by Wenjie Li and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless sensor networks (WSNs) have attracted much interests in the last decade. The first part of this thesis considers sparse random linear network coding is for data gathering and compression in WSNs. An information-theoretic approach is applied to demonstrate the necessary and sufficient conditions to realize the asymptotically perfect reconstruction under MAP estimation. The second part of the thesis concerns the distributed self-rating (DSR) problem, for WSNs with nodes that have different ability of performing some task (sensing, detection...). The main assumption is that each node does not know and needs to estimate its ability. Depending on the number of ability levels and the communication conditions, three sub-problems have been addressed: i) distributed faulty node detection (DFD) to identify the nodes equipped with defective sensors in dense WSNs; ii) DFD in delay tolerant networks (DTNs) with sparse and intermittent connectivity; iii) DSR using pairwise comparison. Distributed algorithms have been proposed and analyzed. Theoretical results assess the effectiveness of the proposed solution and give guidelines in the design of the algorithm.

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 Wireless Sensor Networks written by Feng Zhao and published by Elsevier. This book was released on 2004-07-21 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information processing in sensor networks is a rapidly emerging area of computer science and electrical engineering research. Because of advances in micro-sensors, wireless networking and embedded processing, ad hoc networks of sensor are becoming increasingly available for commercial, military, and homeland security applications. Examples include monitoring (e.g., traffic, habitat, security), industrail sensing and diagnostics (e.g., factory, appliances), infrastructures (i.e., power grid, water distribution, waste disposal) and battle awareness (e.g., multi-target tracking). This book introduces practitioners to the fundamental issues and technology constraints concerning various aspects of sensor networks such as information organization, querying, routing, and self-organization using concrete examples and does so by using concrete examples from current research and implementation efforts. - Written for practitioners, researchers, and students and relevant to all application areas, including environmental monitoring, industrial sensing and diagnostics, automotive and transportation, security and surveillance, military and battlefield uses, and large-scale infrastructural maintenance - Skillfully integrates the many disciplines at work in wireless sensor network design: signal processing and estimation, communication theory and protocols, distributed algorithms and databases, probabilistic reasoning, energy-aware computing, design methodologies, evaluation metrics, and more - Demonstrates how querying, data routing, and network self-organization can support high-level information-processing tasks

Download or read book Wireless Sensor Networks written by Ananthram Swami and published by John Wiley & Sons. This book was released on 2007-10-24 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wireless sensor network (WSN) uses a number of autonomous devices to cooperatively monitor physical or environmental conditions via a wireless network. Since its military beginnings as a means of battlefield surveillance, practical use of this technology has extended to a range of civilian applications including environmental monitoring, natural disaster prediction and relief, health monitoring and fire detection. Technological advancements, coupled with lowering costs, suggest that wireless sensor networks will have a significant impact on 21st century life. The design of wireless sensor networks requires consideration for several disciplines such as distributed signal processing, communications and cross-layer design. Wireless Sensor Networks: Signal Processing and Communications focuses on the theoretical aspects of wireless sensor networks and offers readers signal processing and communication perspectives on the design of large-scale networks. It explains state-of-the-art design theories and techniques to readers and places emphasis on the fundamental properties of large-scale sensor networks. Wireless Sensor Networks: Signal Processing and Communications : Approaches WSNs from a new angle – distributed signal processing, communication algorithms and novel cross-layer design paradigms. Applies ideas and illustrations from classical theory to an emerging field of WSN applications. Presents important analytical tools for use in the design of application-specific WSNs. Wireless Sensor Networks will be of use to signal processing and communications researchers and practitioners in applying classical theory to network design. It identifies research directions for senior undergraduate and graduate students and offers a rich bibliography for further reading and investigation.

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 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 Distributed Sensor Networks written by S. Sitharama Iyengar and published by CRC Press. This book was released on 2004-12-29 with total page 1142 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vision of researchers to create smart environments through the deployment of thousands of sensors, each with a short range wireless communications channel and capable of detecting ambient conditions such as temperature, movement, sound, light, or the presence of certain objects is becoming a reality. With the emergence of high-speed networks an

Download or read book Distributed Detection and Estimation in Wireless Sensor Networks written by Edmond Nurellari and published by . This book was released on 2017 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Sensor Networks written by Ivan Stojmenovic and published by John Wiley & Sons. This book was released on 2005-09-19 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: The State Of The Art Of Sensor Networks Written by an international team of recognized experts in sensor networks from prestigious organizations such as Motorola, Fujitsu, the Massachusetts Institute of Technology, Cornell University, and the University of Illinois, Handbook of Sensor Networks: Algorithms and Architectures tackles important challenges and presents the latest trends and innovations in this growing field. Striking a balance between theoretical and practical coverage, this comprehensive reference explores a myriad of possible architectures for future commercial, social, and educational applications, and offers insightful information and analyses of critical issues, including: * Sensor training and security * Embedded operating systems * Signal processing and medium access * Target location, tracking, and sensor localization * Broadcasting, routing, and sensor area coverage * Topology construction and maintenance * Data-centric protocols and data gathering * Time synchronization and calibration * Energy scavenging and power sources With exercises throughout, students, researchers, and professionals in computer science, electrical engineering, and telecommunications will find this an essential read to bring themselves up to date on the key challenges affecting the sensors industry.

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 Data Fusion and Information Processing in Wireless Sensor Networks written by Jinjun Xiao and published by . This book was released on 2006 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Wireless Sensor Networks Issues and Challenges in Current Scenario s written by Pradeep Kumar Singh and published by Springer Nature. This book was released on 2020-02-08 with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores various challenging problems and applications areas of wireless sensor networks (WSNs), and identifies the current issues and future research challenges. Discussing the latest developments and advances, it covers all aspects of in WSNs, from architecture to protocols design, and from algorithm development to synchronization issues. As such the book is an essential reference resource for undergraduate and postgraduate students as well as scholars and academics working in the field.

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 Distributed Estimation Coding and Scheduling in Wireless Visual Sensor Networks written by Chao Yu and published by . This book was released on 2013 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this thesis, we consider estimation, coding, and sensor scheduling for energy efficient operation of wireless visual sensor networks (VSN), which consist of battery-powered wireless sensors with sensing (imaging), computation, and communication capabilities. The competing requirements for applications of these wireless sensor networks (WSN) (e.g., smart environmental surveillance) include long duration of unattended operation and limited energy supply, which motivate our investigation into energy-efficient estimation, coding, and sensor scheduling to prolong the lifetime of these wireless networked systems. Motivated by a telepresence setting in visual sensor networks, we first consider an abstract setting for investigating efficient distributed estimation and coding in wireless sensor networks where the captured data is jointly Gaussian. The sensors are geographically dispersed, and acquire indirect, noisy observations pertaining to a desired signal. A central processor (CP) communicates with these sensors via a rate-constrained channel and estimates the desired signal. In a simplified scenario where information from one sensor is to be sent to the CP that already has information regarding the desired signal, we establish a decomposed structure for the optimal encoding of the local observation: a first pre-processing step to extract relevant information from the indirect observation with consideration of the side information, followed by a second step of side-informed encoding of the pre-processed output. In the general scenario consisting of multiple sensors, we present a sequential framework to recursively utilize the separation. Simulation results demonstrate that constructions obtained using the proposed decomposition offer very good performance, closely matching nonconstructive information theoretic bounds for the problem. We next propose a novel code construction and design method for low-density parity-check accumulate (LDPCA) codes used for rate-adaptive distributed source coding. We propose a code construction using non-uniform splitting, in contrast to the uniform splitting used in prior literature. We also develop methods to analyze the proposed LDPCA codes using density evolution, based on which code search strategies are developed to find good LDPCA codes. Simulation results show the proposed code design outperforms the conventional LDPCA code design, and provides state-of-the-art performance. The final part of the thesis addresses the networking aspect of VSNs, considering sensor scheduling and energy allocation in a telepresence wireless VSN application, where visual coverage over a monitored region is obtained by deploying image sensors (cameras). Each camera provides coverage over a part of the monitored region, and a CP coordinates these cameras in order to gather required visual data. We model the network lifetime as a stochastic random variable that depends upon the coverage geometry for the cameras and the distribution of data requests over the monitored region, two key characteristics that distinguish our problem from other WSN applications. By suitably abstracting this model of network lifetime and utilizing asymptotic analysis, we propose lifetime-maximizing camera scheduling and energy allocation strategies. The effectiveness of the proposed strategies is validated through simulations"--Page viii-ix.

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 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.