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255
D.: Distributed video coding
 Proc. of the IEEE 93 (2005) 71–83
"... Distributed coding is a new paradigm for video compression, ..."
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Cited by 207 (10 self)
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Distributed coding is a new paradigm for video compression,
The impact of spatial correlation on routing with compression in wireless sensor networks
 In ACM/IEEE International Symposium on Information Processing in Sensor Networks (IPSN 2004
"... The efficacy of data aggregation in sensor networks is a function of the degree of spatial correlation in the sensed phenomenon. The recent literature has examined a variety of schemes that achieve greater data aggregation by routing data with regard to the underlying spatial correlation. A well kno ..."
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Cited by 142 (12 self)
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The efficacy of data aggregation in sensor networks is a function of the degree of spatial correlation in the sensed phenomenon. The recent literature has examined a variety of schemes that achieve greater data aggregation by routing data with regard to the underlying spatial correlation. A well known conclusion from these papers is that the nature of optimal routing with compression depends on the correlation level. In this work, we show the existence of a simple, practical and static correlationunaware clustering scheme that satisfies a minmax nearoptimality condition. The implication for system design is that a static correlationunaware scheme can perform as well as sophisticated adaptive schemes for joint routing and compression.
WynerZiv Coding of Motion Video
 in Proc. Asilomar Conference on Signals and Systems
, 2002
"... In current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The WynerZiv Theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are en ..."
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Cited by 104 (14 self)
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In current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The WynerZiv Theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally (given temporally adjacent frames) could achieve similar efficiency. We report first results on a WynerZiv coding scheme for motion video that uses intraframe encoding, but interframe decoding.
On Network Correlated Data Gathering
 IN IEEE INFOCOM
, 2004
"... We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a SlepianWolf ..."
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Cited by 98 (9 self)
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We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a SlepianWolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the SlepianWolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. For the explicit communication case, we prove that building an optimal data gathering tree is NPcomplete and we propose various distributed approximation algorithms.
Distributed compressed sensing
, 2005
"... Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algori ..."
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Cited by 84 (21 self)
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Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study in detail three simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the SlepianWolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In two of our three models, the results are asymptotically bestpossible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.
Energy conservation in wireless sensor networks: A survey
"... In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally batterypowered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifeti ..."
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Cited by 81 (10 self)
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In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally batterypowered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.
Distributed source coding: Symmetric rates and applications to sensor networks.
 PROCEEDINGS OF THE DATA COMPRESSION CONFERENCE (DCC
, 2000
"... We address the problem of distributed source coding using a practical and constructive approach [1], referred to as Distributed source coding using syndromes (DISCUS), with applications to sensor networks. We propose low complexity encoding and decoding methods based on linear codes, to achieve all ..."
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Cited by 70 (3 self)
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We address the problem of distributed source coding using a practical and constructive approach [1], referred to as Distributed source coding using syndromes (DISCUS), with applications to sensor networks. We propose low complexity encoding and decoding methods based on linear codes, to achieve all points in the achievable rate region of SlepianWolf [2] problem. The extension of these concepts to the construction of Euclideanspace codes is also studied and analyzed for the case of trellis and lattice codes. The performance of these symmetric methods for encoding with a fidelity criterion is shown to be the same as that of asymmetric encoding. Simulations are presented to corroborate these results.
The duality between information embedding and source coding with side information and some applications
 in Proc. IEEE Int. Symp. Information Theory
, 2001
"... Abstract—Aspects of the duality between the informationembedding problem and the Wyner–Ziv problem of source coding with side information at the decoder are developed and used to establish a spectrum new results on these and related problems, with implications for a number of important applications ..."
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Cited by 68 (11 self)
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Abstract—Aspects of the duality between the informationembedding problem and the Wyner–Ziv problem of source coding with side information at the decoder are developed and used to establish a spectrum new results on these and related problems, with implications for a number of important applications. The singleletter characterization of the informationembedding problem is developed and related to the corresponding characterization of the Wyner–Ziv problem, both of which correspond to optimization of a common mutual information difference. Dual variables and dual Markov conditions are identified, along with the dual role of noise and distortion in the two problems. For a Gaussian context with quadratic distortion metric, a geometric interpretation of the duality is developed. From such insights, we develop a capacityachieving informationembedding system based on nested lattices. We show the resulting encoder–decoder
The Distributed KarhunenLoève Transform
 IEEE Trans. Inform. Theory
, 2003
"... The KarhunenLoeve transform (KLT) is a key element of many signal processing tasks, including approximation, compression, and classification. Many recent applications involve distributed signal processing where it is not generally possible to apply the KLT to the signal; rather, the KLT must be ..."
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Cited by 64 (12 self)
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The KarhunenLoeve transform (KLT) is a key element of many signal processing tasks, including approximation, compression, and classification. Many recent applications involve distributed signal processing where it is not generally possible to apply the KLT to the signal; rather, the KLT must be approximated in a distributed fashion.
Ubiquitous access to distributed data in largescale sensor networks through decentralized erasure codes
, 2005
"... Consider a largescale wireless sensor network of n nodes, where a fraction k out of n generate data packets of global interest. Assuming that the individual nodes have limited storage and computational capabilities, we address the problem of how to enable ubiquitous access to the distributed data p ..."
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Cited by 55 (6 self)
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Consider a largescale wireless sensor network of n nodes, where a fraction k out of n generate data packets of global interest. Assuming that the individual nodes have limited storage and computational capabilities, we address the problem of how to enable ubiquitous access to the distributed data packets. Specifically, we assume that each node can store at most one data packet, and study the problem of diffusing the data so that by querying any k nodes, it is possible to retrieve all the k data packets of interest (with high probability). We introduce a class of erasure codes and show how to solve this problem efficiently in a completely distributed and robust way. Specifically we show that we can efficiently diffuse the data by “prerouting” only O(ln n) packets per data node to randomly selected storage nodes. By using the proposed scheme, the distributed data becomes available “at the fingertips” of a potential data collector located anywhere in the network.