Compressed Sensing Based Video Streaming In Wireless Multimedia Sensor Networks
Download Compressed Sensing Based Video Streaming In Wireless Multimedia Sensor Networks full books in PDF, epub, and Kindle. Read online free Compressed Sensing Based Video Streaming In Wireless Multimedia Sensor Networks ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Scott Pudlewski |
| Publisher | : |
| Total Pages | : 141 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
Compressed sensing (CS) has emerged as a promising technique to jointly sense and compress sparse signals. One of the most promising applications of CS is compressive imaging. Leveraging the fact that images can be represented as approximately sparse signals in a transformed domain, CS allows images to be compressed and sampled simultaneously using low-complexity linear operations. Recently, these techniques have been extended beyond images to encode video. Much of the compression in traditional video encoding comes from using motion vectors to take advantage of the temporal correlation between adjacent frames. However, since calculating motion vectors is a significant source of power consumption, any technique appropriate for resource constrained video sensors must exploit temporal correlation through low complexity operations.^In this dissertation, we develop a system that will allow high quality video streaming over wireless sensor networks (WSNs) to develop a wireless multimedia sensor networking (WMSN) device that is able to transmit video using battery operated, low power, low complexity, low cost devices. We will introduce the basics of video streaming over a WMSN, and examines what the challenges of this type of system are. First, we attempt to convince the reader why traditional WMSN systems are not practical for real applications, and why a new system must be developed. We then introduce a system architecture based on the properties of compressed sensing, that solves many of these problems by jointly designing a video encoder, a transport layer rate controller and a video streaming system. The goal of this system is to deliver high quality video while keeping within the constraints of a traditional scalar WSN.^In the rest of this dissertation, we will examine three components of the CS based video streaming system. First, we will develop a compressive video sensing (CVS) video encoder that is able to encode video at very low complexity. We then develop a rate controller for both traditional video, along with video encoded using CVS, and show that, by taking video properties directly into account, we can develop a rate controller that optimizes the received video quality while maintaining fairness in terms of video quality. We then look at a system that corrects errors using a sparse error correction technique based on compressed sensing. We finally look at using relays to reduce the overhead required to correct errors.
| Author | : Shafiullah Khan |
| Publisher | : CRC Press |
| Total Pages | : 549 |
| Release | : 2016-04-21 |
| Genre | : Computers |
| ISBN | : 1466588853 |
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
| Author | : Siyuan Xiang |
| Publisher | : |
| Total Pages | : |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
With the increasing demand of video applications in wireless networks, how to better support video transmission over wireless networks has drawn much attention to the research community. Time-varying and error-prone nature of wireless channel makes video transmission in wireless networks a challenging task to provide the users with satisfactory watching experience. For different video applications, we choose different video coding techniques accordingly. E.g., for Internet video streaming, we choose standardized H.264 video codec; for video transmission in sensor networks or multicast, we choose simple and energy-conserving video coding technique based on compressive sensing. Thus, the challenges for different video transmission applications are different. Therefore, This dissertation tackles video transmission problem in three different applications. First, for dynamic adaptive streaming over HTTP (DASH), we investigate the streaming strategy. Specifically, we focus on the rate adaptation algorithm for streaming scalable video (H.264/SVC) in wireless networks. We model the rate adaptation problem as a Markov Decision Process (MDP), aiming to find an optimal streaming strategy in terms of user-perceived quality of experience (QoE) such as playback interruption, average playback quality and playback smoothness. We then obtain the optimal MDP solution using dynamic programming. However, the optimal solution requires the knowledge of the available bandwidth statistics and has a large number of states, which makes it difficult to obtain the optimal solution in real time. Therefore, we further propose an online algorithm which integrates the learning and planning process. The proposed online algorithm collects bandwidth statistics and makes streaming decisions in real time. A reward parameter has been defined in our proposed streaming strategy, which can be adjusted to make a good trade-off between the average playback quality and playback smoothness. We also use a simple testbed to validate our proposed algorithm. Second, for video transmission in wireless sensor networks, we consider a wireless sensor node monitoring the environment and it is equipped with a compressive-sensing based, single-pixel image camera and other sensors such as temperature and humidity sensors. The wireless node needs to send the data out in a timely and energy efficient way. This transmission control problem is challenging in that we need to jointly consider perceived video quality, quality variation, power consumption and transmission delay requirements, and the wireless channel uncertainty. We address the above issues by first building a rate-distortion model for compressive sensing video. Then we formulate the deterministic and stochastic optimization problems and design the transmission control algorithm which jointly performs rate control, scheduling and power control. Third, we propose a low-complex, scalable video coding architecture based on compressive sensing (SVCCS) for wireless unicast and multicast transmissions ...
| Author | : Habib M. Ammari |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 692 |
| Release | : 2013-12-17 |
| Genre | : Technology & Engineering |
| ISBN | : 3642400663 |
During the last one and a half decades, wireless sensor networks have witnessed significant growth and tremendous development in both academia and industry. A large number of researchers, including computer scientists and engineers, have been interested in solving challenging problems that span all the layers of the protocol stack of sensor networking systems. Several venues, such as journals, conferences, and workshops, have been launched to cover innovative research and practice in this promising and rapidly advancing field. Because of these trends, I thought it would be beneficial to provide our sensor networks community with a comprehensive reference on as much of the findings as possible on a variety of topics in wireless sensor networks. As this area of research is in continuous progress, it does not seem to be a reasonable solution to keep delaying the publication of such reference any more. This book relates to the second volume and focuses on the advanced topics and applications of wireless sensor networks. Our rationale is that the second volume has all application-specific and non-conventional sensor networks, emerging techniques and advanced topics that are not as matured as what is covered in the first volume. Thus, the second volume deals with three-dimensional, underground, underwater, body-mounted, and societal networks. Following Donald E. Knuth’s above-quoted elegant strategy to focus on several important fields (The Art of Computer Programming: Fundamental Algorithms, 1997), all the book chapters in this volume include up-to-date research work spanning various topics, such as stochastic modeling, barrier and spatiotemporal coverage, tracking, estimation, counting, coverage and localization in three-dimensional sensor networks, topology control and routing in three-dimensional sensor networks, underground and underwater sensor networks, multimedia and body sensor networks, and social sensing. Most of these major topics can be covered in an advanced course on wireless sensor networks. This book will be an excellent source of information for graduate students majoring in computer science, computer engineering, electrical engineering, or any related discipline. Furthermore, computer scientists, researchers, and practitioners in both academia and industry will find this book useful and interesting.
| Author | : Subhendu Kumar Pani |
| Publisher | : CRC Press |
| Total Pages | : 346 |
| Release | : 2022-09-01 |
| Genre | : Technology & Engineering |
| ISBN | : 1000793559 |
Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions. This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.
| Author | : Adrian Popescu |
| Publisher | : Springer |
| Total Pages | : 235 |
| Release | : 2018-01-29 |
| Genre | : Computers |
| ISBN | : 3319717189 |
This insightful text presents a guide to video distribution networks (VDNs), providing illuminating perspectives on reducing power consumption in IP-based video networks from an authoritative selection of experts in the field. A particular focus is provided on aspects of architectures, models, Internet protocol television (IPTV), over-the-top (OTT) video content, video on demand (VoD) encoding and decoding, mobile terminals, wireless multimedia sensor networks (WMSNs), software defined networking (SDN), and techno-economic issues. Topics and features: reviews the fundamentals of video over IP distribution systems, and the trade-offs between network/service performance and energy efficiency in VDNs; describes the characterization of the main elements in a video distribution chain, and techniques to decrease energy consumption in software-based VoD encoding; introduces an approach to reduce power consumption in mobile terminals during video playback, and in data center networks using the SDN paradigm; discusses the strengths and limitations of different methods for measuring the energy consumption of mobile devices; proposes optimization methods to improve the energy efficiency of WMSNs, and a routing algorithm that reduces energy consumption while maintaining the bandwidth; presents an economic analysis of the savings yielded by approaches to minimize energy consumption of IPTV and OTT video content services. The broad coverage and practical insights offered in this timely volume will be of great value to all researchers, practitioners and students involved with computer and telecommunication systems.
| Author | : K.R. Rao |
| Publisher | : CRC Press |
| Total Pages | : 496 |
| Release | : 2017-07-12 |
| Genre | : Computers |
| ISBN | : 1351831941 |
Rapid progress in software, hardware, mobile networks, and the potential of interactive media poses many questions for researchers, manufacturers, and operators of wireless multimedia communication systems. Wireless Multimedia Communication Systems: Design, Analysis, and Implementation strives to answer those questions by not only covering the underlying concepts involved in the design, analysis, and implementation of wireless multimedia communication systems, but also by tackling advanced topics such as mobility management, security components, and smart grids. Offering an accessible treatment of the latest research, this book: Presents specific wireless multimedia communication schemes that have proven to be useful Discusses important standardization processing activities regarding wireless networking Includes wireless mesh and multimedia sensor network architectures, protocols, and design optimizations Highlights the challenges associated with meeting complex connectivity requirements Contains numerous figures, tables, examples, references, and a glossary of acronyms Providing coverage of significant technological advances in their initial steps along with a survey of the fundamental principles and practices, Wireless Multimedia Communication Systems: Design, Analysis, and Implementation aids senior-level and graduate-level engineering students and practicing professionals in understanding the processes and furthering the development of today’s wireless multimedia communication systems.
| Author | : Feng Wu |
| Publisher | : CRC Press |
| Total Pages | : 517 |
| Release | : 2014-07-25 |
| Genre | : Technology & Engineering |
| ISBN | : 1482234130 |
Visual information is one of the richest and most bandwidth-consuming modes of communication. To meet the requirements of emerging applications, powerful data compression and transmission techniques are required to achieve highly efficient communication, even in the presence of growing communication channels that offer increased bandwidth. Presenting the results of the author’s years of research on visual data compression and transmission, Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications provides a theoretical and technical basis for advanced research on visual data compression and communication. The book studies the drifting problem in scalable video coding, analyzes the reasons causing the problem, and proposes various solutions to the problem. It explores the author’s Barbell-based lifting coding scheme that has been adopted as common software by MPEG. It also proposes a unified framework for deriving a directional transform from the nondirectional counterpart. The structure of the framework and the statistic distribution of coefficients are similar to those of the nondirectional transforms, which facilitates subsequent entropy coding. Exploring the visual correlation that exists in media, the text extends the current coding framework from different aspects, including advanced image synthesis—from description and reconstruction to organizing correlated images as a pseudo sequence. It explains how to apply compressive sensing to solve the data compression problem during transmission and covers novel research on compressive sensor data gathering, random projection codes, and compressive modulation. For analog and digital transmission technologies, the book develops the pseudo-analog transmission for media and explores cutting-edge research on distributed pseudo-analog transmission, denoising in pseudo-analog transmission, and supporting MIMO. It concludes by considering emerging developments of information theory for future applications.
| Author | : Feng Zhao |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 688 |
| Release | : 2003-04-10 |
| Genre | : Computers |
| ISBN | : 3540021116 |
This book constitutes the refereed proceedings of the Second International Workshop on Information Processing in Sensor Networks, IPSN 2003, held in Palo Alto, CA, USA, in April 2003. The 23 revised full papers and 21 revised poster papers presented were carefully reviewed and selected from 73 submissions. Among the topics addressed are wireless sensor networks, query processing, decentralized sensor platforms, distributed databases, distributed group management, sensor network design, collaborative signal processing, adhoc sensor networks, distributed algorithms, distributed sensor network control, sensor network resource management, data service middleware, random sensor networks, mobile agents, target tracking, sensor network protocols, large scale sensor networks, and multicast.
| Author | : Richa Singh |
| Publisher | : Springer |
| Total Pages | : 169 |
| Release | : 2015-10-01 |
| Genre | : Technology & Engineering |
| ISBN | : 8132226259 |
This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence and Signal Processing was one of the few unique events that are focused on the convergence of the two fields. The book is comprised of chapters based on the top presentations at the workshop. This book has three chapters on various topics of biometrics – two are on face detection and one on iris recognition; all from top researchers in their field. There are four chapters on different biomedical signal / image processing problems. Two of these are on retinal vessel classification and extraction; one on biomedical signal acquisition and the fourth one on region detection. There are three chapters on data analysis – a topic gaining immense popularity in industry and academia. One of these shows a novel use of compressed sensing in missing sales data interpolation. Another chapter is on spam detection and the third one is on simple one-shot movie rating prediction. Four other chapters cover various cutting edge miscellaneous topics on character recognition, software effort prediction, speech recognition and non-linear sparse recovery. The contents of this book will prove useful to researchers, professionals and students in the domains of machine learning and signal processing.
