"In many sensor network applications, the sensor readings are inter-node correlated. In such cases, efficient gathering of sensor readings requires distributed compression. Distributed source coding provides practical solutions for compression of these correlated readings when the appropriate rates for the marginal encoding is known at the sensor nodes. In this thesis, we present a data-gathering technique for sensor networks that exploits correlation between sensor data at different locations in the network. Contrary to distributed source coding, our method does not rely on knowledge of the source correlation model in each node although this knowledge is required at the decoder node. Similar to network coding, our proposed method (which we call Quantized Network Coding) propagates mixtures of packets through the network. The main conceptual difference between our technique and other existing methods is that Quantized Network Coding operates on the field of real numbers and not on a finite field. In this thesis, we study our quantized network coding in both lossless and lossy networks.In the study of lossless networks, we discuss the theoretical foundations for our data gathering technique. By exploiting principles borrowed from compressed sensing, we show that the proposed technique can achieve a good approximation of the sensor readings at the sink node with only a few packets received, and that this approximation gets progressively better as the number of received packets increases. Our first approach is to explain the theoretical foundations for sparse recovery from quantized network coded packets based on an analysis of the Restricted Isometry Property of the corresponding measurement matrices. Extensive simulations comparing the proposed Quantized Network Coding to classic network coding and packet forwarding scenarios demonstrate the delay/distortion advantage of quantized network coding. Furthermore, we discuss the advantages of quantized network coding in a Bayesian scenario where the prior of the sensor readings is available at the decoder node. For such Bayesian scenarios, we also discuss the adaptation of a message passing based decoding algorithm with the aid of simulations.To study the practicality of quantized network coding in lossy networks, we adapt it into the IEEE 802.15.4 standard which characterizes low rate wireless communication for sensor networks. This is done by developing a comprehensive implementation of the PHY and MAC layers of the standard and then adjusting the MAC layer settings to match with our requirements. Our computer simulations using the developed implementation show a significant decrease of the delay in many simulation scenarios. The results obtained using this implementation show more advantages for quantized network coding compared to classic routing based protocols especially for high packet drop rates." --

Reliable and efficient communication of analog observations over a fading multiple access channel (MAC) is important in wireless sensor networks. A sensor network can be well modelled by a set of correlated Gaussian sources communicating to a common receiver over a fading Gaussian MAC (GMAC). It is known that traditional separate source-channel (SSC) coding is sub-optimal when channel state information (CSI) is not available to the transmitters. For this case, neither the optimum performance theoretical achievable (OPTA) nor any practical coding schemes that can outperform traditional coding, remain known. This thesis investigates the minimum mean square error (MMSE) of communicating a pair of Gaussian sources over a bandwidth-matched GMAC with block Rayleigh fading (BF-GMAC) in the absence of transmitter CSI. We derive several upper-bounds to the MMSE as a function of transmitter powers, channel signal-to-noise ratio (CSNR), and the correlation coefficient of the two sources. To derive nontrivial upper bounds which improve on those of SSC coding and uncoded transmission, we incorporate ideas from joint source-channel coding and hybrid digital-analog (HDA) coding to construct coding schemes for which the achievable MMSE can be determined. One main contribution is two new MMSE upper bounds, which appear to be the best known characterizations of the OPTA to date. These bounds (JSC-VQ and HDA-JSC-VQ bounds) are derived by considering a transmission scheme where optimally vector quantized Gaussian sources are directly transmitted in analog form over the BF-GMAC. A comparison of these bounds with the MMSE bound for traditional SSC coding shows a gap that grows with source correlation and CSNR. Although there exists a gap even when the sources are uncorrelated, this gap is relatively small. It is shown that, for highly correlated sources and low average CSNR, uncoded transmission can achieve performance approaching the HDA-JSC-VQ bound. The difficulty of designing a practical coding scheme based on JSC-VQ scheme is the requirement of infinite-dimensional vector quantizers (VQ) for each Gaussian source and the joint detection of long codewords at the receiver. We present a practical coding method constructed by replacing the VQs by trellis coded quantizers (TCQ), which can perform close to the JSC-VQ bound.

This book provides a consolidated view of the various network coding techniques to be implemented at the design of the wireless networks for improving its overall performance. It covers multiple sources communicating with multiple destinations via a common relay followed by network coded modulation schemes for multiple access relay channels. Performance of the distributed systems based on distributed convolutional codes with network coded modulation is covered including a two-way relay channel (TWRC). Two MIF protocols are proposed including derivation of signal-to-noise ratio (SNR) and development of threshold of the channel conditions of both. Features: Systematically investigates coding and modulation for wireless relay networks. Discusses how to apply lattice codes in implementing lossless communications and lossy source coding over a network. Focusses on theoretical approach for performance optimization. Includes various network coding strategies for different networks. Reviews relevant existing and ongoing research in optimization along with practical code design. This book aims at Researchers, Professionals and Graduate students in Networking, Communications, Information, Coding Theory, Theoretical Computer Science, Performance Analysis and Resource Optimization, Applied Discrete Mathematics, and Applied Probability.

Distributed source coding is one of the key enablers for efficient cooperative communication. The potential applications range from wireless sensor networks, ad-hoc networks, and surveillance networks, to robust low-complexity video coding, stereo/Multiview video coding, HDTV, hyper-spectral and multispectral imaging, and biometrics. The book is divided into three sections: theory, algorithms, and applications. Part one covers the background of information theory with an emphasis on DSC; part two discusses designs of algorithmic solutions for DSC problems, covering the three most important DSC problems: Slepian-Wolf, Wyner-Ziv, and MT source coding; and part three is dedicated to a variety of potential DSC applications. Key features: Clear explanation of distributed source coding theory and algorithms including both lossless and lossy designs. Rich applications of distributed source coding, which covers multimedia communication and data security applications. Self-contained content for beginners from basic information theory to practical code implementation. The book provides fundamental knowledge for engineers and computer scientists to access the topic of distributed source coding. It is also suitable for senior undergraduate and first year graduate students in electrical engineering; computer engineering; signal processing; image/video processing; and information theory and communications.

Network coding, a relatively new area of research, has evolved from the theoretical level to become a tool used to optimize the performance of communication networks – wired, cellular, ad hoc, etc. The idea consists of mixing “packets” of data together when routing them from source to destination. Since network coding increases the network performance, it becomes a tool to enhance the existing protocols and algorithms in a network or for applications such as peer-to-peer and TCP. This book delivers an understanding of network coding and provides a set of studies showing the improvements in security, capacity and performance of fixed and mobile networks. This is increasingly topical as industry is increasingly becoming more reliant upon and applying network coding in multiple applications. Many cases where network coding is used in routing, physical layer, security, flooding, error correction, optimization and relaying are given – all of which are key areas of interest. Network Coding is the ideal resource for university students studying coding, and researchers and practitioners in sectors of all industries where digital communication and its application needs to be correctly understood and implemented. Contents 1. Network Coding: From Theory to Practice, Youghourta Benfattoum, Steven Martin and Khaldoun Al Agha. 2. Fountain Codes and Network Coding for WSNs, Anya Apavatjrut, Claire Goursaud, Katia Jaffrès-Runser and Jean-Marie Gorce. 3. Switched Code for Ad Hoc Networks: Optimizing the Diffusion by Using Network Coding, Nour Kadi and Khaldoun Al Agha. 4. Security by Network Coding, Katia Jaffrès-Runser and Cédric Lauradoux. 5. Security for Network Coding, Marine Minier, Yuanyuan Zhang and Wassim Znaïdi. 6. Random Network Coding and Matroids, Maximilien Gadouleau. 7. Joint Network-Channel Coding for the Semi-Orthogonal MARC: Theoretical Bounds and Practical Design, Atoosa Hatefi, Antoine O. Berthet and Raphael Visoz. 8. Robust Network Coding, Lana Iwaza, Marco Di Renzo and Michel Kieffer. 9. Flow Models and Optimization for Network Coding, Eric Gourdin and Jeremiah Edwards.

This monograph reviews several recent compressed sensing advancements in wireless networks with an aim to improve the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources.